Calorimetry in Food Processing Analysis and Design of Food Systems Institute of Food Technologists Series

Calorimetry in Food Processing: Analysis and Design of Food Systems

The IFT Press series reflects the mission of the Institute of Food Technologists – to advance the science of food contributing to healthier people everywhere. Developed in partnership with Wiley-Blackwell, IFT Press books serve as leading-edge handbooks for industrial application and reference and as essential texts for academic programs. Crafted through rigorous peer review and meticulous research, IFT Press publications represent the latest, most significant resources available to food

scientists and related agriculture professionals worldwide. Founded in 1939, the Institute of Food Technologists is a nonprofit scientific society with 22,000 individual members working in food science, food technology, and related professions in industry, academia, and government. IFT serves as a conduit for multidisciplinary science thought leadership, championing the use of sound science across the food value chain through knowledge sharing, education, and advocacy.

IFT Book Communications Committee Barry G. Swanson Syed S. H. Rizvi Joseph H. Hotchkiss Christopher J. Doona William C. Haines Ruth M. Patrick Mark Barrett John Lillard Karen Nachay

IFT Press Editorial Advisory Board Malcolm C. Bourne Fergus M. Clydesdale Dietrich Knorr Theodore P. Labuza Thomas J. Montville S. Suzanne Nielsen Martin R. Okos Michael W. Pariza Barbara J. Petersen David S. Reid Sam Saguy Herbert Stone Kenneth R. Swartzel

A John Wiley & Sons, Inc., Publication

Calorimetry in Food Processing: Analysis and Design of Food Systems

EDITOR

Gönül Kaletunç

A John Wiley & Sons, Inc., Publication

Edition first published 2009 © 2009 Wiley-Blackwell and the Institute of Food Technologists Chapter 7 remains with the U.S. Government. Blackwell Publishing was acquired by John Wiley & Sons in February 2007. Blackwell’s publishing program has been merged with Wiley’s global Scientific, Technical, and Medical business to form Wiley-Blackwell. Editorial Office 2121 State Avenue, Ames, Iowa 50014-8300, USA For details of our global editorial offices, for customer services, and for information about how to apply for permission to reuse the copyright material in this book, please see our website at www.wiley.com/wiley-blackwell. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by Blackwell Publishing, provided that the base fee is paid directly to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For those organizations that have been granted a photocopy license by CCC, a separate system of payments has been arranged. The fee codes for users of the Transactional Reporting Service are ISBN-13: 978-0-8138-1483-4/2009. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Library of Congress Cataloging-in-Publication Data Calorimetry in food processing : analysis and design of food systems/editor Gönül Kaletunç. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-8138-1483-4 (alk. paper) ISBN-10: 0-8138-1483-9 (alk. paper) 1. Food–Analysis. 2. Thermal analysis. 3. Calorimetry–Industrial applications. 4. Food industry and trade. I. Kaletunç, Gönül TX544.C35 2009 338.4'7664–dc22 2009008348 A catalog record for this book is available from the U.S. Library of Congress. Set in 11.5 on 13.5 pt Times by SNP Best-set Typesetter Ltd., Hong Kong Printed in Singapore 1 2009

Titles in the IFT Press series • Accelerating New Food Product Design and Development (Jacqueline H. Beckley, Elizabeth J. Topp, M. Michele Foley, J.C. Huang, and Witoon Prinyawiwatkul) • Advances in Dairy Ingredients (Geoffrey W. Smithers and Mary Ann Augustin) • Biofilms in the Food Environment (Hans P. Blaschek, Hua H. Wang, and Meredith E. Agle) • Calorimetry and Food Process Design (Gönül Kaletunç) • Nondigestible Carbohydrates and Digestive Health (Teresa M. Paeschke and William R. Aimutis) • Food Ingredients for the Global Market (Yao-Wen Huang and Claire L. Kruger) • Food Irradiation Research and Technology (Christopher H. Sommers and Xuetong Fan) • Food Laws, Regulations and Labeling (Joseph D. Eifert) • Food Risk and Crisis Communication (Anthony O. Flood and Christine M. Bruhn) • Foodborne Pathogens in the Food Processing Environment: Sources, Detection and Control (Sadhana Ravishankar and Vijay K. Juneja) • Functional Proteins and Peptides (Yoshinori Mine, Richard K. Owusu-Apenten, and Bo Jiang) • High Pressure Processing of Foods (Christopher J. Doona and Florence E. Feeherry) • Hydrocolloids in Food Processing (Thomas R. Laaman) • Microbial Safety of Fresh Produce (Xuetong Fan, Brendan A. Niemira, Christopher J. Doona, Florence E. Feeherry, and Robert B. Gravani) • Microbiology and Technology of Fermented Foods (Robert W. Hutkins) • Multiphysics Simulation of Emerging Food Processing Technologies (Kai Knoerzer, Pablo Juliano, Peter Roupas, and Cornelis Versteeg) • Multivariate and Probabilistic Analyses of Sensory Science Problems (Jean-François Meullenet, Rui Xiong, and Christopher J. Findlay) • Nondestructive Testing of Food Quality (Joseph Irudayaraj and Christoph Reh) • Nanoscience and Nanotechnology in Food Systems (Hongda Chen) • Nonthermal Processing Technologies for Food (Howard Q. Zhang, Gustavo V. BarbosaCànovas, and V.M. Balasubramaniam, Editors; C. Patrick Dunne, Daniel F. Farkas, and James T.C. Yuan, Associate Editors) • Nutraceuticals, Glycemic Health and Type 2 Diabetes (Vijai K. Pasupuleti and James W. Anderson) • Packaging for Nonthermal Processing of Food (J. H. Han) • Preharvest and Postharvest Food Safety: Contemporary Issues and Future Directions (Ross C. Beier, Suresh D. Pillai, and Timothy D. Phillips, Editors; Richard L. Ziprin, Associate Editor) • Processing and Nutrition of Fats and Oils (Ernesto M. Hernandez and Afaf Kamal-Eldin) • Processing Organic Foods for the Global Market (Gwendolyn V. Wyard, Anne Plotto, Jessica Walden, and Kathryn Schuett) • Regulation of Functional Foods and Nutraceuticals: A Global Perspective (Clare M. Hasler) • Sensory and Consumer Research in Food Product Design and Development (Howard R. Moskowitz, Jacqueline H. Beckley, and Anna V.A. Resurreccion) • Sustainability in the Food Industry (Cheryl J. Baldwin) • Water Activity in Foods: Fundamentals and Applications (Gustavo V. Barbosa-Cànovas, Anthony J. Fontana Jr., Shelly J. Schmidt and Theodore P. Labuza) • Whey Processing, Functionality and Health Benefits (Charles I. Onwulata and Peter J. Huth)

Dedication

For my parents, my son, and my husband for their patience and encouragement. Hayatta en hakiki mür¸sit ilimdir. “The truest guide in life is science.” —Mustafa Kemal Atatürk, September 22, 1924

vii

Dedication

This book is also dedicated to the memory of the late Professor Michel Ollivon, a great scientist and an exceptional human being, who passed away on June 16th, 2007, during the preparation of the book.

viii

Table of Contents

Preface Contributor List

xiii xvii

Part 1 Analysis of Food and Biological Materials by Calorimetry Chapter 1

Chapter 2

Chapter 3

Chapter 4

3

Calorimetric Methods as Applied to Food: An Overview Gönül Kaletunç

5

Methods and Applications of Microcalorimetry in Food Pierre Le Parlouër and Luc Benoist

15

High-Pressure Differential Scanning Calorimetry Günther W.H. Höhne and Gönül Kaletunç

51

Calorimetry of Proteins in Dilute Solution G. Eric Plum

Chapter 5 Thermal Analysis of Denaturation and Aggregation of Proteins and Protein Interactions in a Real Food System Valerij Y. Grinberg, Tatiana V. Burova, and Vladimir B. Tolstoguzov ix

67

87

x

Table of Contents

Chapter 6

Chapter 7

Chapter 8

Part 2

Heat-Induced Phase Transformations of Protein Solutions and Fat Droplets in Oil-in-Water Emulsions: A Thermodynamic and Kinetic Study Perla Relkin Analysis of Foodborne Bacteria by Differential Scanning Calorimetry Michael H. Tunick, John S. Novak, Darrell O. Bayles, Jaesung Lee, and Gönül Kaletunç Coupling of Differential Scanning Calorimetry and X-Ray Diffraction to Study the Crystallization Properties and Polymorphism of Triacyglycerols Christelle Lopez, Daniel J.E. Kalnin, and Michel R. Ollivon

Calorimetry as a Tool for Process Design

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Overview of Calorimetry as a Tool for Efficient and Safe Food-Processing Design Alois Raemy, Corinne Appolonia Nouzille, Pierre Lambelet, and Alejandro Marabi Shelf Life Prediction of Complex Food Systems by Quantitative Interpretation of Isothermal Calorimetric Data Simon Gaisford, Michael A.A. O’Neill, and Anthony E. Beezer Use of Thermal Analysis to Design and Monitor Cereal Processing Alberto Schiraldi, Dimitrios Fessas, and Marco Signorelli Importance of Calorimetry in Understanding Food Dehydration and Stability Yrjö H. Roos

119

147

169

199

201

237

265

289

Table of Contents

xi

Chapter 13

High-Pressure Calorimetry and Transitiometry Stanislaw L. Randzio and Alain Le Bail

Chapter 14

Calorimetric Analysis of Starch Gelatinization by High-Pressure Processing 341 Kelley Lowe and Gönül Kaletunç

Chapter 15

Use of Calorimetry to Evaluate Safety of Processing Hans Fierz

Index

311

351

369

Preface

The global food industry is very large, producing sales worldwide on the order of approximately U.S. $1 trillion. To remain competitive in this complex industry, it is vital that manufacturers optimize foodprocessing conditions, most importantly not only to ensure the safety of food products but also to produce affordable, healthy, and convenient products, with desired sensory attributes. The global scale of the food industry brings the new challenges of increasing transport and export and in turn new requirements for increased shelf life. Optimization of food-processing conditions as well as development of new products requires knowledge of the physical properties of the food products and their components as the variables that are relevant to processing and storage conditions. Detailed knowledge of physical properties enables manufacturers to prevent waste of time and resources caused by trial and error during product formulation and process design. Many food-processing protocols involve application of heating or cooling over a broad range of temperature. Knowledge of a food’s thermal properties as a function of temperature and composition is necessary for heat transfer and energy balance calculations used to rationally design these thermal-processing protocols. During processing, the food components go through conformational and phase changes that affect the state and texture of the final food product. Temperature-scanning calorimetry provides a useful tool for detecting, monitoring, and characterizing thermal processes in food materials. Moreover, calorimetry can be used to evaluate the effects of various physical and chemical stresses, including nonthermal treatments, on specific components by comparing the thermal profiles of pre- and post-treated food and biological materials to develop an xiii

xiv

Preface

understanding of the mechanism of processing-induced changes. The data generated from thermal analysis techniques also can be used to develop equations that predict the physical properties of pre- and postprocessed foods as a function of processing and storage conditions. Although the use of calorimetry to measure the physical properties of food materials has increased both in academia and in industry over the past 20 years, the analysis of data frequently is complicated by multiple overlapping transitions and kinetically controlled events that occur in food materials. This book is designed to introduce the basic principles of calorimetry, applications of calorimetry to characterize food products, interpretation of the resultant data, and the use of these data for process optimization and product development. The book is organized in two sections. The first section, consisting of eight chapters, focuses on the basic principles of calorimetry and its use for a wide range of materials from dilute solutions to solids. The second section, consisting of seven chapters, emphasizes the use of calorimetric data as a tool for process design and product development. Chapter 1 provides an overview of calorimetry and the organization of the book. Chapters 2 and 3 focus on experimental design principles, calibration, data collection and analysis for microcalorimetry and highpressure calorimetry. Chapter 4 addresses applications of ultrasensitive calorimetry to proteins and their interactions in dilute solution to characterize the thermal and thermodynamic stability and the thermodynamic origins of that stability. Chapters 5, 6, and 7 undertake the characterization of concentrated, multicomponent systems that are commonly observed in foods and complex biological systems such as bacteria. The final chapter in this section, Chapter 8, focuses on the use of an instrument that combines X-ray diffraction and highsensitivity differential scanning calorimetry (DSC) in the same apparatus to simultaneously obtain complementary thermal and structural information for a sample. Section Two of the book comprises Chapters 9 through 15. Chapter 9 provides an overview of the use of phase transition information in development of phase diagrams that can be used for efficient process design. Chapter 10 covers application of isothermal calorimetry for analysis of food stability, shelf life, and isothermal cooking processes. Chapter 11 describes application of thermal analysis to cereal-based products and mathematical treatment of the complex thermograms to

Preface

xv

deconvolute the contributions from different components of the system. Chapter 12 reviews the use of calorimetric data for selection of dehydration parameters to produce products with improved storage stability. Chapter 13 describes the relatively new technique of scanning transitiometry and its specific application to gelatinization of wheat starch dispersions and for investigation of pressure shift freezing. Chapter 14 covers the application of calorimetry to characterize the impact of nonthermal treatment and to determine kinetic parameters during storage. Chapter 15 reviews the use of calorimetry to quantify the probability and potential severity of exothermic events such as formation of hot spots in dryers and to establish safe conditions for handling materials to prevent accidents in the food industry. This book is designed to explain the capabilities of calorimetry for characterization of food and biological systems, which can range from single component, single-phase systems to multicomponent, multiphase systems. Therefore, information described in the book will provide comprehensive insight for scientists who have experience with calorimetry as well as a basic understanding for beginners. This text may also be used as a textbook for a graduate-level course. The book is also intended to serve as a resource for food scientists, food technologists, and food engineers working in the area of process design, optimization, and product development. The descriptions of the basic principles and potential uses of calorimetry to provide critical information for their respective areas and will serve as a bridge between these workers and specialists in calorimetry.

Contributors

Bayles, Darrell O. (Chapter 7) Dairy Processing & Products Research Unit, USDA-ARS-Eastern Regional Research Center, Wyndmoor, PA, USA Beezer, Anthony E. (Chapter 10) The School of Pharmacy, University of London, London, UK Benoist, Luc (Chapter 2) SETARAM, Lyon, France Burova, Tatiana V. (Chapter 5) Nesmeyanov Institute of Organo-Element Compounds, Russian Academy of Sciences, Moscow, Russian Federation Fessas, Dimitrios (Chapter 11) DISTAM, University of Milan, Milano, Italy Fierz, Hans (Chapter 15) Swiss Safety Institute, Basel, Switzerland Gaisford, Simon (Chapter 10) The School of Pharmacy, University of London, London, UK Grinberg, Valerij Y. (Chapter 5) A.N. Nesmeyanov Institute of Organo-Element Compounds, Russian Academy of Sciences, Moscow, Russian Federation xvii

xviii

Contributors

Höhne, Günther W.H. (Chapter 3) University of Ulm, Ulm, Germany (Retired) Kaletunç, Gönül (Chapters 1, 3, 7, 14) The Ohio State University, Department of Food Agricultural and Biological Engineering, Columbus, OH, USA Kalnin, Daniel J.E. (Chapter 8) YKI, Ytkemiska Institutet AB, The Institute for Surface Chemistry, Stockholm, Sweden Lambelet, Pierre (Chapter 9) Nestlé Research Center, Nestec LTD, Lausanne, Switzerland Le Bail, Alain (Chapter 13) ENITIAA, UMR CNRS GEPEA (6144), Nantes, France Lee, Jaesung (Chapter 7) Department of Food Science and Technology, The Ohio State University, Columbus, OH, USA Le Parlouër, Pierre (Chapter 2) Thermal Consulting, Caluire, France Lopez, Christelle (Chapter 8) UMR Science et Technologie du Lait et de l’Oeuf, INRA-Agrocampus Ouest, Rennes Cedex, France Lowe, Kelley (Chapter 14) Abbott Nutrition Products Division, Columbus, OH, USA Marabi, Alejandro (Chapter 9) Nestlé Research Center, Nestec Ltd., Lausanne, Switzerland Nouzille, Corinne Appolonia (Chapter 9) Nestlé Research Center, Nestec Ltd., Lausanne, Switzerland Novak, John S. (Chapter 7) Dairy Processing & Products Research Unit, USDA-ARS-Eastern Regional Research Center, Wyndmoor, PA, USA

Contributors

xix

Michel Ollivon (Chapter 8, published posthumously) Université Paris-Sud, Chatenay-Malabry, France O’Neill, Michael A.A. (Chapter 10) Department of Pharmacy and Pharmacology, University of Bath, Bath, UK Plum, G. Eric (Chapter 4) IBET Inc., Columbus, OH, USA, and Rutgers, The State University of New Jersey, Department of Chemistry and Chemical Biology, Piscataway, NJ, USA Raemy, Alois (Chapter 9) Nestlé Research Center, Nestec Ltd., Lausanne, Switzerland Randzio, Stanislaw L. (Chapter 13) Polish Academy of Sciences, Institute of Physical Chemistry, Warszawa, Poland Relkin, Perla (Chapter 6) UMR 1145 (AgroParisTech, CEMAGREF, INRA), AgroParisTech, Department of Science and Engineering for Food and Bioproducts, Massy, France Roos, Yrjö H. (Chapter 12) Department of Food and Nutritional Sciences, University College Cork, Ireland Schiraldi, Alberto (Chapter 11) DISTAM, University of Milan, Milano, Italy Signorelli, Marco (Chapter 11) DISTAM, University of Milan, Milano, Italy Tolstoguzov, V.B. (Chapter 5) Tolstoguzov consulting.com, Pully, Switzerland. Tunick, Michael H. (Chapter 7) Dairy Processing & Products Research Unit, USDA-ARS-Eastern Regional Research Center, Wyndmoor, PA, USA

Calorimetry in Food Processing: Analysis and Design of Food Systems

Part 1 Analysis of Food and Biological Materials by Calorimetry

Chapter 1 Calorimetric Methods as Applied to Food: An Overview Gönül Kaletunç

Introduction Calorimetry An Overview of the Book References

5 6 8 13

Introduction Several thermal and nonthermal methods are applied to process and preserve food materials and to manufacture value-added products. The goals of food processing are to inactivate spoilage and pathogenic microorganisms and to maintain this status in storage during the intended shelf life of the product. During processing, changes take place in food components, including vitamins, lipids, carbohydrates, and proteins. Such changes lead to structural and functional changes in foods at the micro- and macromolecular levels that affect the physical, organoleptic, and nutritional properties of the food. Food materials are complex biological systems. Food products may have a broad range of structures spanning the three states of matter, including dilute to concentrated liquids, solids, and mixtures of multiliquid, liquid-solid, liquid-gas, and solid-gas structures. The combination of complex structures making up complex biological compounds makes the characterization of food systems challenging. To address the wide variety of compositions and structures, many biophysical techniques are uesd to characterize the structure and properties of food materials before and after processing to develop a fundamental 5

6

Calorimetry in Food Processing

understanding of the impact of processing and storage conditions. The data resulting from such studies can be used to predict the physical properties of foods so that food processing and storage conditions are optimized.

Calorimetry Among biophysical techniques, calorimetry presents itself as particularly well suited for analysis of food materials. Among many reasons, the first is the relevance of the experimental protocols of calorimetry to the majority of processes employed in food preservation. Specifically, because many food-processing methods involve thermal treatment (heating, cooling, freezing) of the materials, thermal characterization of food systems and their components leads to data that can be related directly to the processing protocols. Determination of thermal properties of food materials, such as specific heat as a function of temperature, is essential for heat transfer and energy balance calculations (Kaletunç 2007). Generation of a reliable database to develop equations predicting thermal properties of food materials for optimization of food processes can be accomplished by using calorimetry. Moreover, food materials and their components go through conformational and phase transitions during processing. Calorimetry data can be analyzed to evaluate the thermal and thermodynamic stability of various phases for a rational design of food product formulations and process conditions. Differential scanning calorimetry, which measures heat capacity as a function of temperature, is a well-established thermal analysis technique that detects and monitors thermally induced conformational transitions and phase transitions as a function of temperature. During temperature scanning, depending on the complexity of the material, many peaks or inflection points (one to several) reflecting the thermally induced transitions can be observed. The direction of the peak corresponds to the nature of the transition, being heat absorbing (endotherms) or heat releasing (exotherms). While melting of solids and denaturation of proteins display endotherms, crystallization of carbohydrates and aggregation of proteins manifest themselves as exotherms. The temperatures for the endothermic and exothermic transitions and

Calorimetric Methods as Applied to Food: An Overview

7

the heat involved in such transitions are measured using a calorimeter. Inflection points are indicative of glass transitions; that is, transitions from a glassy to rubbery state. The transition temperatures (Tpeak or Tg) reflect the thermal stability of the phase or state going through the transition. One can extract from calorimetry data values for the thermal and thermodynamic changes in free energy (ΔG), enthalpy (ΔH), entropy (ΔS), and heat capacity (ΔCp) of the various transitions in addition to determination of the bulk heat capacity of the material. The basis for thermodynamic study of food materials is that the relevant initial and final states (preprocessing and postprocessing states) can be defined and the energetic and structural differences between these states can be measured using calorimetric instrumentation. To this end, calorimetry can be used to evaluate the effect of other physical and chemical variables by comparing the thermograms of the materials before and after exposure to the variable outside the calorimetry. The basics of application of calorimetry to food materials are discussed in detail in this book. However, it is important to start the discussion with a summary of the advantages of using calorimetry for study of biological materials. These advantages can be outlined as follows: • Direct measurement of the energetics of the transition is obtained (ΔH and ΔCp). The experimental results are not model dependent. • Calorimetry can be applied to a range of materials, pure or complex. Materials do not have to be optically transparent or have chromophores as required by spectroscopic methods. • Materials do not have to be uniform or have to be a homogeneous mixture. In fact, in addition to pure materials, the technique can be used to evaluate the interactions among the components in a complex system and how the interactions are altered by the processing. • Calorimetry does not require elaborate or destructive sample preparation. • Calorimetry is an established technique which has been around since the 16th century (Haines 1995). Today, the instruments are highly developed for accurate measurement of thermal events. The theory behind the technique is well developed, which facilitates interpretation of the data (Höhne et al. 2003).

8

Calorimetry in Food Processing

While the technique is powerful, the validity and utility of the data depend strongly on the careful use of the equipment and correct interpretation of data. Some analytical methods provide results specific to materials; however, calorimetry data depends on the conditions used during the experiment (Haines 1995). One must be careful in choosing the calorimetry parameters: 1. Time scale: Especially in dynamic measurement systems, for events to be detected the experimental time scale should match the time scale of the observed event. 2. Magnitude of the heat flow: If the energy associated with the transition is small, it can lead to ambiguities in its detection. Increasing the scanning rate enhances the signal; however, it may cause deviation from equilibrium conditions, which requires models beyond the standard equilibrium thermodynamics treatment of calorimetry data. 3. Moisture loss during experiment: Biological samples in general are high-moisture content materials. If the sample cell is not sealed well, the moisture content of the sample will change due to evaporation during the course of experiment. This may lead to overestimation of the transition temperature as well as the transition enthalpy change. 4. Interpretation of overlapping peaks: Biological samples may contain multiple components that undergo thermally induced transitions at similar temperatures. As a result, overlapping peaks may be observed on a differential scanning calorimetry (DSC) thermogram. Even if the origin of the event is known, because the peak temperatures may shift due to overlap, individual events may appear to happen at different temperatures. The individual peaks can be resolved experimentally (Barrett et al. 2002, 2005), or the complex thermograms can be deconvoluted by using special software (Fessas and Schiraldi 2000).

An Overview of the Book This book focuses on the basics of calorimetry and specific applications for characterization of food systems. The material in this book is designed to provide food scientists, food technologists, and food engi-

Calorimetric Methods as Applied to Food: An Overview

9

neers with knowledge about the potential uses of calorimetry as a tool in process design and optimization as well as product development and improvement. The book consists of two sections. The first section includes eight chapters describing the principles of calorimetry alone and coupled with other techniques as well as the use of calorimetry to characterize biological systems ranging from pure single phase to multicomponent and multiphase systems of solids, dilute and concentrated solutions of macromolecules, emulsions, foams, and bacteria. The second section of the book is designed to illustrate the use of calorimetric data to guide engineers and processors in design and optimization of processes. The multicomponent nature of the food materials presents a challenge in that the specific component undergoing a conformational or phase transition may be in small quantity relative to the whole, thus generating an insufficient heat signal to detect. As an alternative to increasing the heating rate, the heat signal can be enhanced by increasing the sample size. In Chapter 2, the challenges of increasing sample size and strategies to overcome these challenges by using microcalorimetry are discussed. The increased interest of consumers in minimally processed foods pushed the food research community to explore novel technologies that present alternatives to thermal processing. High hydrostatic pressure (HHP) processing has become the most promising alternative technology. Currently, HHP processing is implemented for several foods and has a market value of more than $500 million. The optimization of HHP processing requires knowledge of physical properties under conditions relevant to the pressures attained during the process. The design of calorimeters operating under the pressures used in industry is very challenging. Chapter 3 focuses on the design of a high-pressure calorimeter and the protocols to be followed for calibration, data collection, and analysis. Applications of ultrasensitive calorimetry to proteins and their interactions in dilute solution are examined in Chapter 4. Emphasis is placed on the practical aspects of collecting and analyzing differential scanning calorimetry (DSC) data to characterize the thermal and thermodynamic stability and the thermodynamic origins of that stability in a protein in solution. The thermodynamics of association between a protein and a small molecule or another macromolecule are quantifiable by application of isothermal titration calorimtery (ITC). The

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Calorimetry in Food Processing

design and execution of ITC experiments are described with emphasis on the information content of titration curves. Together or separately, DSC and ITC provide valuable tools for developing a predictive understanding of protein stability and interactions as a function of temperature and solution conditions. Investigation of dilute systems is essential to elucidate the behavior of macromolecules thermodynamically. However, in biological systems and foods, dilute systems are rarely encountered. Commonly, macromolecules exist in foods at high concentration and in complexes with other macromolecules and low-molecular-weight compounds. Heat denaturation and aggregation of proteins are common during food processing and affect the quality attributes of food. Therefore, Chapter 5 uses calorimetry to study the effects of pH, salts, alcohols, and polysaccharides on thermal denaturation and aggregation of food proteins in order to elucidate the mechanisms of structure formation, structuretexture and structure-physical property relationships in foods. Proteins also play an important role in development of emulsions and foams that are examples of multicomponent and multiphase food systems. Both the formation and the stability of such complex systems depends on the adsorption properties of proteins at oil-in-water or gasin-water interfaces. Chapter 6 reviews the use of DSC in scanning and isothermal mode for monitoring effects of food composition and physicochemical environment on the conformation and structural modifications of proteins in emulsions under the time-temperature combinations relevant to processing. The results presented in this chapter illustrates that a combination of thermodynamic and kinetic data obtained by using DSC in scanning and isothermal modes provide a better understanding of emulsions and the ability to control structure-forming mechanisms in food systems. The main goal of food processing is to manufacture foods that are stable and safe to consume, which requires the inactivation of bacteria to prevent spoilage and foodborne diseases. Thermal inactivation of microorganisms is associated with irreversible denaturation of membranes, ribosomes, proteins, and nucleic acids. DSC can be used to monitor the reversible and irreversible changes in the cellular components of bacteria. Chapter 7 describes using DSC to provide an insight into the mechanism of bacterial cell inactivation. Also illustrated is the utility of DSC data to quantitatively evaluate bacterial inactivation kinetics. Calorimetry can be used to evaluate the effect of food-

Calorimetric Methods as Applied to Food: An Overview

11

processing variables other than heat on bacteria. Chapter 7 describes the analysis by calorimetry of damage to bacterial cells due to chemical, nonthermal, or antibiotic treatments and the relationship between the calorimetric data and loss of cell viability. The data collected by calorimetry are complementary to data collected by other biophysical methods. Thermal analysis is a valuable tool to observe phase transition, but especially for complex systems, such as lipids, the thermal observables can be due to a variety of structures forming during the heating or cooling process. Generally, another technique such as Fourier transform infrared spectroscopy or x-ray diffraction (XRD) is used in parallel to acquire structural information. Obtaining complementary data can be further improved by performing simultaneous DSC-FTIR (Yoshida 1999) or DSC-XRD (Yoshida et al. 1996; Ollivon et al. 2006) measurements on the same sample. Chapter 8 describes in detail the development of a new instrument, called MICROCALIX, combining XRD at both wide and small angles as a function of temperature (XRDT) or time (XRDt), and high-sensitivity DSC, in the same apparatus with scanning or isothermal modes over the temperature range −30 to +230 °C. This approach enables one to obtain complementary thermal and structural properties information on the same sample in one experiment. Foods exhibit thermally induced transitions over a temperature range between −50 °C and 300 °C. The thermal behavior of a food is mainly a reflection of its major component, however, with some change due to interactions with other components. Chapter 9 focuses on the use of phase transition information in development of phase diagrams that can be used for efficient process design. Heat of a solution as a parameter of great importance for food powder dissolution is also emphasized. The relevance of calorimetric data to the food industry is illustrated by specific examples. Biological samples undergo changes even when they are kept at constant temperature. Changes, physical or chemical in origin, may produce heat that can be studied with isothermal calorimetry. However, detection and monitoring of small quantities of heat, especially at the initial stage of the physical or chemical event, requires using a highsensitivity calorimeter. Chapter 10 focuses on application of isothermal calorimetry, a relatively less-exploited application of calorimetry in comparison with DSC, for qualitative and quantitative analysis of food stability, shelf life, and isothermal cooking processes. Specific

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Calorimetry in Food Processing

examples are discussed, from simple ingredients to complex biological processes. Cereal-based products are staple foods all around the world. Although the main component in such foods is starch, thermally induced transitions are highly affected by the presence of other compounds in cereals, including proteins, nonstarch carbohydrates, and lipids, either due to competition for available water or direct interactions. Chapter 11 provides a review of thermal analysis applications to cereal-based products and cereal processing. This chapter discusses in detail mathematical treatment of the complex thermograms to deconvolute the contributions from different components in the system. Drying has been used as a method of food preservation since ancient times. In modern practice, water is removed by evaporation upon application of heat or by sublimation from a frozen product under vacuum. During the drying process, amorphous or partially crystalline states are formed. The thermal stability of the amorphous state is defined by the glass transition temperature, which depends strongly on the amount of water present in the food system. Chapter 12 reviews the use of calorimetric data for selection of dehydration parameters to produce products with improved storage stability. This chapter also discusses the relationship between the glass transition and collapse of structure in freeze-dried materials, flavor retention by encapsulation of volatiles in amorphous systems, solids crystallization, lipid oxidation, nonenzymatic browning, and enzymatic changes. Chapter 13 describes the relatively new technique of scanning transitiometry developed by Randzio (1996) based on scanning of one of the three variables—pressure, volume, or temperature—and measurement of the other two, as well as the heat signal. This chapter also discusses the specific application of scanning transitiometry for gelatinization of wheat starch dispersions and for investigation of pressure shift freezing. In addition, the technique is applied to the study of water, water in pork muscle, solutions of gelatin in water, and lipids. Chapter 14 focuses on the application of calorimetry to determine the effects of high hydrostatic pressure on starch gelatinization as well as to characterize the recrystallization of the gelatinized starch during subsequent storage for calculation of starch recrystallization kinetic parameters. These results are used in selection and optimization of HHP processing parameters and storage conditions for foods containing starch.

Calorimetric Methods as Applied to Food: An Overview

13

Foods show chemical reactivity leading to self-heating and selfignition of hot spots. Especially handling of dry powders in bulk, such as in milling, drying, and packaging, can be dangerous due to potential dust explosions. Chapter 15 reviews the evaluation by calorimetry of the thermal consequences of exothermic decompositions in foods, describes the methodology for quantifying the risk in terms of its severity and its probability, and discusses methods for collecting the stability data correctly. Specific cases of formation of hot spots in dryers, storage and hot discharge, and transport safety are discussed. The importance of establishing safe conditions for handling of materials in prevention of accidents in the food industry is emphasized.

References Barrett A., Cardello A., Maguire P., Richardson M., Kaletunç G., and Lesher L. 2002. Effects of Sucrose Ester, Dough Conditioner, and Storage Temperature on Long-Term Textural Stability of Shelf-Stable Bread. Cereal Chem, 79(6): 806–811. Barrett A.H., Marando G., Leung H., and Kaletunç G. 2005. Effect of Different Enzymes on the Textural Stability of Shelf-stable Bread. Cereal Chem, 82(2): 152–157. Fessas D., and Schiraldi A. 2000. Starch Gelatinization Kinetics in Bread Dough, DSC Investigations on Simulated Baking Processes. J Therm Anal Calorim, 61:411–423. Haines P.J. 1995. Thermal Methods of Analysis, Principles, Applications and Problems. Glasgow: Blackie. Höhne G.W.H, Hemminger, W., and Flammersheim, H.J. Differential Scanning Calorimetry: an Introduction for Practitioners. 2nd Ed. Berlin; New York: Springer-Verlag, 2003. Kaletunç G. 2007. Prediction of Heat Capacity of Cereal Flours: A Quantitative Empirical Correlation. J Food Eng, 82(2):589–594. Ollivon M., Keller G., Bourgaux C., Kalnin D., Villeneuve P., and Lesieur P. 2006. DSC and High Resolution X-Ray Diffraction Coupling. J Therm Anal Calorim, 85:219–224. Randzio S.L. 1996. Scanning Transitiometry. Chemical Society Reviews, 25:383. Yoshida H., Ichimura Y., Kinoshita R., and Teramoto Y. 1996. Kinetic Analysis of the Isothermal Crystallization of an N-Alkane and Polyethylene Observed by Simultaneous DSC/FT-IR/WAXD Measurement. Thermochim Acta, 282/283: 443–452. Yoshida H. 1999. Structure Relaxation of N-Alkanes Observed by the Simultaneous DSC/FTIR Method. J Therm Anal Calorim, 57(3):679–685.

Chapter 2 Methods and Applications of Microcalorimetry in Food Pierre Le Parlouër and Luc Benoist

Introduction The Heat Flux Calorimetric Principle DSC versus Heat Flux Microcalorimetry Comparison between DSC and Heat Flux Microcalorimetry The Calvet Principle Calibration Description of Different Heat Flux Calorimeters Used for Food Characterization High Sensitivity Heat Flux Calorimeter The Mixing and Reaction Heat Flux Microcalorimeter Methods of Microcalorimetry in Food Heat Capacity Determination Heating Mode Mixing and Reaction Calorimetry Pressure Calorimetry Calorimetry under Controlled Relative Humidity Conclusion References

15 17 19 19 22 23 26 26 29 30 30 35 40 43 45 45 46

Introduction Heat is involved at different steps in the preparation of foods, such as cooking and processing. During heating, cooling, or freezing, the food products undergo different types of transformations, including melting, 15

16

Calorimetry in Food Processing

crystallization, gelation, gelatinization, denaturation, and oxidation. All these transformations occur in a certain range of temperature and are associated with heat variations. The thermal analysis techniques, and specifically differential scanning calorimetry (DSC), are used as a main approach for investigating the thermal properties of foods (Harwalkar and Ma 1990; Farkas and Mohácsi-Farkas Csilla 1996; Schiraldi et al. 1999; Raemy et al. 2000). However, in most food processing food ingredients are mixed or diluted with a liquid (water, milk) or with a powder (sugar, salt, yeast). For simulation of such transformations and interactions, the limited volume and the lack of in situ mixing constitute the major drawbacks of the DSC technique. For such investigations, microcalorimetry (in the isothermal and scanning modes) is the ideal solution because it has the capacity to work on bulk materials and diluted solutions with a very high sensitivity. Microcalorimeters are found as reaction or solution calorimeters, pressure calorimeters according to the transformation to be simulated, and provide a wide range of experimental conditions for applications such as mixing, dilution, wetting, neutralization, and enzymatic reaction, which have relevance to food industry. For a food technologist, it is very important to understand various thermal and functional properties of food components and ingredients for fundamental research, food quality assurance, and for product development. Although many articles have been published in the field, to our knowledge a book dedicated to the very challenging field of microcalorimetric applications in food science has not been available. Microcalorimetry, compared with DSC, still remains as a lesserknown technique. For a long time, it has suffered from a reputation as an old and slow technique (needing days of experimentation), of large instruments (microcalorimetry meaning microquantity of measured heat and not microsize instrument), and was used mainly by experts. Especially with the development of microcalorimetry in the biological and pharmaceutical fields (Ladbury and Chowdhry 2004; Craig and Reading 2007), there have been many advances in instrumentation in the last decade that facilitated the use of calorimeters in laboratories. Microcalorimetry benefits food research and opens new opportunities of experiments and applications that are to be described in this chapter.

Methods and Applications of Microcalorimetry in Food

17

The Heat Flux Calorimetric Principle Existing calorimeters operate on the following principles: • the heat flux principle • the heat-compensating principle • the heat-accumulating principle In this chapter, the heat flux calorimetric principle is described as it is used in most calorimeters for food characterization. The heat flux calorimeter consists of a measurement chamber surrounded by a detector (thermocouples, resistance wires, thermisters, thermopiles) to integrate the heat flux exchanged by the sample contained in an adapted vessel. The measurement chamber is insulated in a surrounding heat sink made of a high thermal conductivity material. The heat flux for a given sample at a temperature Ts is equivalent to: dqs dh dT =− + Cs s dt dt dt

(2.1)

where dh/dt is heat flux produced by the transformation of the sample or the reaction and Cs is heat capacity of the sample, including the container. The heat flux dqs/dt is exchanged with the thermostatic block at a temperature Tp through a thermal resistance, R, described by the following relation: dqs Tp − Ts = dt R

(2.2)

Equation 2.1 shows that the thermal contribution due to the heat capacity of the sample and container is very large and will provide a major disturbance at the introduction of the container in the calorimeter. From Equation 2.2, it is also evident that any temperature perturbation of the thermostatic block will affect the calorimetric measurement. To solve these issues, a symmetrical calorimeter is preferred. Two identical calorimetric chambers, one housing a container with the sample and an identical reference container an inert material

18

Calorimetry in Food Processing Ts Cs

Sample + container

Thermostatic block R

Tp Heating elements Heat sink

Figure 2.1. One-cell calorimetric principle.

(the reference container may also be empty) are placed in the thermostatic block at the same temperature, Tp. The heat flux difference is measured between the two chambers. dq dqs dqr dh dT dT = − =− + C s s − Cr r dt dt dt dt dt dt

(2.3)

Here, Cr is heat capacity of the reference, including the container, and Tr is temperature of the reference. Equation 2.2 becomes: dq Tr − Ts = dt R

(2.4)

or by derivation R

d 2 q dTr dTs = − dt 2 dt dt

(2.5)

By combining Equations 2.4 and 2.5, the characteristic equation for the calorimetric measurement is obtained. dTp dh dq d 2q =− + (Cs − Cr ) − RCs 2 dt dt dt dt

(2.6)

Methods and Applications of Microcalorimetry in Food

19

Table 2.1. Some endothermic and exothermic effects for different food types. Food Type

Endothermic Effect

Exothermic Effect

Fat, oil Protein

Melting, lipidic transition Denaturation

Enzyme

Denaturation

Starch

Gelatinization, glass transition Melting Melting Melting, glass transition

Crystallization, oxidation Aggregation, crystallization Aggregation, enzymatic reaction Retrogradation, oxidation

Milk Hydrocolloid, gelatin Carbohydrates Yeast Bacteria

Crystallization, oxidation Gelation Crystallization, decomposition Fermentation Growth, metabolism, fermentation

If dh/dt corresponds to an endothermic transformation or reaction, the dh/dt value is positive. If dh/dt corresponds to an exothermic transformation or reaction, the dh/dt value is negative. If the calorimetry is performed isothermally, the parameter dTp/dt is null. In a small perturbation of the temperature Tp of the thermostatic block, the corresponding thermal effect will be minimized if the Cs and Cr heat capacities are similar. The last term R Cs d2q/dt2 (called as thermal lag) mostly depends on the thermal resistance or the time of response of the calorimeter and the heat capacity of the sample and the container. For a long period (t >> RCs) it will be negligible. Table 2.1 gives an overview of some endothermic or exothermic effects occurring in various types of food. DSC versus Heat Flux Microcalorimetry Comparison between DSC and Heat Flux Microcalorimetry The differences between DSC and heat flux microcalorimetry are related mainly to the size of the sample and the sensitivity of the measurement but also to interactions between solid and liquid materials. To clearly understand the difference, it is important to analyze the technological principles that are behind each technique.

20

Calorimetry in Food Processing

International Confederation for Thermal Analysis and Calorimetry (ICTAC), in its nomenclature, considers two types of DSC: the heat flux DSC and the power-compensated DSC (www.ictac.org). Even if the measurement principles are different, the heat transfer from (or to) the sample is about the same. The detector for each DSC model is a plate-type design. The sample, contained in a metallic crucible, is placed and centered on the plate acting as a flat-shaped sensor. A reference crucible (empty or containing an inert material) is placed on the other plate. In plate DSC (heat flux type and power-compensated type), the heat exchange between the sample and the detector occurs through the bottom of the crucible, corresponding to a two-dimensional detection. In fact, only a part of this heat transfer is measured, as a significant part is dissipated through the walls and the cover of the crucible (Figure 2.2). The ratio of the heat flux measured by the sensor to the total heat flux produced by the thermal event, calculated by simulation using

Figure 2.2. Schematic of a plate-shaped DSC sensor.

Methods and Applications of Microcalorimetry in Food

21

50

% Flux

40 30 20 0.01 mm 0.05 mm 0.10 mm

10 0 0

150

300

450

600

Temperature (°C)

Figure 2.3. Efficiency ratio of a flat-shaped DSC as a function of the sensor plate thickness.

thermal modeling software, shows that only around half of the heat flux is dissipated through the plate (Daudon 1996; Le Parlouër and Mathonat 2005). Figure 2.3 clearly shows that the efficiency rapidly decreases with the temperature and the thickness of the plate. The efficiency is also affected by the amount of the sample tested. Therefore, it is recommended to work with small amount of material (about 5–10 mg) when using a plate DSC to minimize the heat losses. The thermal conductivities of the crucible and the gas used in the experimental chambers also are very important parameters to be considered in the efficiency of the heat exchange. For example, a very heatconductive gas (helium) will favor the heat transfer between the crucible and the detector, but at the same time increase the heat losses. Hence, the calibration of a plate-type DSC (heat flux or powercompensated type) is very critical and has to be run with the experimental conditions selected for testing the sample. The main difference between heat flux calorimetry and DSC (heat flux or power compensated type) is that in a microcalorimeter, the heat exchange between the sample and the detector is completely measured. Such a high efficiency is achieved by applying the technological principle developed by Tian and Calvet. Calorimeters are also designed on the power-compensating principle using a detector that surrounds the sample in the same way. MicroCal (www.microcal.com) and CSC (now TA Instruments)

22

Calorimetry in Food Processing

(www.tainstruments.com) have developed such ultrasensitive instruments, mostly used for the investigations of dilute liquids. Because these calorimeters operate with fixed vessels, they are not well-adapted for the characterization of foods. The Calvet Principle The detection is based on a three-dimensional fluxmeter sensor. The fluxmeter element consists of a ring of several thermocouples in series (Figure 2.4). The corresponding thermopile of high thermal conductivity surrounds the experimental space within the calorimetric block. The radial arrangement of the thermopiles guarantees an almost complete integration of the heat. This is verified by the calculation of the efficiency ratio that indicates that an average value of 94% ± 1% of heat is transmitted through the sensor on the full range of temperature of the Calvet-type DSC (Figure 2.5). In this setup, the sensitivity of the DSC is not affected by the type of crucible, the type of purge gas, or the flow rate. The main advantage of the setup is the increase

Figure 2.4. Schematic of the Calvet type calorimeter.

Methods and Applications of Microcalorimetry in Food

23

Ratio of energy (%)

Ratio of energy transmitted through the thermopile 95 94.8 94.6 94.4 94.2 94 93.8 93.6 93.4 93.2 93 0

100

200

300

400

500

T (°C)

Figure 2.5. Efficiency ratio of a Calvet-type calorimeter versus temperature.

of the experimental vessel’s size, and consequently the size of the sample, without affecting the accuracy of the calorimetric measurement. Calibration The calibration of the calorimetric detectors is a key parameter and has to be performed very carefully. In fact, the main purpose of the calibration is to transform the electric signal (emf) provided by the thermocouples of the detector expressed in microvolts (μV) in a thermal power (heat flux) signal expressed in milliwatts (mW). For DSC detectors, this conversion is achieved using metallic reference materials (Richardson and Charsley 1998). Although this recommended procedure is widely used, it has some limitations: • The calibration can only be performed at the temperature at which the reference material melts. • At low temperature, it is difficult to find good reference materials. • The calibration is mostly performed in a heating mode, but very rarely in the cooling mode. • The accuracy of the calibration depends on the purity and quality of the reference materials. For Calvet-type calorimeters, a specific calibration, so-called Joule effect or electrical calibration, has been developed to overcome the drawbacks described above (Calvet and Prat 1964). A dedicated vessel

24

Calorimetry in Food Processing

P

K=S/P S

Figure 2.6. Joule effect calibration.

with a built-in electrical heater (platinum resistance) simulating the experimental vessel that contains the sample is introduced into the calorimeter at a given temperature. A well-defined electrical power (between 20 and 200 mW) is applied to the resistance. The calorimeter gives a corresponding deviation (Figure 2.6). The stabilized signal, expressed in microvolts, is directly correlated to the applied power, expressed in milliwatts. The main advantages of this type of calibration are as follows: • It is an absolute calibration. • The use of standard materials for calibration is not necessary. The calibration can be performed at a constant temperature, in the heating mode and in the cooling mode. • It can be applied to any experimental vessel volume. • It is a very accurate calibration. To understand the direct correlation between the electrical signal and the heat flux, consider that a power, W, is fully dissipated in a calibration vessel (Figure 2.7) surrounded by a fluxmeter composed of crowns of thermocouples (Figure 2.3). An elementary power, wi, is dissipated through each thermocouple giving an elementary variation of temperature ΔTi between the internal and external weldings: wi = δ i ΔTi where Δi is the conductance of the thermocouple.

(2.7)

Methods and Applications of Microcalorimetry in Food

25

W

wi ei g i Δq i

Figure 2.7. Joule effect calibration principle.

The corresponding variation of temperature generates an elementary electromotive force (emf) according to the Oersted law: ei = ε i ΔTi

(2.8)

where εi is the thermoelectric constant of the thermocouple. By combining Equations 2.7 and 2.8, for the thermocouples in series, we obtain: E = ∑ ei = ∑

εi wi δi

(2.9)

Because all the thermocouples are identical, Equation 2.9 can be expressed as follows: E=

ε ∑ wi δ

or

E=

ε W δ

(2.10)

Equation 2.10 shows that the power dissipated in the vessel is directly correlated with the heat flux. The term ε/Δ corresponds to the calibration factor of the calorimeter.

26

Calorimetry in Food Processing

Description of Different Heat Flux Calorimeters Used for Food Characterization According to the Calvet principle, many different calorimeters have been designed with various temperature ranges, small and large volumes, with a broad range of sensitivity. In this chapter, we describe two different Calvet calorimeters (www.setaram.com) that are used worldwide in many food research laboratories. High Sensitivity Heat Flux Calorimeter The development of the very high sensitivity heat flux calorimeter (www.setaram.com) was mainly motivated by the limitations of the standard DSCs: small amount of sample, limited sensitivity, no possibility of interaction or mixing. It was designed to be used as a multipurpose calorimeter working in isothermal and scanning modes with batch and flow capacities on a significant volume of sample (1 cm3). The calorimetric chamber is made of a highly thermal conductive block with two cylindrical cavities for the experimental vessels (sample and reference). The detectors are built with semiconducting Peltier elements, characterized for their high sensitivity compared with a standard thermocouple-based detector. For the temperature control of the calorimeter, two principles are used: • A thermostatic loop of liquid flows around the calorimetric block for a temperature range from −20 °C to 120 °C • Different shields with Peltier elements are located around the calorimetric block to extend the use at a lower temperature for a temperature range from −45 °C to 120 °C. In both cases, the vessels are easily removed from the calorimetry block. This is a key point for the cleaning of the vessels when different types of foods, such as fatty compounds, gels, and proteins, are used. The tops of the calorimeters are opened to allow the introduction of fluids (gas, liquid) by means of adapted and dedicated vessels. The thermostatic loop of liquid provides a prestabilizing ring at the upper part of the calorimeter that allows the liquid to preheat before entering the calorimetric chamber. According to the type of experiments to be

Methods and Applications of Microcalorimetry in Food

27

Figure 2.8. Standard and mixing vessels (batch), fluid circulation vessel (flow).

performed on food components, there are different experimental vessels for the batch or the flow applications (Figure 2.8). The standard batch vessel is mainly used to investigate food components in a liquid or solid form in a closed system. The batch-mixing vessel is composed of two chambers that allow isolation of each material before mixing in the calorimeter. The mixing operation is achieved by pushing the rod from outside. The batch high-pressure vessel is mainly dedicated to investigation of food components under pressure, especially for modification of structure (glass transition, polymorphism) when high pressure is applied. For such experiments, the calorimetric vessel is fitted with a highpressure gas panel (maximum pressure: 1000 bar) (Le Parlouër et al. 2004). The fluid-mixing vessel is designed to introduce a gas or a liquid into the vessel to interact with the sample inside. Before introducing a liquid, the liquid temperature is stabilized at the temperature of the calorimeter. The fluid-mixing vessel makes possible the mixing of two liquids in situ in the calorimetric vessel using an adapted mixer. The entering liquids are prestabilized at the temperature of the calorimeter and are introduced through micropumps at variable flow rates. Table 2.2 gives an overview of the variety of applications that can be performed with the different vessels.

Table 2.2. Applications of the MicroDSC technique versus the vessel and the heating mode. Vessel

Heating Mode

Component

Application

Batch

Scanning Isothermal Scanning Scanning

Protein (animal, cereal…) Protein Enzyme Starch

Isothermal Scanning

Starch Milk

Scanning

Fat

Isothermal Scanning Scanning

Fat Hydrocolloids Sugar

Isothermal Scanning

Aroma Fat, chocolate

Denaturation, aggregation, lyophilization Crystallization denaturation, stability Gelatinization, retrogradation, glass transition Crystallization (stalling) Melting, crystallization, denaturation, aggregation Melting, crystallization, lipidic transition, polymorphism Crystallization Melting, gelation Melting, crystallization, glass transition (amorphism) Stability Polymorphism versus pressure Glass transition versus pressure Enzymatic reaction Wetting Yogurt processing Dough and bread processing Bacteria growth, food safety Oxidative stability Enzymatic reaction

Batch high pressure

Starch Batch mixing

Isothermal

One fluid vessel Two-fluid mixing vessel

Isothermal

Enzyme Starch Dairy bacteria Yeast Bacteria Oil

Isothermal

Enzyme

28

Methods and Applications of Microcalorimetry in Food

29

The Mixing and Reaction Heat Flux Microcalorimeter The mixing and reaction microcalorimeter (www.setaram.com) is used for larger amounts of materials to better fit with the experimental needs of the food industry. The microcalorimeter can be used as a DSC for temperature scanning, but with large-volume samples. It is, however, more suitable for the applications in the isothermal mode. The microcalorimeter has a large experimental volume (15 cm3). It is built around a metallic conductive block with two cavities that contain the thermopiles, which are made of crowns of thermocouples. The block itself is surrounded by the heating element and arranged in an insulated chamber. The calorimeter can be fitted on a rotating mechanism to use with a special mixing vessel. The microcalorimeter offers a large choice of experimental vessels for use with various applications. The most commonly used vessels in food research are as follows: • The batch standard vessel is designed for investigating transformation during heating or cooling a large volume of samples in the solid or liquid form. It also can be used to determine heat capacity. • The batch high-pressure vessel is designed for simulation of reaction and decomposition under pressure in a closed vessel or under controlled pressure (max: 100 bar). It is used to define safety conditions of some food-processing operations and also for simulation of supercritical gas extraction. The gas-flow vessel is fitted with two coaxial tubes and is used to produce a circulation of gas (inert or active) around the sample. It is used for investigation of oxidative stability of foods. The mixing vessel using the rotating mechanism is divided into two chambers and separated by a metallic lid. One of the materials is placed in the lower chamber (i.e., powder) and the other material is placed in the upper chamber (i.e., liquid). The mixing of the two components is provided by rotating the calorimeter, the metallic lid acting as a stirrer. This mixing vessel is designed for investigation of liquid-liquid mixing (dilution, neutralization) or solid-liquid mixing (dissolution, hydration, wetting). The membrane mixing vessel is used for mixing of viscous samples, often seen with food components and for applications in which the rotation of the calorimeter cannot be used. In such a vessel, the separation between both chambers is achieved with a thin membrane

30

Calorimetry in Food Processing

(metal or PTFE). The vessel is fitted with a metallic rod that is operated from outside the calorimeter. The mixing of components is obtained by pushing the rod to break the membrane. The rod is also used as a stirrer during the test. The ampoule mixing vessel is designed for a slow dissolution process and for a wetting operation. The sample is sealed under vacuum in a breakable ampoule. The vacuum operation allows desorbing the surface of the solid sample for easier dissolution. The sealed ampoule and the solution are introduced into the vessel. By breaking the ampoule, the solid and liquid samples are brought into contact. The Table 2.3 gives an overview of the major calorimetric applications, either in scanning or isothermal modes. Methods of Microcalorimetry in Food Microcalorimetry offers a variety of methods that are applied to the characterization of foods and their components. Heat Capacity Determination Heat capacity plays an important role in thermal process and in refrigeration applications. Heat loads, processing times, and industrial equipment sizes are influenced by the heat capacity of the material. Combined with thermal conductivity and thermal diffusivity, heat capacity data are needed for modeling of the thermal processes. Heat capacity varies with temperature and composition, as well as water content (Kaletunç, 2007). Because food material can be in solid or liquid form, different ways of measuring heat capacity using the calorimetric techniques have been developed. Heat capacity is thermodynamically defined as the ratio of a small amount of heat ΔQ added to the substance to the corresponding small increase in its temperature dT: C=

δQ dT

(2.11)

For processes at constant pressure, the heat capacity is expressed as:

δH⎞ Cp = ⎛ ⎝ δT ⎠ p

(2.12)

Methods and Applications of Microcalorimetry in Food

31

Table 2.3. Applications of the C80 calorimetric technique versus the vessel and the heating mode. Vessel

Mode

Component

Application

Batch standard

Scanning

Starch Salt Carbohydrate

Batch high pressure

Scanning

Coffee

Oil

Gelatinization, retrogradation Solubility Melting, crystallization, amorphism, decomposition Safety (roasting), supercritical CO2 extraction Self-ignition, explosion (powder) Gelatinization under pressure, glass transition versus pressure Polymorphism versus pressure Oxidative stability

Oil Sugar Salt Enzyme Hydrocolloid Starch Yeast Food powder

Neutralization Dissolution Dissolution Enzymatic reaction Binding Wetting, gelatinization Fermentation Wetting, dissolution

Cereal Starch

Fat, chocolate Gas flow Mixing (reversing)

Mixing (membrane) Mixing (ampoule)

Scanning, isothermal Isothermal

Isothermal Isothermal

Although DSC is a technique well suited to measure heat capacity (Richardson and Charsley 1998), essentially only one procedure has been developed using a continuous heating mode for solid samples. In this chapter, another procedure is described using a step-heating mode. Heat capacity determination in temperature-scanning mode If there is no conformational or phase transformation for the temperature range considered, the calorimetric signal for a given mass of sample heated at a constant heating rate dT/dt is relative to the following relation for the sample side:

32

Calorimetry in Food Processing ⎛ dq ⎞ = ( m c + m c ) dT s p( s) cs p( cs) ⎝ dt ⎠ s dt

(2.13)

where ms and mcs are, respectively, sample mass and vessel mass (including the cover) and cp(s) and cp(cs) are, respectively, specific heat capacity of the sample and its vessel. For the reference side, an empty vessel is used giving the corresponding signal: ⎛ dq ⎞ = ( m c ) dT cr p( cr ) ⎝ dt ⎠ r dt

(2.14)

where mcr is reference vessel mass and cp(cr) is specific heat capacity of reference vessel (equal to cp(cs)). The resulting differential calorimetric signal dq/dt is given by the following equation: ⎛ dq ⎞ = ( m c + m c − m c ) dT s p( s) cs p( cs) cr p( cr ) ⎝ dt ⎠ dt

(2.15)

To get rid of the thermal effect generated by both vessels, the same test (called blank test) is run with identical empty containers. The following equation describes the blank test heat flow. ⎛ dq ⎞ = ( m c − m c ) dT cs p( cs) cr p(cr ) ⎝ dt ⎠ b dt

(2.16)

By subtracting the two calorimetric traces, the specific heat capacity of the sample is extracted (Figure 2.9). c p( s) =

1 ⎡⎛ dq ⎞ ⎛ dq ⎞ ⎤ dT − ms ⎢⎣⎝ dt ⎠ ⎝ dt ⎠ b ⎥⎦ dt

(2.17)

As described in the calibration section, the Joule effect technique allows conversion of calorimetric signal in milliwatts without the need of standard reference materials. Therefore, in Equation 2.17, all of the parameters (sample mass, calorimetric signals, heating rate) are accu-

Methods and Applications of Microcalorimetry in Food

33

Heat flow (mW)

Ab As

T time

Figure 2.9. cp determination in the temperature-scanning mode.

rately known to determine the specific heat capacity of the sample cp(s) (expressed in J.g−1.°C−1) at a given temperature. For DSC technique, a third test is needed using a standard reference material (sapphire) that has a known specific heat capacity. cp determination in the temperature step mode The technique described in previous section is easy to use, but has a drawback regarding the accuracy of the cp determination. Using the temperature scanning mode, the sample is continuously heated and is never at the thermal equilibrium. However, cp is a thermodynamical parameter, defined at the thermal equilibrium. The temperature step mode has been developed to address this limitation. A temperature step is applied to the sample, and the thermal equilibrium is established (characterized by return of the baseline) after each step. If Equation 2.15 is integrated from time t0 (beginning of the step) to time tn (return to the baseline), the corresponding equation is obtained:

[Q ]tt0n = (ms c p(s) + mcs c p(cs) − mcr c p(cr ) ) ΔT

(2.18)

where cp corresponds to the mean cp value between the two temperatures defining the step of temperature. Q is obtained by integrating the corresponding surface defined by the calorimetric signal between t0 and tn. The signal corresponding to the blank test is subtracted when

34

Calorimetry in Food Processing

an identical step of temperature is applied to obtain the final equation for the mean cp of the sample. c p( s) =

1 (Q − Qb ) ΔT ms

(2.19)

In Equation 2.19, the result is independent of fluctuations of the baseline between the tests contrary to that of specific heat determination in temperature scanning mode. cp determination for liquids Both methods described above apply mainly for the cp determination of solid and powder foods. They also can be used for liquids, but the cp contribution of the vapor above the liquid sample must be accounted for to have an accurate measurement. The correction can be obtained by using a vessel designed for the cp determination of liquids (Cerdeirina et al. 2000). The vessel is a cylindrical container with a tube welded on the top (Figure 2.10). The liquid is introduced in the calorimetric vessel via the tube using a syringe with a long needle, which allows a complete filling of the vessel without a vapor phase. As the tube is opened, the liquid will freely expand when heating. The

Figure 2.10. Liquid cp vessel and principle.

Methods and Applications of Microcalorimetry in Food

35

cp determination is run for a given volume V of liquid, located in the calorimetric detection zone. If Q0 is the differential calorimetric area corresponding to an increase ΔT of the temperature of the calorimeter when the two vessels (sample and reference) are empty, Q1 when the measure vessel is filled with a standard liquid of known heat capacity, and Q2 with the liquid to be investigated, the following equations are obtained: Q1 − Q0 = V ⋅ ΔT ⋅ S ⋅ ρ1 ⋅ c p1

(2.20)

Q2 − Q0 = V ⋅ ΔT ⋅ S ⋅ ρ2 ⋅ c p 2

(2.21)

where S is calibration coefficient of the calorimeter, V is volume of the vessel, ρ1 and ρ2 are masses of standard and sample, and cp1 and cp2 are heat capacities of standard and sample. The heat capacity of the liquid sample, at a given temperature, is obtained without needing to know and measure the corresponding volume V: ⎡ (Q − Q0 ) ⎤ ρ1 cp2 = ⎢ 1 c p1 ⎣ (Q2 − Q0 ) ⎥⎦ ρ2

(2.22)

The determination of the specific heat capacity requires the measurement of the density of the liquid sample. This cp measurement does not need vapor phase correction. Heat capacity of foods The specific heat of foods depends on their composition, specifically the water content (Kaletunç 2007). Table 2.4 gives an overview of the specific heat of selected foods above and below freezing (www. engineeringtoolbox.com). Heating Mode Microcalorimetry is used under the various heating modes are described next. Scanning calorimetry The scanning mode (heating or cooling) is the usual method that applies to the standard DSC technique. A microcalorimeter also can

36

Calorimetry in Food Processing

Table 2.4. Heat capacity data for some foodstuffs. Food Category

Type

Fruit

Apple Grapefruit Orange juice Cabbage Potato Pork (bacon) Pork (ham) Salmon Carp Butter Cream Milk (cow) Milk (coconut) Ice cream

Vegetable Meat Fish Dairy

Cp before Freezing (J g−1°C−11)

Cp above Freezing (J g−11°C−11)

1.76 1.84 1.8 1.88 1.72 1.05 1.42 1.55 1.72 1 1.88 1.97 1.76 1.67

3.64 3.81 3.73 3.94 3.43 1.51 2.6 2.97 3.43 1.26 3.77 3.77 3.98 3.1

be used as a DSC, but with low or very low scanning rates (less than 2 °C.min−1). Longer time of experimentation may be considered to be a disadvantage, but it provides a better resolution of different thermal processes. Melting and crystallization Physical state transformations (crystallization, melting, polymorphism) in fat samples are associated with thermal effects that are easily measured by DSC. Microcalorimetry used in the scanning mode allows improvement of the resolution of different effects because of low scanning rate, especially for characterization of emulsions (Relkin and Sourdet 2005). Denaturation and aggregation Proteins are the food components most studied by the microcalorimetric technique and include studies of conformation changes of food proteins (animal, vegetable, plant), food enzymes and enzyme preparations for the food industry, as well as effects of various additives on their thermal properties. The denaturation and aggregation processes in thermal gelation of whey proteins were resolved with the microcalorimetric technique (Fitzsimons et al. 2007). Numerous previous studies of the thermal gelation of whey proteins, carried out on conventional (fast-scanning)

Methods and Applications of Microcalorimetry in Food

37

3.2 3.1

90

8

100

3.0 Heat flow (mW)

85

2.9 2.8 2.7 2.6 2.5 2.4 40

50

60 70 80 Temperature (°C)

90

100

Figure 2.11. DSC heating scans (1.0°C/min) of 3.0 wt.% WPI pH 7.0) in the presence of NaCl at the different concentrations (80, 85, 90, and 100 mM NaCl). From Fitzsimons et al. (2007).

DSC calorimeters (typical sample mass 15–50 mg), have shown only endothermic transitions. Slow transfer of heat into a large vessel (850 mg of sample) allows the exothermic heat flow from the slow aggregation process to keep pace with the endothermic heat flow from the more rapid denaturation process and give a detectable exotherm (Figure 2.11). The same resolution effect with a slow scanning rate has also been noticed on pea storage protein, vicilin (Bacon et al. 1989), on bovine serum albumin (Barone et al. 1992, 1995), and on ovalbumin (Hagolle et al. 1997; Relkin 2004). Gelation Microcalorimetry is applied to investigation of gels formed by biopolymers, such as carrageenan (Williams et al. 1991a, 1992), xanthan (Williams et al. 1991a, b), gellan (Miyoshi et al. 1995; Robinson et al. 1991), agar (Cooke et al. 1996), pectin, and gelatin. Polysaccharides are widely used for their gelling and thickening properties in the food industry. In presence of a cation (for example, potassium K+), a solution of kappa-carrageenan gives an aggregate structure during heating. The temperature of transformation and the reversibility of the reaction

38

Calorimetry in Food Processing

(melting/gelation) can be obtained from the calorimetry data. Furthermore, detection of the transition depends not only on the polysaccharide concentration but also on the product type. For xanthan and gellan, the energy associated with the transition is very weak, and the high sensitivity of the microcalorimeter is needed. Gelatinization and retrogradation Microcalorimetry is used to characterize the gelatinization behavior of starches and interaction of starch with other food components, as well as phase transitions during baking processes (Eliasson 2003). Calorimetry in the scanning mode is used not only to study the order-disorder behavior of starch during gelatinization but also to study the recrystallization (retrogradation) during storage (Berland et al. 2003). Crystallization can also be investigated in the isothermal mode. A special calorimetric vessel has been designed to investigate the starch gelatinization during cooking of pasta (Riva et al. 1991). Isothermal calorimetry Isothermal calorimetry is commonly used to simulate a process that occurs at a constant temperature or to check the storage stability of a food component (Schäffer and Lorinczy 2005). When reactions and transitions take place within a food system, the kinetic parameters of reactions and transitions are obtained from analysis of isothermal calorimetric curves (Riva and Schiraldi 1993). Shelf life Shelf life of foods (Franzetti et al. 1995; Riva et al. 1997, 1998, 2001) is investigated using isothermal calorimetry by continuously monitoring the kinetics of microbial growth or enzymatic activity in fresh foods, such as whole eggs, fresh milk, and fresh carrots (Figure 2.12), or growth of bacteria in milk (Berridge et al. 1974). There are studies in the literature reporting the evaluation of bacteriological quality of seafood (Gram 1992), characterization of the thermal consequences of irradiation of bacteria (Mohácsi-Farkas et al. 1994), and microbial degradation (Teeling and Cypionka 1997; Andlid et al. 1999) by using isothermal calorimetry. Oxidative stability Thermal oxidative decomposition of edible oils examined by calorimetry can be used for predicting oil stability under normal or high pressure of oxygen.

Methods and Applications of Microcalorimetry in Food

39

0.14 0.1

A-PASTEURIZED WHOLE EGG

T=21.7°C exo

HF / mW g–1

0.12 0.08

T=14.7°C

0.06 0.04

T=9.6°C

0.02 0 0

2

8

4 6 Time / days

10

0.12

0.08

T=24.6°C

B-PASTEURIZED WHOLE MILK

exo

HF / mW g–1

0.1

0.06 T=14.7°C

T=19.6°C

0.04 0.02 0 0

1

2

3

4

5

6

7

8

Time / days 0.14 0.1 0.08

C-FRESH CARROTS

T=24.6°C exo

HF / mW g–1

0.12

T=19.6°C

0.06

T=14.6°C

0.04 0.02 0 0

0.5

1

1.5

2

2.5

3

Time / days

Figure 2.12. Isothermal traces at different temperatures for pasteurized whole egg, pasteurized whole milk, and fresh carrots. From Franzetti et al. (1995).

Isothermal crystallization The investigation of crystallization in the isothermal mode requires a high stability of the baseline of the microcalorimeter combined with a high sensitivity because such a test may last many hours. This type of experimental protocol is applied to isothermal crystallization of proteins, isothermal crystallization of fats, or isothermal retrogradation of starch.

40

Calorimetry in Food Processing heat flow / mW g–1

0 0.10

5

10

15

20

25

30

35

40

0.08 0.06 0.04 0.02 0.00 40 30 T / °C

20 10 0

5

10

15

20 25 time / hours

30

35

0 40

Figure 2.13. Stepwise heating thermogram (from 0 to 40 °C) of 4.5% gelatine (LH1e) in aqueous 0.1 M NaCl. From Cuppo et al. (2001).

Step heating in calorimetry Step heating (or cooling) calorimetry is a technique that is between the two previously described modes. A small variation of temperature is applied to the sample by step. After each step, the sample is maintained at a constant temperature for a certain period of time. The relevance of the stepwise methods resides primarily in the ability to follow step by step the differential structural changes as a function of the temperature. The technique was applied to follow the kinetics of the gelation of gelatine (Cuppo et al. 2001) (Figure 2.13). Mixing and Reaction Calorimetry For investigation of mixing and reaction processes in foods, the microcalorimetry has major advantages over the DSC. As described previously, the larger capacity of the calorimetric chamber allows the design of specific mixing vessels. Liquid-liquid or solid-liquid interactions are evaluated by mixing obtained by stirring or ampoule breaking. Mixing can be performed by two different modes: 1. Batch mixing: The two components A and B are brought into contact in the mixing vessel. The heat of mixing corresponds to a given concentration of A or B.

Methods and Applications of Microcalorimetry in Food

41

2. Flow mixing: The two components A and B at a given flow rate are pumped and mixed in the vessel. The concentration of the mixture can be adjusted by modifying the flow rate of A or B. Dissolution, solubility In food industry, solid-liquid and liquid-liquid interactions are often encountered, such as dissolution of powder (sugar, salt) and solubility of proteins, lipids, and fibers. For such studies, the batch-mixing vessel is ideal because it provides information relevant to the start of the reaction and the corresponding kinetics. Neutralization The batch-mixing vessel is also convenient for the investigation of any reaction occurring during a food process, such as neutralization of edible oils by soda. Raw edible oils contain free fatty acids that have to be neutralized before being used. The amount of soda necessary for neutralization has to be adjusted based on the acidity of the oil. The simulation of the operation was performed on a microcalorimeter using edible oil with variable acidities (Figure 2.14). Binding A mixing calorimeter is useful to investigate the impact of the weak nonspecific physical interactions of the food biopolymers (proteins, heat flow

4 mW exo 1

1 rape seed 8,8 %

2

2 peanut

3%

3 peanut

0,85 %

3

time (mn) 0

5

10

15

20

25

30

Figure 2.14. Neutralization of free acidity in edible oil by soda (C80).

42

Calorimetry in Food Processing HEAT FLOW

50μW

0.5

1.0

1.5

2.0

2.5 TIME (h)

Figure 2.15. Enzymatic reaction (maltose + glucoamylase) at 33 °C in batch mode (MicroDSCIII).

polysaccharides) with each other and with the major low-molecularweight ingredients of the multicomponent food colloids (sugars, mineral salts, small-molecule surfactants). The structure formation in the bulk aqueous phase and at the interfaces of colloidal systems, as well as the functional properties, depend on these weak interactions (Semenova 2007). Enzymatic reactions The example for the enzymatic reaction of transformation of maltose using glucoamylase illustrates the two modes of mixing. In batch mode, 30 mg of maltose powder is mixed with 20 μl of glucoamylase. The corresponding exothermic effect indicates the transformation of maltose for a given concentration of enzyme (Figure 2.15). In flow mode, maltose (1% in H2O) is circulated in both tubes of the mixing vessel to establish the baseline of the test. Then, glucoamylase (1% in H2O) is introduced. A corresponding exothermic deviation measures the efficiency of the enzyme for maltose transformation at a given temperature and concentration. When the enzyme flow is replaced by maltose, the calorimetric signal is returned to the baseline (Figure 2.16). This mixing mode is flexible because various enzyme concentrations can be tested consecutively.

Methods and Applications of Microcalorimetry in Food

43

HEAT FLOW

20μW

maltose + maltose

enzyme + maltose 0

maltose + maltose 10

20

30

Time (min)

Figure 2.16. Enzymatic reaction (maltose + glucoamylase) at 25 °C in flow mode (0.3 ml.min−1, MicroDSCIII).

Fermentation, bacterial growth Isothermal microcalorimetry provides informative data regarding microbial growth and microbial metabolism involving yeast and bacteria. Following are some examples of applications of isothermal microcalorimetry: • • • • • •

Dosage of antibiotics or detection of antibiotics in milk Control of alcoholic fermentation Control of panification Control of production of dairy products (lactic acid bacteria) Control of biomass reaction Control of bacterial growth

Microcalorimetry was used to investigate the growth of probiotic cultures (Schäffer, Szakaly, and Lorinczy 2004; Schäffer and Lorinczy 2005). The process of yogurt production also was simulated by mixing yogurt containing lactic acid bacteria with milk at 37 °C. The exothermic effect is associated with the bacterial growth and the characteristics of the final product, according to the temperature, the pH, and the quality of the milk. Pressure Calorimetry High-pressure processing is an important application for food research. It is important to understand the pressure-dependent phase change phe-

44

Calorimetry in Food Processing

nomena in food to improve and develop new technologies. High hydrostatic pressure can cause denaturation of proteins, solidification of lipids, inactivation of microorganisms, and destabilization of biomembranes. The calorimetric technique is used to investigate the transformations induced by pressure. Because a commercial high-pressure calorimeter is not available (see Chapter 3) for high hydrostatic pressure applications, the sample is processed in a specific pressure device, outside the calorimeter. This application was used to investigate the protein denaturation in egg white after high-pressure processing (Andrassy et al. 2006) to detect the structural changes in milk protein beta-lactoglobulin induced by combined effects of pressure and temperature (Tedford and Schaschke 2000; Kolakowski et al. 2001). High-pressure induced modifications of soy protein in soy milk, studied using microcalorimetry, showed that denaturation of b-conglycinin and glycinin occurred at 300 MPa and 400 MPa, respectively, as judged by the absence of endothermic peaks in thermograms of pressure-treated samples (Zhanga et al. 2005). Microcalorimetry in the scanning mode also was used to evaluate the relative high hydrostatic pressure resistance of bacterial strains from Staphylococcus aureus (Figure 2.17) and Escherichia coli in

B

A Heat Flow 0.5 mW

20

40

60 80 Temperature (°C)

100

120

Figure 2.17. Calorimetric traces of untreated control (A) and pressure-treated (345 MPa, 35 °C, 10 min) (B) Staphylococcus aureus. From Alpas et al. (2003).

Methods and Applications of Microcalorimetry in Food

45

vivo. The total apparent enthalpy change and thermal stability were two calorimetric parameters used to compare bacterial strains of untreated control and pressure-treated bacteria (Alpas et al. 2003). Pressure can be applied on the sample inside the calorimetric vessel. A maximum gas pressure of 100 MPa was reported by Le Parlouër et al. (2004) to be used for investigation of polymorphic changes in fatty compounds, the modification of the glass transition temperature for food with amorphous phase (sugar, starch, dough, frozen foods), or oxidative stability of oils. Supercritical extraction process using CO2 as a fluid was simulated in a high-pressure calorimeter with an adapted vessel (Stassi and Schiraldi 1994). Such a setup allows investigation either at constant pressure and at variable supercritical CO2 flow rate, or as batch system with a variable pressure. The latter allows the monitoring of the solubility-pressure relationship. Calorimetry under Controlled Relative Humidity Many food processes (extrusion, baking, drying, milling) may generate amorphous compounds. Their stability upon storage, especially in a humid atmosphere, has to be controlled. Water induces plasticization and leads to depression of the glass transition temperature, causing significant changes in the physicochemical and crystallization properties of the food components containing an amorphous phase. Scanning calorimetry is recognized as an efficient technique for measurement of the glass transition temperature of hydrated products (Bizot et al. 1997; Borde et al. 2002). Scanning calorimetry was also used to monitor crystallization of lactose in humidified powders, because lactose crystallisation and Maillard reaction are two major modifications occurring in milk and whey powders during processing and storage (Morgan et al. 2005). Typically, samples are equilibrated outside the calorimeter, and the influence of water content or water activity are investigated using calorimetry. However, the sample can be prepared with a defined water content, and thermal properties can be measured inside a special vessel by combining DSC and humidity generator (Le Parlouër and Mathonat 2003). Conclusion As in other industrial domains, the food industry is more and more interested in applying new techniques of investigations to improve the

46

Calorimetry in Food Processing

products, to enhance quality and safety, to find new ingredients, and to better understand the different processes involved in the food production. Although microcalorimetry is not a new technique, the broad range of applications for calorimetry is not well known in the area of food technology. As described in this chapter, the possibilities of using different types of calorimeters are unlimited and can be applied to a large variety of foods. Because heat is involved in most food transformations during processing and storage, calorimetry will provide answers to food technologists in their daily research.

References Alpas H., Lee J., Bozoglu F., and Kaletunc G. 2003. Evaluation of high hydrostatic pressure sensitivity of Staphylococcus aureus and Escherichia coli O157:H7 by DSC. Int J Food Microbiol, 87(3):229–237. Andlid T., Blomberg L., Gustafsson L., and Blomberg A. 1999. Characterization of Saccharomyces cerevisiae CBS 7764 isolated from rainbow trout intestine. Syst Appl Microbiol, 22(1):145–155. Andrassy E., Farkas J., Seregely Z., Dalmadi I., Tuboly E., and Lebovics V. 2006. Changes of hen eggs and their components caused by non-pasteurizing treatments. II. Some non-microbiological effects of gamma irradiation or hydrostatic pressure processing on liquid egg white and egg yolk. Acta Alimentaria, 35(3):305–318. Bacon J.R., Noel T.R., and Wright D.J. 1989. Studies on the thermal behaviour of pea (Pisum sativum) vicilin. J Sci Food Agri, 49:335–345. Barone G., Capasso S., Del Vecchio P., De Sena C., Giancola C., Graziano G. 1995. Thermal denaturation of bovine serum albumin and its oligomers and derivatives PH dependence. J Thermal Anal, 45:1255–1264. Barone G., Giancola C., and Verdoliva A. 1992. DSC studies on the denaturation and aggregation of serum albumins. Thermochim Acta, 199:197–205. Berland S., Relkin P., and Launay B. 2003. Calorimetric and rheological properties of wheat flour suspensions and doughs. Effects of wheat types and milling procedure. J Thermal Anal Calorim, 71:311–320. Berridge N.J., Cousins C.M., and Cliffe A.J. 1974. Microcalorimetry applied to certain species of bacteria growing in sterilized separated milk. J Dairy Res, 41:203. Bizot H., Le Bail P., Leroux B., Davy J., Roger P., and Buleon A. 1997. Calorimetric evaluation of the glass transition in hydrated, linear and branched polyanhydroglucose compounds. Carbohydrate Polymers, 32:33–50. Borde B., Bizot H., Vigier G., and Buleon A. 2002. Calorimetric analysis of the structural relaxation in partially hydrated amorphous polysaccharides. II. Phenomenological study of physical aging. Carbohydrate Polymers, 48(1):83–96.

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Calvet E. and Prat H. 1964. Recent Progress in Microcalorimetry, H.A. Skinner, editor. MacMillan: London. Cerdeirina C.A., Miguez J.A., Carballo E., Tovar C.A., de la Puente E., and Romani L. 2000. Highly precise determination of the heat capacity of liquids by DSC: Calibration and measurement. Thermochim Acta, 347:37–44. Cooke D., Gidley M.J., and Hedges N.D. 1996. Thermal properties of polysaccharides at low moisture. J Thermal Anal, 47:1485–1498. Craig D.Q.M. and Reading M. 2007. Thermal Analysis of Pharmaceuticals. CRC Press, Taylor & Francis Group: Boca Raton, FL. Cuppo F., Venuti M., and Cesaro A. 2001. Kinetics of gelatin transitions with phase separation: T-jump and step-wise DSC study. Int J Biol Macromol, 28: 331–341. Daudon J.L. 1996. Heat Flux Devices and Methods for Optimum Specific Heat Measurements. 14th European Conference on Thermophysical Properties, September 16–19, Lyon, France. Eliasson A.C. 2003. Utilization of thermal properties for understanding baking and staling processes. In: Characterization of Cereal and Flours, Kaletunç G. and Breslauer K.J., editors. Marcel Dekker, Inc: New York. Engineering Toolbox website: www.engineeringtoolbox.com Farkas J. and Mohácsi-Farkas C. 1996. Application of DSC in food research and food quality. J Thermal Anal, 47:1787–1803. Fitzsimons M.S., Mulvihill M.D., and Morris E.R. 2007. Denaturation and aggregation processes in thermal gelation of whey proteins resolved by DSC. Food Hydrocolloids, 21:638–644. Franzetti L., Galli A., Perazzoli A., and Riva M. 1995. Calorimetric investigations on microbial acetic-acid production. Ann Microbiol Enzimol, 45:291. Gram L. 1992. Evaluation of the bacteriological quality of seafood. Int J Food Microbiol, 16:25. Hagolle N., Relkin P., Dalgliesh D.G., and Launay B. 1997. Transition temperatures of heat-induced structural changes in ovalbumin solutions at acid and neutral pH. Food Hydrocolloids, 11:311–317. Harwalkar V.R. and Ma C.Y. 1990. Thermal Analysis of Foods. Elsevier Applied Science: London. International Confederation for Thermal Analysis and Calorimetry website: www. ictac.org Kaletunç, G. 2007. Prediction of heat capacity of cereal flours: A quantitative empirical correlation. J Food Eng, 82(2):589–594. Kolakowski P., Dumay E., and Cheftel J.C. 2001. Effects of high pressure and low temperature on (-lactoglobulin unfolding and aggregation. Food Hydrocolloids, 15:215–232. Ladbury J.E. and Chowdhry B.Z., editors. 2004. Biocalorimetry 2: Applications of Calorimetry in the Biological Sciences. Wiley: London. Le Parlouër P. and Mathonat C. 2003. WETSYS: An Automated Relative Humidity Device for the Calvet Calorimeters. 31st NATAS Proceedings, Albuquerque, NM.

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Le Parlouër P. and Mathonat C. 2005. SENSYS: An Innovative Concept for the Calvet DSC111 and TG-DSC111. 33rd NATAS Proceedings, p.44, Universal City, CA. Le Parlouër P., Dalmazzone C., Herzhaft B., Rousseau L., and Mathonat C. 2004. Characterization of gas hydrates formation using a new high pressure Micro-DSC. J Thermal Anal Calorim, 78:165–172. MicroCal, LLC website: www.microcal.com Miyoshi E., Takaya T., and Nishinari K. 1995. Effects of salts on the gel-sol transition of gellan gum by differential scanning calorimetry and thermal scanning rheology. Thermochim Acta, 267:269–287. Mohácsi-Farkas C., Farkas J., and Simon A. 1994. Thermal denaturation of bacterial cells examined by DSC. Acta Alimentaria, 23:157. Morgan F., Nouzille C.A., Baechler R., Vuataz G., Raemy A. 2005. Lactose crystallisation and early Maillard reaction in skim milk powder and whey protein concentrates. Le Lait, 85:315–323. Raemy A., Lambelet P., and Garti N. 2000. Thermal behavior of foods and food constituents. In: Thermal Behavior of Dispersed Systems, Garti N., editor. Marcel Dekker Inc: New York. Relkin P. 2004. Using DSC for monitoring protein conformation stability and effects on fat droplets crystallinity in complex food emulsions. In: The Nature of Biological systems as Revealed by Thermal Methods, Lorinczy D., editor. Kluwer Academic Publishers: London. Relkin P. and Sourdet S. 2005. Factors affecting fat droplet aggregation in whipped frozen protein-stabilized emulsions. Food Hydrocolloids, 19:503–511. Richardson M.J. and Charsley E.L. 1998. Calibration and standardisation in DSC. In: Handbook of Thermal Analysis and Calorimetry, Vol. 1: Principles and practice, Brown M. E., editor. Elsevier Science BV: The Netherlands. Riva C., Piazza L., and Schiraldi A. 1991. Starch gelatinization in pasta cooking: Differential flux calorimetry investigations. Cereal Chem, 68:622–627. Riva M. and Schiraldi A. 1993. Kinetic parameterization of transitions and reactions in food systems from isothermal and nonisothermal DSC traces. Thermochim Acta, 220:117. Riva M., Fessas D., and Schiraldi A. 2001. Isothermal calorimetry approach to evaluate shelf life of foods. Thermochim Acta, 370:73. Riva M., Fessas D., Franzetti L., and Schiraldi A. 1998. Calorimetric characterization of different yeast strains in doughs. J Thermal Anal Calorim, 52:753. Riva M., Franzetti L., Galli A., and Schiraldi A. 1997. Growth and fermentation activity of Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus in milk: A calorimetric investigation. Ann Microbiol Enzymol, 47:199. Robinson G., Manning C.E., and Morris E.R. 1991. Conformation and physical properties of the bacterial polysaccharides: Gellan, welan and rhamsan. In: Food Polymers Gels & Colloids, Dickinson E., editor. Woodhead Publishing, Cambridge, UK.

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Schäffer B. and Lorinczy D. 2005. Isoperibol calorimetry as a tool to evaluate the impact of the ratio of exopolysaccharide producing microbes on the properties of sour cream. J Thermal Anal Calorim, 82:537–541. Schäffer B., Szakaly S., and Lorinczy D. 2004. Examination of the growth of probiotic culture combinations by the isoperibolic batch calorimetry. Thermochim Acta, 415:123–126. Schiraldi A., Piazza L., Fessas D., and Riva M. 1999. Thermal analysis in foods and foods processes. In: Handbook of Thermal Analysis and Calorimetry, Vol. 4, Kemp R.B., editor. Elsevier Science BV: The Netherlands. Semenova M.G. 2007. Thermodynamic analysis of the impact of molecular interactions on the functionality of food biopolymers in solution and in colloidal systems. Food Hydrocolloids, 21(1):23–45. Setaram Instrumentation website: www.setaram.com Stassi A. and Schiraldi A. 1994. Solubility of vegetable cuticular waxes in supercritical CO2 isothermal calorimetry investigation. Thermochim Acta, 246(2): 417–425. TA Instruments website: www.tainstruments.com Tedford L.A. and Schaschke C.J. 2000. Induced structural change to β-lactoglobulin by combined pressure and temperature. Biochem Eng J, 5(1):73–76. Teeling H. and Cypionka H. 1997. Microbial degradation of tetraethyl lead in soil monitored by microcalorimetry. Appl Microbiol Biotechnol, 48:275. Williams P.A., Clegg S.M., Day D.H., and Phillips G.O. 1991a. In: Food Polymers, gels and colloids, Dickinson E., editor. Woodhead Publishing, Cambridge, UK. Williams P.A., Clegg S.M., Langdon M.J., Nishinari K., and Phillips G.O. 1992. Gums and Stabilisers for the Food Industry 6, Williams P.A, editor. Oxford University Press: UK. Williams P.A., Day D.H., Langdon M.J., Phillips G.O., and Nishinari K. 1991b. Synergistic interaction of xanthan gum with glucomannans and galactomannans. Food Hydrocolloids, 4:489–493. Zhanga H., Lib L., Tatsumic E., and Isobe S. 2005. High pressure treatment effects on proteins in soy milk. Lebensmittel-Wissenschaft und-Technologie, 38:7–14.

Chapter 3 High-Pressure Differential Scanning Calorimetry Günther W.H. Höhne and Gönül Kaletunç

Introduction Construction of the High-Pressure DSC Calibration of the High-Pressure DSC Temperature Calibration Procedure Heat Calibration Procedure Applications of the High-Pressure DSC Conclusion References

51 53 57 58 61 63 63 64

Introduction Pressure is an essential variable in physical chemistry. Measurements at different pressures are therefore of great importance from the thermodynamic perspective. The change of pressure provides increased insight into the thermodynamic behavior of materials. The wider the pressure region, the better description of material response to pressure is obtained, which enables one to develop predictive capability. Higher pressure is often used during production and processing of materials, and the change of properties that occurs with pressure is therefore of great interest for optimization of processing conditions. In particular, the latent heat of reactions, phase and conformational transitions, and their pressure dependence are valuable information for quantitative 51

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Calorimetry in Food Processing

analysis of systems under study both in basic research and in industrial processing. There is an increasing interest in application of high pressure for food processing and preservation. Although the main goal of this application is to produce microbiologically safe food, high pressure affects the high-molecular-weight components of a food product, causing conformational and phase changes. Proteins and starches constitute a large percentage of many food products. The phase or conformational changes in such compounds may cause appearance or textural changes that might affect the quality of the final food products; therefore, the effects of high-pressure processing on food products and their individual components need to be determined. Calorimetry is well-suited for study of food materials because food processing involves either heating or cooling of materials, which can be simulated in a calorimeter so that data can be directly related to the process protocols (Miles, Mackey, and Parsons 1986; Mackey et al. 1991; Lee and Kaletunç 2002a, 2000b). If the food products are processed by other means, such as using high pressure or chemicals, temperature-scanning calorimetry may used to compare thermograms of food product before and after exposure to treatment to evaluate its effect (Niven, Miles, and Mackey 1999; Alpas et al. 2003; Kaletunç et al. 2004; Lee and Kaletunç 2005). However, this approach provides information about only irreversible changes that occur in the food product. Therefore, there is great interest in high-pressure calorimetry to characterize the changes in a sample and to determine the thermal properties, such as heat capacity, under the conditions relevant to highpressure processing. Some commercially available calorimeters that operate at rather moderate pressures of up to 100 MPa (1000 bar) exist. Unfortunately, there is no truly high-pressure calorimeter on the market (i.e., working at pressures well above 100 MPa). One major limitation is the need for a fluid medium (gas or liquid) to transfer the pressure to the sample, and any liquid or highly compressed gas has a rather large thermal conductivity (compared with a gas at ambient pressure). The heat flows through the pressure medium rather than through the thermal pathway constructed inside the calorimeter to measure the heat flow. Consequently, a large thermal leakage occurs, leading to much reduced calorimeter sensitivity. These problems and the fact that high pressures up to 500 MPa (5 kbar) or even more are not easy to handle due to the need for special

High-Pressure Differential Scanning Calorimetry

53

equipment and their potential danger have impeded the development of a commercially available high-pressure calorimeter. Currently, researchers in the field construct their own equipment (see Chapter 13). Although high-pressure differential thermal analysis (DTA), a nonquantitative caloric method, exists in several laboratories worldwide at pressures up to 1 GPa (e.g., see Szabó et al. 1969; Shulgin and Godovsky 1992; Schmidt et al. 1994; Nakafuku and Sugiuchi 1996), the number of high-pressure calorimeters remains small. The most widely used calorimeter type is the differential scanning calorimeter (DSC; see Chapter 1), and some high-pressure differential scanning calorimeters (HP-DSC) have been constructed during the last three decades. Different research groups have approached the several problems of HP-DSC in different ways (Schmidt et al. 1994; Arntz 1980; Kamphausen 1975; Sandrock 1982a; Eichler and Gey 1979; Mellander, Baranowski, and Lundén 1981; Randzio 1983; Schneider 1985; Zhu et al. 2004a). To our knowledge, only one power-compensated DSC (based on the PerkinElmer DSC-7, PerkinElmer, Waltham, Massachusetts) exists, and it works up to a pressure of 500 MPa (5 kbar) (Blankenhorn and Höhne 1991). Höhne and co-workers (Blankenhorn and Höhne 1991; Ledru et al. 2006) modified a commercial powercompensated DSC (PerkinElmer DSC 7) by building a new highpressure measuring head, rather than building a completely new high-pressure DSC. In this chapter, we focus on the construction of this calorimeter because it was demonstrated that it can generate calorimetric data at high pressures successfully (Höhne and Blankenhorn 1994; Höhne, Schawe, and Shulgin 1997; Höhne 1998; Rastogi, Höhne, and Keller 1999; Höhne, Rastogi, and Wunderlich 2000; Ingram et al. 2008), and it is possible to build similar ones without significant difficulty (Ledru et al. 2006).

Construction of the High-Pressure DSC The HP-DSC presented here operates on the power compensation principle, making use of a commercial DSC (PerkinElmer). The original measuring head was replaced with a head built by Höhne and co-workers (1991) with the same electrical and sensor properties but positioned inside an autoclave that can be pressurized by means of a hand-operated spindle pump (Figure 3.1).

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Calorimetry in Food Processing

Figure 3.1. High-pressure DSC setup. (a) DSC; (b) spindle pump; (c) autoclave (Ledru et al. 2006 in accordance with Blankenhorn and Höhne 1991).

High-pressure handling is a dangerous task. To illustrate, note that a pressure of 500 MPa (5 kbar) is nearly two times the pressure inside a gun being shot. If a part of the autoclave fails during an experiment under high pressure, the effect may potentially be worse than that from a bullet. Consequently, there are high safety demands on high-pressure experiments. Usually, an autoclave driven with gas as the pressure medium must be operated in a separate high-pressure shelter room, and the operator must be outside in a safe place. To avoid such trouble, we gave preference to silicone oil as the pressurizing medium. Using a liquid pressure medium (usually oil) for high-pressure experiments is still rather dangerous, and proper steps to avoid accidents have to be taken, but the measures are by far less expensive than that used with highly compressed gas. Because of these safety reasons, the complete high-pressure system, including the autoclave (Figure 3.2), the spindle pump, high-pressure lines, valves, and transducers, were provided by a high-pressure specialist (SITEC-Sieber Engineering AG, Switzerland). The HP-DSC head consists of two silver furnaces constructed by the research group and is located within ceramic housings (Figure 3.3). To avoid greater heat loss, the size was chosen in order to fit closely within the autoclave but without direct solid-to-solid contact. The furnaces (Figure 3.4)

Figure 3.2. High-pressure DSC: Autoclave unit (Ledru 2006 in accordance with Blankenhorn and Höhne 1991).

Figure 3.3. High-pressure DSC: Ceramic housing with the silver furnace inside (Ledru et al. 2006 in accordance with Blankenhorn and Höhne 1991).

55

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Calorimetry in Food Processing

Figure 3.4. High-pressure DSC: Silver furnace with sample pan inside (Ledru et al. 2006 in accordance with Blankenhorn and Höhne 1991).

were electrically isolated with special ceramic glue. They were provided with platinum wire windings for both the heaters and sensors to match the resistance of the original DSC cell and with leads through the autoclave closing caps to the calorimeter control system. The purpose of locating the two furnaces within the ceramic housings is to isolate them, preventing cross talk between the sample and reference and minimizing the disturbance of the heat flow signal caused by convection currents in the oil. These housings contain holes for oil entry and escape during the filling/emptying and pressure change process, and they have screw caps (Figure 3.3) to allow their volume to be adjusted in order to balance the two furnaces to obtain a flat baseline. Using a branched silicone oil of approximately 100 mPa/s (Wacker AS 100, Wacker-Chemie GmbH, Germany), the HP-DSC may be operated in a temperature range from 20 °C to 300 °C at pressures from ambient to 500 MPa and with various heating and cooling rates (from 0.5 K.min−1 to 20 K.min−1). The actual pressure value within the autoclave is measured using a pressure transducer close to the reference cell, which can be connected to a data logger for pressure recording. The sample must, of course, be encapsulated to avoid any contact with the pressurizing medium. This is not an easy matter, as the encap-

High-Pressure Differential Scanning Calorimetry

57

sulation must be oil-tight on the one hand and free from air bubbles and empty space on the other. The latter would lead to large deformation of the sample container when the pressure rises; thus, the container possibly would not remain oil-tight and the measurement would be faulty. There are several possible ways to solve the problem. One is to prepare the sample to fit exactly between two aluminum crucibles that then are welded together with a proper press. Another possibility is to put the samples into crucibles of a plastic metal such as indium or lead and close the crucible hermetically. In operation, the furnaces are placed within the autoclave with their axes horizontal. The autoclave has a lid (Figure 3.4) that screws down onto the crucible, ensuring that it is firmly located within the silver furnace and that good thermal contact is made. The HP-DSC constructed this way had the following properties: pressure and temperature ranges from 0.1 to 500 MPa and from ambient to 600 K (330 °C), respectively, and thermal noise 50–100 μW peak to peak (much larger than in normal DSCs because of the oil convection). The detection limit for transitions (peak area) is about 5 mJ (i.e., 1 J g−1). Compared to common DSCs, the baseline repeatability of the HP-DSC is poor (2–3 mW) because of unavoidable small differences in oil volume between the sample and reference cell when a new sample is remounted. This makes it impossible to determine heat capacities of a sample with the usual method, which is to subtract an empty pan run from the sample run. The change in baseline after remounting a new sample is often larger than the expected difference of the heat flow rate between the two runs. This unavoidable effect is a serious disadvantage of the high-pressure DSC.

Calibration of the High-Pressure DSC To get precise and reliable thermodynamic data, a careful calibration of every calorimeter is necessary. For normal DSCs, this includes both temperature and heat calibration or heat flow rate calibration using standard procedures (Höhne, Hemminger, and Flammersheim 2003) and certified reference substances with well-known temperature and heat of transition values. Based on calibration procedure, a function or table of corrections is generated to obtain the true temperature and the true heat of transition from the measured quantities. As a rule, the

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correction depends on temperature but may also be influenced by other parameters, such as sample mass, pan type, and heating rate (for details, see the textbook by Höhne, Hemminger, and Flammersheim [2003]). Various reference substances with different transition temperatures are needed for a complete calibration. For the HP-DSC, however, the change of the calibration with the change of pressure must be taken into consideration, too. Sensitivity of every thermometer and every heat flow rate sensor is affected by pressure. This is particularly important for very high pressure levels used in the HP-DSC. Consequently, the resulting correction function for the HP-DSC always depends on at least two parameters, namely, temperature and pressure. Unfortunately, there is no existing certified data for the influence of pressure on the temperature and heat of transition for the recommended calibration reference substances. Among common reference substances such as indium, tin, lead, and zinc, only for indium is there reliable information in literature (Höhne et al. 1996) regarding the pressure dependence of temperature and enthalpy of fusion. The respective literature for the high-pressure dependence of the melting point of tin (McDaniel, Babb, and Scott 1962; Sandrock 1982b) and lead (McDaniel, Babb, and Scott 1962) differs a little. The pressure dependence of the melting enthalpy for tin and lead has, to our knowledge, not been reported; therefore, we assume that the melting enthalpy of tin and lead is almost pressure independent, similar to that of indium for the calibration of HP-DSC. As a result, the calibration of the HP-DSC cannot be as precise as the calibration of normal DSCs. Consequently, the uncertainty of HP-DSC enthalpy measurements must be considered much higher than the uncertainty of common DSC measurements at ambient pressure. Temperature Calibration Procedure The measured temperature Tmeas has to be corrected (Tcorr) by adding a correction term based on temperature and pressure dependence: Tcorr ( p, T ) = Tmeas ( p, T ) + ΔTcorr ( p, T )

(3.1)

Normally, the correction ΔTcorr depends on the heating rate, too. This influence is, however, of minor importance if the same heating rate is always used for all measurements.

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Calibration means the determination of the ΔTcorr(p,T) function (for a certain heating rate) either as a best fit function or, more often, in form of a table or array. The respective calibration procedure compares the measured temperature of transition at different temperatures and pressures with the true value of certified reference substances. To check possible nonlinear dependences, at least three different transitions must be measured at more than three different pressures. Indium is the only reference substance for which the pressure dependence of the melting point is well known (Höhne et al. 1996): In In Tfus K ] + [( 0.0507 ± 0.003) K MPa −1 ]⋅[ p MPa ] ( p ) K = [Tfus,0

(3.2)

In = 429.7485 K is the fixed melting point of the ITS-90 for where Tfus,0 indium at normal pressure (Preston-Thomas 1990) and p is the pressure. The given standard deviation defines the limits of this best value estimation and leads to a maximum uncertainty of 1.5 K at 500 MPa. For tin, to our knowledge two reliable publications exist for the pressure dependence of the melting point (McDaniel, Babb, and Scott 1962; Sandrock 1982b). The best value approximation for these data reads: Sn Sn Tfus K ] + [(0.0324 ± 0.0025) K MPa −1 ]⋅[ p MPa ] − ( p ) K = [Tfus,0 (3.3) [(1.45 ± 4.86)10−6 K MPa −2 ]⋅[ p MPa ]2 Sn where Tfus,0 = 505.078 K , the fixed melting point of the ITS-90 for tin at normal pressure (Preston-Thomas, 1990) and p the pressure. The standard deviations were defined from this best value estimation and, according to the literature data, lead to a maximum uncertainty of 2.5 K at 500 MPa. For the pressure dependence of the melting point of lead, to our knowledge only one publication exists (McDaniel, Babb, and Scott 1962). Any best value estimation, therefore, cannot be performed for the pressure dependence of its melting point, and the uncertainty is not known. Consequently, lead cannot be used for calibration of the HPDSC. Because we have two reference substances only, we have to restrict ourselves to a linear approximation of the temperature dependence of the correction function.

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To calibrate the temperature of the HP-DSC the first time at least three different In as well as Sn samples with a mass of 1–10 mg have to be weighted precisely (±0.01 mg) and encapsulated as described above in hermetically sealed pans. With these samples, heating and cooling runs must be performed at different pressures and typical heating rates. To detect possible differences between the reference and the sample side of the HP-DSC, a small sample of the same calibrant should also be positioned on the reference side. ΔTcorr(p,T) is defined as the reference temperature minus the measured temperature at each pressure for both indium and tin, where the reference values are defined by Equations 3.2 and 3.3 for indium and tin, respectively. This then permits the construction of a calibration diagram in which the ΔTcorr values are plotted against the measured temperature for both indium and tin for pressures from 0.1 MPa to 500 MPa, as shown in Figure 3.5. In this figure, a linear relationship between the temperature correction and the measured temperature has been assumed, as is usual for two-point calibration, to enable an extrapolation to be made over a wider temperature range. For the temperature correction, a maximum uncertainty of 1.6 K at 500 MPa has been calculated from Equation 3.2 from the indium values. For

15

ΔTcoor/K

10

5

0.1 MPa 50 MPa 100 MPa 150 MPa 200 MPa 250 MPa 300 MPa 350 MPa 400 MPa 450 MPa 500 MPa

0

–5 330

380

430

480

530

Measured temperature/K

Figure 3.5. Example of temperature correction function for high-pressure DSC (according to Ledru et al. 2006).

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61

tin values, the maximum uncertainty of the correction is larger, namely, 3.1 K at 500 MPa. This rather time-consuming procedure is absolutely necessary for a correct temperature calibration of the HP-DSC. The calibration should be verified in regular periods. This can be done in a shortened procedure with only one sample and one heating rate at two or three pressures. If the result of the respective correction remains unchanged, no further action is needed. If not, the whole calibration procedure must be repeated. As noted, a small indium sample may be placed and left in the reference cell to verify the temperature calibration “online,” and then equation 3.1, valid for the sample cell, may be rewritten for the reference cell as: Ttrue ( p ) = Tmeas ( p ) + ΔTcorr ( p, T ) + ΔTs-r

(3.4)

where ΔTcorr(p,T) is the correction obtained from the calibration procedure and ΔTs−r is an additional correction that corresponds to the difference between the reference and sample temperature sensor. This difference is often temperature and pressure independent and can be taken as a constant value compared with the overall uncertainty of the corrected temperatures, which are within the range of 1–4 K (depending on temperature and pressure) for our calorimeter.

Heat Calibration Procedure The measured heat or heat flow rate in a DSC is related to the true value as follows: Δ fus H true ( p ) = Δ fus H meas ( p )⋅ Rcorr ( p )

(3.5)

where Rcorr(p) is the calibration factor. For power-compensated DSC, the calibration factor does not depend highly on temperature, but rather it is a function of pressure. The heat calibration is performed similarly to the usual procedure for common power-compensated DSCs. An indium sample is positioned in the HP-DSC, and the melting peak is measured at different pressures. The calibration factor k(p) is then determined by comparing the measured value with the reference value at the respective pressure:

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Calorimetry in Food Processing Rcorr ( p ) =

In Δ fus H ref ( p) In Δ fus H meas ( p )

(3.6)

For the pressure dependence of the latter, the best value estimation from Preston-Thomas (1990) is used: In Δ fus H ref J g −1 = Δ fus H 0In, ref J g −1 + [(3.3 ± 2 )10 −3 g −1 MPa −1 ]⋅[ p MPa ] − [(2.6 ± 2)10−7 J g−1 MPa −2 ]⋅[ p MPa ]2

(3.7) where Δ fus H 0Inref = 28.62 ± 0.11J g −1 is taken as the best value for the heat of fusion of indium at normal pressure and p the pressure. This best value estimation includes an uncertainty of 0.1 J g−1 at ambient pressure increasing to 1.1 J g−1 at 500 MPa for the best value of the heat of fusion of indium. A best value estimation of the pressure dependence of the melting enthalpy for tin has not been reported; therefore, we did not use the melting enthalpy of tin for calibration purposes. The total uncertainty of a heat measurement with the HP-DSC is the sum of the uncertainties of the reference material and the respective measurement, which is about 0.9–2 J g−1. In Figure 3.6 Rcorr(p) resulting from such a heat calibration is given as an example.

Figure 3.6. Calibration factor in dependence on pressure for a high-pressure DSC (according to Ledru et al. 2006).

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63

Following the calibration of the HP-DSC, the measured values can be reliably corrected. The calibration must be repeated if any essential part of the high-pressure calorimeter is changed or replaced. This is especially necessary if the oil is changed, because the heat transfer conditions become different. It may be sufficient to verify the calibration from time to time, at least when a different type of sample pan and scanning rate are used. To be on the safe side, it is recommended to always have a small indium sample in the otherwise empty reference pan to verify the calibration “online” during every measurement.

Applications of the High-Pressure DSC To our knowledge, HP-DSC has rarely been applied to studying food samples to date (Zhu et al. 2004b) because there are no HP-DSCs commercially available. The few working groups that have constructed such a device (Sandrock 1982a; Blankenhorn and Höhne 1991; Ledru et al. 2006) were mainly interested in polymer science (Höhne 1999; Ledru et al. 2006) or organic chemistry (Sandrock 1982b). Another problem arises from the demand to seal the samples hermetically in such a way that any cavity is avoided. For solid materials, this is rather easy, as described above. But it seems to be impossible to seal liquids or solutions, which often are matter of interest in food science, in this way. For liquid samples, special hermetically sealed pans must be developed and filled in such a way that no cavity or bubble remains inside after closure to avoid a huge deformation of the pan when the pressure rises.

Conclusion High-pressure differential scanning calorimeters are not available commercially. They are, however, very valuable instruments for obtaining essential thermodynamic data, including food applications. If highpressure measurements are needed, one must build such an instrument oneself. This is not an easy task and needs experience. As described above, the high-pressure power compensated DSC has been built and works well. The advantages of this type of high-pressure calorimeter are as follows:

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• the well-tested construction, • well-established calibration procedures, and • compatibility with the widely used PerkinElmer DSCs and the wellknown power compensation method. The disadvantages are the following: • Sample size (and mass) is limited to 30 μl. • The samples must be hermetically sealed. • The air-free sealing of liquid samples is very difficult. Nevertheless, we believe that the high-pressure power compensated DSC can be very useful in food research applications. It should be possible to overcome its disadvantages, which will be easier than constructing a new high-pressure calorimeter better suited for demands of food science or to modify other types of high-pressure calorimeters that currently exist.

References Alpas H., Lee J., Bozoglu F., and Kaletunç G. 2003. Differential scanning calorimetry of pressure-resistant and pressure-sensitive strains of Staphylococcus aureus and Escherichia coli O157 : H7. International J Food Microbiol, 87:229–237. Arntz H. 1980. New high pressure low temperature differential scanning calorimeter. Rev Sci Instrum, 51(7):965–967. Blankenhorn K. and Höhne G.W.H. 1991. Design, specifications and application of a high pressure DSC cell. Thermochim Acta, 187:219–224. Eichler A. and Gey W. 1979. Method for the determination of the specific heat of metals at low temperatures under high pressures. Rev Sci Instrum, 50(11):1445–1452. Höhne G.W.H. 1999. High pressure differential scanning calorimetry on polymers. Thermochim Acta, 332:115–123. Höhne G.W.H. and Blankenhorn K. 1994. High pressure DSC investigations on n-alkanes, n-alkane mixtures and polyethylene. Thermochim Acta, 238: 351–370. Höhne G.W.H., Dollhopf W., Blankenhorn K., and Mayr P.U. 1996. On the pressure dependence of the heat of fusion and melting temperature of indium. Thermochim Acta, 273:17–24. Höhne G.W.H., Hemminger W., and Flammersheim H.J. 2003. Differential Scanning Calorimetry, 2nd revised and enlarged ed. Springer-Verlag: Berlin.

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Höhne G.W.H., Rastogi S., and Wunderlich B. 2000. High pressure differential scanning calorimetry of poly(4-methyl-pentene-1). Polymer, 41:8869–8878. Höhne G.W.H., Schawe J.E.K., and Shulgin A.I. 1997. The phase transition behaviour of linear polyethylenes at high pressure. Thermochim Acta, 296:1–10. Ingram M.D., Imrie C.T., Ledru J., and Hutchinson J.M. 2008. Unified approach to ion transport and structural relaxation in amorphous polymers and glasses. J Phys Chem, 112:859–866. Kaletunç G., Lee J., Alpas H., and Bozoglu F. 2004. Evaluation of structural changes induced by high hydrostatic pressure in Leuconostoc mesenteroides. Appl Environ Microbiol, 70:1116–1122. Kamphausen M. 1975. New differential scanning high pressure microcalorimeter. Rev Sci Instrum, 46(6):668–669. Ledru J., Imrie C.T., Hutchinson J.M., and Höhne G.W.H. 2006. High pressure differential scanning calorimetry: Aspects of calibration. Thermochim Acta, 446:66–72. Lee J. and Kaletunç G. 2002a. Evaluation by differential scanning calorimetry of the heat inactivation of Escherichia coli and Lactobacillus plantarum. Appl Environ Microbiol, 68:5379–5386. Lee J. and Kaletunç G. 2002b. Calorimetric determination of inactivation parameters of microorganisms. J Appl Microbiol, 93:178–189. Lee J. and Kaletunç G. 2005. Evaluation by differential scanning calorimetry of the effect of acid, ethanol, and NaCl on Escherichia coli. J Food Prot, 68: 487–493. Mackey B.M., Miles C.A., Parsons S.E., and Seymour D.A. 1991. Thermal denaturation of whole cells and cell components of Escherichia coli examined by differential scanning calorimetry. J Gen Microbiol, 137(10):2361–2374. McDaniel M.L., Babb Jr. S.E., and Scott G.J. 1962. Melting curves of five metals under high pressure. J Chem Phys, 37(4):822–828. Mellander B.E., Baranowski B., Lundé A. 1981. Transition enthalpies of silver iodide in the high-pressure region determined by DSC. Phys Rev, 23(8):3770–3773. Miles C.A., Mackey B.M., and Parsons S.E. 1986. Differential scanning calorimetry of bacteria. J Gen Microbiol, 132(4):939–952. Nakafuku C. and Sugiuchi T. 1996. Effect of pressure on the phase diagram of binary mixtures of n-alkanes. Polymer, 34(23):4945–4952. Niven G.W., Miles C.A., and Mackey B.M. 1999. The effects of hydrostatic pressure on ribosome conformation in Escherichia coli: An in vivo study using differential scanning calorimetry. Microbiol, 145:419–425. Preston-Thomas H. 1990. The international temperature scale of 1990 (ITS90). Metrologia, 27:3–10. Randzio S.L. 1983. A pressure-scanning calorimeter. J Physics E Sci Instrum, 16:691–694. Rastogi S., Höhne G.W.H., and Keller A. 1999. Unusual pressure-induced phase behavior in crystalline poly(4-methylpentene-1): Calorimetric and spectroscopic results and further implications. Macromolecules, 32:8897–8909.

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Sandrock R. 1982a. High-pressure high-temperature differential scanning calorimeter. Rev Sci Instrum, 53(7):1079–1081. Sandrock R. 1982b. Differentialkalorimetrie (DSC) bei hohen Drücken; Phasenverhalten und Umwandlungsenthalpien sowie daraus abgeleitete thermodynamische Gröen von Polyethylen und Diamantan bis 6000 bar und 600 K. Dissertation, RuhrUniversität: Bochum. Schmidt C., Rittmeier-Kettner M., Becker H., Ellert J., Krombach R., and Schneider G.M. 1994. Differential thermal analysis (DTA) and differential scanning calorimetry (DSC) at high pressures. Experimental techniques and selected results. Thermochim Acta, 238:321–336. Schneider G.M. 1985. Recent developments of microcalorimetry at high pressures. Thermochim Acta, 88:159–168. Shulgin A.I. and Godovsky Y. 1992. DTA measurements on polymers under high pressure—polyethylene and poly(diethylsiloxane). J Thermal Anal, 38: 1243–1250. Szabó J., Luft G., and Steiner R. 1969. Anwendung der Differentialthermoanalyse zu reaktioiiskinetischen Untersuchungen von Hochdruckreaktionen. Chemie Ing Technik, 41:1007–101. Zhu S., Bulut S., Le Bail A., and Ramaswamy H.S. 2004a. High pressure differential scanning calorimetry (DSC): Equipment and technique validation using water-ice phase-transition data. J Food Process Eng, 27:359–376. Zhu S., Ramaswamy H.S., and Le Bail A. 2004b. High-pressure differential scanning calorimetry: Evaluation of phase transitions in pork muscle at high pressures. J Food Proc Eng, 27: 377.

Chapter 4 Calorimetry of Proteins in Dilute Solution G. Eric Plum

Introduction Differential Scanning Calorimetry Isothermal Titration Calorimetry (ITC) Conclusion References

67 68 77 83 84

Introduction As the food industry begins to take advantage of recent developments in protein chemistry by introducing enzymes and structural proteins into modern food materials and their processing, detailed understanding of protein chemical and physical properties becomes increasingly important. Development of a predictive understanding of the energetics-structure-function relationships will be required to fully exploit the possibilities presented to engineer proteins with novel substrate specificity or enhanced physical properties, including thermal stability, pH, and ionic strength optima. Calorimetry provides several valuable tools for the characterization of the thermal properties of proteins and their interactions with other macromolecules and small-molecule affectors. The objective of this chapter is to introduce and summarize the methods of modern 67

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ultrasensitive calorimetry, their application to purified protein samples, and interpretation of the resultant data. In its broadest terms, calorimetry involves measurement of heat effects in a system in response to some perturbation. Modern ultrasensitive calorimetry comprises two distinct techniques, each of which requires specialized instrumentation to affect the perturbation to the system. In differential scanning calorimetry (DSC), that perturbation is a change in temperature of the sample. In isothermal titration calorimetry (ITC), the perturbation is the introduction of new material into the sample.

Differential Scanning Calorimetry Information content To fully exploit the structural and catalytic properties of proteins it is critical to develop a predictive understanding of their functions and stability as a function of temperature and solution conditions. Monitoring the unfolding of a macromolecule induced by exposure to elevated temperature is a classical method for evaluating stability. DSC is particularly well suited to characterization of protein stability because no chromophores are required, nor are optically clear solutions required. Most importantly, interpretation of the resultant data is not dependent on any model of the unfolding process. The thermodynamic characterization of the protein unfolding process derived from DSC data can be used to predict the stability of the protein at any temperature. With the techniques of modern molecular biology and biochemistry one can manipulate the structure of biological macromolecules almost at will. Site-directed mutagenesis permits substitution or deletion of amino acids at the polypeptide sequence level. Techniques have been developed to include, in addition to the naturally occurring amino acids, a variety of nonnatural amino acid variants into proteins. Because they are state functions, thermodynamic quantities (heat capacity, enthalpy, entropy, free energy) represent sums of contributions from many sources. Systematic comparison of protein variants allows, in principle, for the quantification of contributions of particular amino acid side chain interactions to the measured thermodynamic quantities. The increasingly wide availability of detailed structural models for

Calorimetry of Proteins in Dilute Solution

69

proteins from x-ray crystallography and nuclear magnetic resonance permit one to examine mutation-induced changes in atomic detail. Unfortunately, a simple substitution of one amino acid for another may have effects that propagate well beyond the particular interactions that appear to change in the three-dimensional structure. Because many of the forces that determine the thermodynamic parameters operate on scales of distance smaller than can be reliably determined by the structural methods, these assignments may not be reliable. Instrumentation During scanning calorimetric examination of a biological macromolecule in dilute aqueous solution, most of the energy introduced into the system goes toward heating the solvent. A concentration of about 0.1 mg/ml is typically used for protein DSC. Differential scanning calorimeters of sufficient sensitivity to study biological macromolecules in dilute solution typically are based on the power compensation method. Modern ultrasensitive DSC instruments are capable of detecting signals deviating from the baseline of well under 100 nW. While commercial instruments vary in the means by which they make the measurement (Privalov et al. 1995; Plotnikov et al. 1997), the general concept is described here. The power compensation DSC instrument comprises two matched cells, both fixed in position and in thermal contact with a thermopile, housed in an adiabatic chamber. In one cell is placed the sample solution. In the second cell is placed a reference solution. A small (2–3 atm) pressure is maintained on the cells to suppress bubble formation and evaporation of the samples. As the instrument is heated, the thermopile responds to differences in temperature of the two cells. Heat is applied to the lagging cell to zero the temperature difference. The amount of energy required to compensate for the thermal event that causes the cell to lag in temperature is directly related to the applied heat, which is quantified in terms of power (energy per time). Thus, the primary data collected by the power compensated DSC instrument is a curve of power versus temperature. Division by the heating rate converts the curve to the heat capacity difference between the sample and reference cells, frequently referred to as excess heat capacity, as a function of temperature. Normalization by the amount of analyte yields a curve in terms of specific or molar heat capacity.

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The sample must be prepared so that the only the difference between the sample and reference solutions is the presence of the macromolecule of interest. This is generally best accomplished by exhaustive equilibrium dialysis of the sample solution (dialysand) against the buffer solution (dialysate) and subsequent use of the dialysate as the reference solution. Even with the greatest care, the solutions in the sample and reference cells cannot be matched exactly. The macromolecule under study displaces solution that contains solvent, buffers, salts, etc., which have temperature-dependent heat capacities. These effects, coupled with imperfect matching of the calorimeter cells, mean that a small but nontrivial baseline correction must be made to separate the contribution of the macromolecule order-disorder transition from the solution displacement and instrumental effects. Small errors in sample preparation and baseline correction can lead to large errors in the calculated thermodynamic parameters. Some attempts have been made to standardize DSC experimental and analysis procedures to reduce interlaboratory variability (Hinz and Schwarz 2001), but care should be exercised when comparing data from different laboratories. Basic equations With modern instrumentation, complex multidomain DSC thermograms can be resolved into individual transitions (Völker et al. 1999). However, for this discussion it is assumed that the thermogram is of a single transition, which is typically observed for single domain globular proteins in solution. Thus, only monomolecular unfolding processes, those involving only a single polypeptide chain, are considered. Methods to address multisubunit proteins exist but are beyond the scope of this discussion. Figure 4.1 shows a simulated DSC thermogram of the temperatureinduced unfolding of a small globular protein. Differential scanning calorimetry data are analyzed using a set of standard thermodynamic relations (Privalov and Potekhin 1986). All thermodynamic relations herein are at constant pressure. The DSC curve is analyzed in terms of the relationships between the measured heat capacity and the thermodynamic parameters of interest. The excess heat capacity, C pex, is the difference in heat capacity between the sample solution containing the macromolecule of interest relative to the reference solution. Tm is the temperature at the mid-

Calorimetry of Proteins in Dilute Solution

71

12,000 Tm

Cp cal mol–1 K–1

10,000 8,000

ΔH(Tm)

6,000 4,000 2,000

ΔCp

0 300

325

350

375

400

Temperature (K)

Figure 4.1. Simulated DSC thermogram of a small globular protein. The excess heat capacity versus temperature curve is calculated using Tm = 350, ΔH(Tm) = 100 kcal/ mol, and ΔCp = 1.5 kcal/mol · K.

point of the transition; that is, the temperature at which the concentrations of the folded and unfolded forms of the protein are equal. The maximum of the C pex versus temperature curve will correspond to Tm only when the unfolding unit is monomolecular and the difference in heat capacity between the folded and unfolded forms of the protein is negligible. The enthalpy change at Tm is determined from integration of the DSC curve ΔH (Tm ) = ∫ C pex dT

(4.1)

where the integration covers the entire temperature range of the denaturation transition. The free energy change at temperature T, which is a measure of the protein’s stability at that temperature, depends on the enthalpy and entropy changes. ΔG (T ) = ΔH (T ) − T ΔS (T )

(4.2)

To describe the thermodynamics of the system at any temperature, it is necessary to adjust the DSC determined enthalpy and entropy changes determined at Tm. The heat capacity change associated with the order-disorder transition, ΔCp, which comes directly from the DSC curve, is used for the temperature extrapolation.

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ΔH (T ) = ΔH (Tm ) + ∫T ΔC p dT

(4.3)

ΔC p dT T

(4.4)

m

T

ΔS (T ) = ΔS (Tm ) + ∫T

m

For a monomolecular process, ΔG(Tm) = 0 and therefore from Equation 4.2 ΔS (Tm ) =

ΔH (Tm ) Tm

(4.5)

For higher order complexes, additional statistical effects must be included (Marky and Breslauer 1987). Assuming that the heat capacity change is independent of temperature, by integrating and combining the expressions above, the enthalpy, entropy, and free energy changes are approximated by ΔH (T ) ≈ ΔH (Tm ) − (Tm − T )ΔC p

(4.6)

T ΔS (T ) ≈ ΔS (Tm ) − ΔC p ln ⎛ m ⎞ ⎝T ⎠

(4.7)

Combining Equations 4.2, 4.6, and 4.7 permits calculation of the free energy change at any temperature from the three parameters, ΔH(Tm), Tm, and ΔCp, obtainable from the DSC curve. ΔG (T ) ≈

Tm − T T ΔH (Tm ) − (Tm − T ) ΔC p + T ΔC p ln ⎛ m ⎞ ⎝T ⎠ Tm

(4.8)

Using these expressions one can predict the stability of the protein and its thermodynamic origins at any temperature. The van’t Hoff enthalpy change While the enthalpy change measured by DSC does not depend on a model, comparison with models can provide insight into the nature of the order-disorder transition. Consider an equilibrium constant, Keq. K eq =

[ unfolded ] [folded ]

(4.9)

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Because the shape of the DSC curve reflects the change in the equilibrium constant as a function of temperature, the van’t Hoff model can be applied to DSC data as an alternate means of enthalpy change determination. The van’t Hoff model is based on the temperature dependence of the dimensionless equilibrium constant. ⎛ ∂ ln K eq ⎞ ⎛ ∂ ln K eq ⎞ ΔH vH = RT 2 ⎜ = −R ⎜ ⎟ ⎝ ∂T ⎠ ⎝ ∂ (1 / T ) ⎟⎠

(4.10)

Note that the units of the van’t Hoff enthalpy, ΔHvH, are defined by the units of the constant R. Comparison of the model independent calorimetrically determined ΔH(Tm) value with the ΔHvH value assesses the validity of the assumptions employed in the derivation of the van’t Hoff relation. Specifically, it is assumed that the transition from the ordered, low temperature form to the disordered, high temperature form passes through no thermodynamically significant intermediate states (two-state assumption); that is, there is no partial unfolding of the protein in the denaturation pathway. The ΔHvH reports the enthalpy change associated with disruption of a single cooperative unit, the fraction of the protein that acts as a single thermodynamic unit. There are several methods used to extract ΔHvH from the shape of equilibrium denaturation curves, which are frequently based on indirect observations such as temperature-dependent spectroscopic measurements (Marky and Breslauer 1987). The equation below comes most directly from the DSC curve. Because the equation, as written here, does not account for changes in heat capacity, a DSC curve from which the contribution of ΔCp has been subtracted should be used. 2 ΔH vH = 4 RTmax

C pmax ΔH (Tmax )

(4.11)

For small globular proteins, it is expected that ΔHvH = ΔH(Tm). When ΔHvH ≠ ΔH(Tm), an error in the determination of the folded protein concentration may be indicated, the baseline may be assigned incorrectly, or the origins of the deviation may depend on a more fundamental property of the protein system. While in practice they must be examined and eliminated, for this discussion errors in concentration

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determination and assignment of the baseline will be discounted. When ΔHvH < ΔH(Tm), a deviation from the two-state assumption is indicated; the cooperative unit is smaller than the entire protein. The transition involves either unfolding intermediates or independent domains. When ΔHvH > ΔH(Tm), aggregation of the unfolded polypeptide has sharpened the DSC observed transition. Origins of the heat capacity change The molecular origins of the heat capacity of protein and its change with denaturation are still a matter of active study and debate (Prabhu and Sharp 2005). About 30 years ago, Sturtevant (1977) enumerated the underlying contributions to the heat capacity of proteins; these include the hydrophobic effect, electrostatic effects, hydrogen bonds, intramolecular vibrations, and changes in equilibria. The heat capacity and its change with denaturation can be roughly divided into contributions from hydration (protein-solvent and solvent-solvent interactions) and from intraprotein interactions, ΔC p = ΔC pHydration + ΔC pProtein. Relatively little progress has been made in the ensuing years in understanding the magnitudes of the various contributions to the heat capacity. Because it is widely believed that the contribution of hydration dominates, most theoretical and experimental work has been directed at the ΔC pHydration term. Record and coworkers (Spolar, Ha, and Record 1989) described an empirical correlation between the change in solvent-accessible nonpolar surface area of a protein ΔAnp and the heat capacity change associated with thermal denaturation. Numerous workers have extended the empirical model by inclusion of terms to account for differences in the contributions from hydration of polar and nonpolar surface elements (Prabhu and Sharp 2005). ΔC pHydration = c p ΔAp + cnp ΔAnp

(4.12)

where cp and cnp are empirical coefficients, ΔAp and ΔAnp are the differences between the folded and unfolded forms of the protein in polar and nonpolar surface area in contact with solvent. Although the structure of the folded form of most proteins is stable and the relevant surface areas readily calculated, the fluctuating structure of the unfolded form is poorly defined, and thus different methods of calculating the surface areas of the unfolded form lead to different empirical equations.

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Cold denaturation Due to the sign and magnitude of the heat capacity change relative to the changes in enthalpy and entropy observed for globular proteins, at some temperature a maximum in the free energy change is observed (Privalov 1990). This further implies that, in addition to the high temperature (Tm) at which ΔG = 0, there is a low temperature that satisfies the same condition. In most cases, this implied cold denaturation temperature is below the freezing point of water; however, there are examples of cold denaturation observable within the aqueous liquid temperature range accessible experimentally with fixed cell instrumentation, wherein the cells must be filled completely (Privalov 1990). The cold denaturation phenomenon may be particularly important in freezing and lyophilization processes. DSC data can quantify high-affinity binding Binding equilibria between a protein and a small molecule effector, such as a cofactor or drug, or a second protein subunit, can alter the protein’s denaturation temperature. If the second molecule binds more tightly to the folded form of the protein than to the unfolded form, the denaturation will shift to higher temperature. Conversely, preferential binding to the unfolded form shifts the denaturation to lower temperature. DSC thermograms are particularly well suited to measure very tight binding based on the observed bindinginduced changes in the heat capacity versus temperature profiles. Brandts and Lin (1990) present methods and models for analysis of the shapes of DSC thermograms to quantify binding affinities for protein-small molecule and protein-protein interactions up to 1040 M−1, whereas most methods cannot quantify binding constants greater than 1010 M−1. Assumptions The above described analysis of DSC thermograms in terms of equilibrium thermodynamic parameters depends on a number of assumptions about the system and the design of the experiment. It is important to evaluate the validity of the assumptions to assure the quality of the derived thermodynamic data and because deviations from the assumed behavior can provide insight into the protein unfolding process.

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Equilibrium, two-state, reversible transitions The above analysis of the DSC curve assumes that the system is in equilibrium. The DSC measurement is that of power (energy/time). The observed signal increases with scanning rate; therefore, it is advantageous to scan the temperature at a high rate. This advantage is tempered by the necessity to maintain equilibrium conditions in the sample. If the scanning rate is too high, the ΔH value will still be correct, but the shape and position of the curve will be compromised. Thus, ΔG, ΔS, and Tm will be incorrect. To ensure that the equilibrium assumption is valid, it is advisable to conduct the DSC experiment as a function of scanning rate. If the resulting thermograms are independent of scanning rate, the equilibrium assumption is satisfied. As part of the equilibrium assumption, it is further assumed that the process is fully reversible. Generally, protein denaturation is described by a reaction involving a reversible unfolding of the native state (N) to form a soluble unfolded form (U), which may subsequently irreversibly form an aggregated state (D) (Privalov and Potekhin 1986). N

⎯⎯ → U ⎯⎯ → mD ←⎯ ⎯

(4.13)

Even if the U → D transition had no effect on Cp, it would manifest itself in the shape of the transition, reflected in an increase in ΔHvH. If the U → D transition is not monomolecular, that is m ≠ 1, ΔHvH will increase with increasing concentration. It is commonly assumed that subsequent to the temperature induced unfolding process the protein exhibits a random coil confirmation. This, however, is an oversimplification in most cases. Due to their implication in some diseases, unfolded proteins are receiving intensive structural study (Mittag and Forman-Kay 2007). Some proteins refold into an alternate confirmation, whereas others form aggregates or precipitates. Upon unfolding, most soluble proteins exhibit reduced solubility in aqueous solutions and tend to aggregate. The extent of this aggregation varies widely, depending on the specific amino acid composition and sequence as well as the solution conditions. Minor aggregate formation may not be readily visualized in the DSC trace but will affect the shape of the transition resulting in a difference between the observed calorimetric and van’t Hoff enthalpy changes. Extensive

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aggregation resulting in precipitation is often apparent by wild fluctuation in the high temperature baseline. Concentration and purity The enthalpy and heat capacity measurements conducted using DSC are based on the amount of material thought to be in the sample. Whether the objective is molar or specific values, the measured thermal effects are divided by the quantity of the analyte protein. Therefore, accurate DSC results depend on accurate determination of the amount of analyte protein present, as well as its purity. The best means of determining concentration of the analyte will vary with the properties of the particular protein under study. It is generally assumed that the concentrations of all components in the solution are sufficiently low that all activity coefficients may be satisfactorily approximated by unity. While this assumption may not be correct, in most cases there is little alternative. Selection of hydrogen ion buffer The selection of hydrogen ion buffer is critically important in DSC experiments. Buffers with high heats of ionization lead to temperature dependent changes in the pH of the solution. Thus, any pH dependent changes in the protein will be superimposed onto the temperature dependent changes, which the DSC experiment is designed to measure. Unfortunately, some of the most widely used buffers for general biological macromolecule studies exhibit high ionization heats. A particularly egregious example is tris buffer, although most buffers carrying amine groups are problematic. A second important issue in buffer selection for DSC studies is the buffer’s propensity for metal ion chelation. Because proteins frequently carry anionic functionalities on their surfaces or require metal ion cofactors, interactions with metal ions are often important in stabilizing their structures. Competition for metal ion binding between the protein and the buffer can compromise the DSC experiment. Isothermal Titration Calorimetry (ITC) Information content Most biological processes involve one or more binding events. The types of binding reactions are varied and include, but are not limited

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to, assembly of protein subunits into functional enzyme complexes, formation of enzyme-inhibitor complexes, formation of protein-nucleic acid complexes, enzyme-substrate binding, and enzyme-cofactor binding. These binding processes can be described in terms of the standard thermodynamic parameters. A predictive understanding of the binding process can be achieved by measurement of the binding free energy change ΔG, enthalpy change ΔH, entropy change ΔS, and their temperature dependence ΔCp. All of the binding processes enumerated above are amenable to analysis by some variation of the ITC experiment. Because the binding sites for small molecules to proteins tend to be well defined and small in number, and because most of the binding reactions involve a nonzero enthalpy change, protein-small molecule interactions frequently are particularly well suited to examination by isothermal titration calorimetry. Here, we consider a simple association (without reaction) defined by the equilibrium nL + M ↔ MLn of a small molecule, L, with a protein or other macromolecule, M, with n identical, noninteracting binding sites. The ITC method can be applied to more complex equilibria; however, that is beyond the scope of this discussion. Instrumentation The design of the power compensation isothermal titration calorimeter is conceptually similar to the power compensation DSC but is adapted for isothermal operation and for introduction of liquid into the sample cell (Wiseman et al. 1989). The instrument comprises a thermostated chamber housing inverted lollipop-shaped sample and reference cells. The cell volumes are on the order of 1 ml. A small constant amount of heat is applied to the reference cell. A sensor detects differences in the temperature between the cells, and heat is applied to the lagging cell. The energy applied per unit of time is recorded. Rather than by heating as in the DSC, the system is perturbed by addition of material into the sample cell by means of a syringe. Usually, the titrant solution contains the small molecule L and the sample cell contains the macromolecule M. Typically, upon introduction of an aliquot of titrant (a few microliters) into the sample cell, an identical volume of the previous solution is expelled from the measuring volume of the cell. Stirring of the sample cell provides efficient mixing of the titrant solution into the titrate solution. In a typical experiment, about 20 injections of titrant are made. The concentration of L in

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the syringe is selected to provide a final ratio of concentrations [L]/[M] ≈ 2n. The primary data from an ITC experiment is a plot of applied power as a function of time. Upon addition of an aliquot of titrant, any thermal response in the sample cell is compensated by heating the sample or reference cell, as appropriate. Sufficient time must elapse between injections to return the instrument response to the baseline. Thus, for each addition of titrant to the sample cell, a peak is observed in the power-versus-time profile. Each of these peaks is integrated to yield a value for the thermal response (heat) due to the titrant injection. The resultant plot of the measured heat due to injection of L, dQ/d[L], versus the concentration of added binding species, or more typically the molar ratio of the binding species in the cell, that is [L]/[M], is analyzed to determine the thermodynamic parameters that characterize the binding process. See Figure 4.2 for examples of the raw and integrated data plots. For the model described here, the resultant curve is sinusoidal beginning at approximately ΔH for tight binding or less for lower-affinity binding and declining to a small value that includes dilution effects (see below), with an inflection point at [L]/[M] = n. Thermal effects observed in the ITC experiment include those associated with macromolecule-macromolecule, macromolecule-small molecule interactions, small molecule-small molecule interactions, and heats of dilution, as well as temperature differences between the solution in the syringe and the solution in the sample cell. Therefore, it is critical that additional experiments identical to the first, except for selective absence of the binding species in the sample cell or syringe, be performed to correct for dilution heats and artifacts due to temperature differences between the syringe and sample cell. The corrected heat is determined by subtraction of dilution heats for L and for M as well as a buffer “dilution” that corrects for injection artifacts and mismatched buffers. Qcorrected = Qmeasured − QL_dilution − QM_dilution − Qbuffer

(4.14)

ITC data analysis While in some advanced applications DSC data are model dependent, determination of the basic thermodynamic functions from DSC does not depend on a model of the process under study. In contrast,

Figure 4.2. Isothermal titration calorimetry of binding of two inhibitors to βglucosidase (Zechel et al. 2003). (a) The raw data (upper panel) and the integrated data with fitted curve (lower panel) for an inhibitor with K = 1.25 × 105 M−1. (b) An inhibitor with K = 1.5 × 107 M−1. Reprinted with permission from J. Am. Chem. Soc. 2003, 125, 14313–23. Copyright 2003 American Chemical Society.

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81

isothermal titration calorimetry is model dependent. To interpret the ITC curve, expressions must be derived to relate the change in heat, dQ, as a function of change in added small molecule, d[L], to the amount of complex formed, d [LM], in the cell volume, V. dQ d [ LM ] = ΔHV d [L] d [L]

(4.15)

For the reaction described herein where n = 1, a closed form expression d [ LM ] for can be written in terms of K, [M], [L] (Wiseman et al. d[L] 1989). 1 ⎞ [L] ⎞ ⎛⎛ 1 − ⎜ ⎜1 + 2 − ⎝ ⎝ [ M ] K ⎟⎠ [ M ] ⎟⎠ d [ LM ] 1 (4.16) = + 1 2 2 2 ⎛ [L] 2 d [L] 1 ⎞ ⎛ 1 ⎞ ⎞ [L] ⎛ ⎛ ⎞ ⎜⎝ ⎜⎝ [ M ] ⎟⎠ − 2 [ M ] ⎜⎝ 1 − [ M ] K ⎟⎠ + ⎜⎝ 1 + [ M ] K ⎟⎠ ⎟⎠ The ITC curve can then be fitted by standard nonlinear least squares techniques for ΔH and K. The free energy and entropy changes are determined by the standard relations ΔG(T) = RTlnK and ΔH (T ) − ΔG (T ) . A series of titrations as a function of T temperature provide the heat capacity change associated with the binding reaction via the temperature dependence of the enthalpy dΔH . change, ΔC p = dT Only for the most simple models can a closed form expression be written relating the change in heat accompanying introduction of an aliquot of titrant to the parameters of the binding reaction. More elaborate models can be evaluated numerically. Software provided with the instruments can analyze ITC data in terms of several different models, including the single binding site case considered here, multiple identical binding sites that may or may not interact, and multiple classes of distinct binding sites. Many, if not most, binding events involving macromolecules and small molecules or other macromolecules are coupled to changes in ionization state of one or more charged groups. Thus, the measured ΔS (T ) =

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heat associated with the binding event includes the net ionization heats of the groups for which protonation or deprotonation occurs as well as compensating effects from the included hydrogen ion buffer. It is therefore advisable to measure the association heat in two or more hydrogen ion buffers, which differ in ionization heat, at identical pH values (Baker and Murphy 1996). The plot of the measured association heat as a function of the buffer ionization heat permits determination of the intrinsic heat of binding and the net number of protons taken up or released upon binding. The intercept at zero buffer ionization heat corresponds to the intrinsic enthalpy of binding; the slope corresponds to the net change in protonation. Due to the strong model dependence of the ITC method, challenges arise that are not part of the analysis of DSC data. Most ITC-binding isotherms comprise a small number of features: specifically, the intercept on the enthalpy axis and one or two inflection points. Thus, the number of fitting parameters that the data can support is limited. Many processes of interest involve competing equilibria with multiple binding species and binding sites that require elaborate multiparameter models to describe. Caution, and appropriate statistical tests, should be applied to ensure that the data can support the applied model. It is relatively easy to devise a model that cannot be adequately addressed by ITC isotherms alone; however, if complementary data are available to fix some of the fitting parameters, such models may become tenable. Range of applicability The classical ITC experiment described here is limited to binding affinities and concentrations that produce a titration curve with a shape that can be fit accurately by nonlinear least squares methods. A rough estimate of this range defined by the macromolecule concentration and the equilibrium association constant is 1 < [M]K < 1000 (Wiseman et al. 1989), with 10 < [M]K < 100 being preferred. Note that while the macromolecule concentration could always be adjusted to place [M]K in range, the heat produced by the binding reaction must be detectable. This places an effective limit on the range of binding affinities accessible to direct ITC measurement. Figure 4.3 shows how the shape of the ITC titration curve varies with [M]K. As [M]K → ∞, the ITC curve becomes a step function with essentially all the L injected binding to M, until all of the available binding sites are exhausted. So dQ/d[L] = ΔH for [L]/[M] < n and

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Figure 4.3. Dependence of the shape of the ITC titration curve on [M]K for the reaction nL + M ↔ MLn.

dQ/d[L] = 0 for [L]/[M] > n. There is no information about K except that it is large. As [M]K → 0, the ITC curve approximates a flat line and provides no information on ΔH, n, or K. Linkage to other equilibria can be used to measure indirectly binding affinities that are too tight or too weak to measure by standard ITC experiments (Doyle et al. 1995; Sigurskjold 2000). If n is known, reasonable estimates of K may be obtained by extension of the titration range so the final [L]/[M] >> 2n (Turnbull and Daranas 2003). Note that this method provides only an estimate of K and will not provide accurate ΔH values.

Conclusion Modern ultrasensitive calorimetry provides powerful tools for understanding the stability of proteins in solution, the forces that maintain their folded structures, and their interactions with other macromolecules and small molecules. Differential scanning calorimetry quantifies the thermal (Tm) and thermodynamic (ΔG) stabilities of the protein. The thermodynamic origins of the stability (ΔH, ΔS, and ΔCp) can be interpreted to dissect the forces maintaining the folded structure and how they depend on

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the structure of the protein and the solution conditions. The modelindependent values derived directly from the DSC thermogram can be compared to model-dependent values derived from the shape of the DSC curves to gain insight into the unfolding mechanism and aggregation. Isothermal titration calorimetry provides a means to directly measure the heat of interaction (ΔH) of a protein with another macromolecule or with small molecules. Application of models for the association reaction provides detailed thermodynamic characterization (K, ΔG, ΔS, and ΔCp) of the binding process. Taken together, the techniques of modern solution calorimetry provide a predictive understanding of the stability of a protein and its interactions with other molecules as a function of temperature and solution conditions. References Baker B.M. and Murphy K.P. 1996. Evaluation of linked protonation effects in protein binding reactions using isothermal titration calorimtery. Biophys J, 71:2049–55. Brandts J.F. and Lin L. 1990. Study of strong to ultra tight protein interactions using differential scanning calorimetry. Biochemistry, 29:6927–40. Doyle M.L., Louie G., Dal Monte P.R., and Sokoloski T.D. 1995. Tight binding affinities determined from thermodynamic linkage to protons by titration calorimetry. Methods Enzymol, 259:183–94. Hinz H.J. and Schwarz F.P. 2001. Measurement and analysis of results obtained on biological substances with differential scanning calorimetry. Pure Appl Chem, 73(4):745–59. Marky L.A. and Breslauer K.J. 1987. Calculating thermodynamic data for transitions of any molecularity from equilibrium melting curves. Biopolymers, 26:1601–20. Mittag T. and Forman-Kay J.D. 2007. Atomic-level characterization of disordered protein ensembles. Curr Opin Struct Biol, 17:3–14. Plotnikov V.V., Brandts J.M., Lin L., and Brandts J.F. 1997. A new ultrasensitive scanning calorimeter. Anal Biochem, 250:237–244. Prabhu N.V. and Sharp K.A. 2005. Heat capacity in proteins. Annu Rev Phys Chem, 56:521–48. Privalov G., Kavina V., Freire E., and Privalov P.L. 1995. Precise scanning calorimeter for studying thermal properties of biological macromolecules in dilute solution. Anal Biochem, 232:79–85. Privalov P.L. 1990. Cold denaturation of proteins. Crit Rev Biochem Mol Biol, 25(4):281–305. Privalov P.L. and Potekhin S.A. 1986. Scanning microcalorimetry in studying tem-

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perature-induced changes in proteins. Methods Enzymol, 131:4–51. Sigurskjold B.W. 2000. Exact analysis of competition ligand binding by displacement isothermal titration calorimetry. Anal Biochem, 277:260–66. Spolar R.S., Ha J., and Record, Jr. M.T. 1989. Hydrophobic effect in protein folding and other noncovalent processes involving proteins. Proc Natl Acad Sci USA, 86:8382–85. Sturtevant J.M. 1977. Heat capacity changes in processes involving proteins. Proc Natl Acad Sci USA, 74:2236–40. Turnbull W.B. and Daranas A.H. 2003. On the value of c: Can low affinity systems be studied by isothermal titration calorimetry? J Am Chem Soc, 125:14859–66. Völker J., Blake R.D., Delcourt S.G., and Breslauer K.J. 1999. High-resolution calorimetric and optical melting profiles of DNA plasmids: Resolving contributions from intrinsic melting domains and specifically designed inserts. Biopolymers, 50:303–18. Wiseman T., Williston S., Brandts J.F., and Lin L. 1989. Rapid measurement of binding constants and heats of binding using a new titration calorimeter. Anal Biochem, 179:131–7. Zechel D.L., Boraston A.B., Gloster T., Boraston C.M., Macdonald J.M., Tilbrook D., Matthew G., Stick R.V., and Davies G.J. 2003. Iminosugar glycosidase inhibitors: Structural and thermodynamic dissection of the binding of isofagomine and 1-deoxynojirimycin to β-glucosidases. J Am Chem Soc, 125:14313–23.

Chapter 5 Thermal Analysis of Denaturation and Aggregation of Proteins and Protein Interactions in a Real Food System Valerji Y. Grinberg, Tatiana V. Burova, and Vladimir B. Tolstoguzov

Introduction Effects of pH on Thermal Denaturation of Food Proteins Effects of Salts on Thermal Denaturation of Food Proteins Effects of Alcohols on Thermal Denaturation of Food Proteins Effects of Odorants on Thermal Denaturation of Food Proteins Effects of Polysaccharides on Thermal Denaturation of Food Proteins Postdenaturation Aggregation of Food Proteins Conclusion References

87 89 95 99 102 105 110 112 113

Introduction Normally, food contains a heterogeneous, heterophase mixture of high- and low-molecular-weight components and their aggregates and complexes. Among food macromolecules, proteins and polysaccharides are largely responsible for the structural changes accompanying food processing and for mechanical and other physical properties of foods. Proteins are both most-multifunctional biopolymers and mostversatile food macromolecules. Normally, a functional protein has a unique ordered (crystal-like) molecular structure, which appears to be 87

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responsible for a high specificity and efficiency of its functioning. Food proteins greatly influence the structure-property relationship in foods. Heating is one of the most important treatments of food processing. Heat denaturation and aggregation of proteins are therefore the most typical events in food processing. For instance, the difference between raw and boiled eggs is caused by the denaturation and aggregation of the denatured egg proteins. The heat denaturation involves a cooperative or noncooperative transition of a protein from its folded to its unfolded state. It is related to some structural disorganization of the three-dimensional structure of native molecules. The unfolding changes the interaction of the protein with aqueous medium and induces aggregation of the unfolded protein molecules. Consequently, the denaturation governs the structure, flavor, texture, and other qualities of food. It also contributes to the nutritional qualities and physical stability of the foods during storage. The heat effects of denaturation and aggregation of proteins are usually small and have the opposite sign, namely, heat absorption (endothermic) and release (exothermic), respectively. The key role of heating in food processing determines a high efficiency of thermal analysis techniques, in particular, differential scanning calorimetry (DSC), for food system investigations. Among various DSC methods, the high-sensitivity differential scanning calorimetry is of most significance. It was developed and substantially evaluated in the USSR Academy of Sciences and later extensively applied and improved in many laboratories. Its high sensitivity provides the heat capacity measurements in dilute solutions of proteins and other biopolymers. In the field of food science, the high-sensitivity DSC was first systematically applied at the A.N. Nesmeyanov Institute of Organo-Element division of the USSR Academy of Sciences in the early 1980s (Tolstoguzov et al. 1985; Tolstoguzov 1988, 1991; Grinberg et al. 1989). A systematic investigation in the field of food protein denaturation was begun with thermal denaturation of individual proteins under physicochemical conditions typical of food processing (Grinberg et al. 1988, 1989, 2000; Burova et al. 1989a,b; Burova et al. 1991). These conditions are pH, salt composition (Bikbov et al. 1983; Danilenko et al. 1986a, 1987), the presence of lipids and flavor compounds (Mikheeva et al. 1998; Burova et al. 1999; Grinberg et al. 2002; Burova et al. 2003), other low-molecular weight compounds (Danilenko et al.

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1986b), and polysaccharides (Tolstoguzov 1988, 1991; Burova et al. 1992a, 2002b). The applied objective of calorimetry was to elucidate the mechanisms of structure formation and structure-texture and structure-physical property relationships in foods. These investigations resulted in (1) general methodological approaches to study denaturation of proteins; (2) basic information on the protein behavior upon heating and processing into different kinds of food; (3) food flavorings; (4) thermodynamic compatibility of denatured proteins with native proteins and polysaccharides and phase behavior of macromolecular components in biological and food systems; and (5) thermodynamic aspects of composition-property relationship in formulated food (Tolstoguzov 1988, 1998, 2000, 2002). The results have been reported in many reviews and research publications (Tolstoguzov et al. 1985; Tolstoguzov 1988, 1991, 2000, 2002; Grinberg et al. 1989, 2000; Burova et al. 2003). This chapter is concentrated on the effects of pH, neutral salts, alcohols, and polysaccharides on thermal denaturation of food proteins. Another objective of this chapter is to consider the potential of high-sensitivity DSC for investigation of protein aggregation.

Effects of pH on Thermal Denaturation of Food Proteins Among food proteins, the seed storage oligomeric proteins and, primarily, 11S globulins that represent the main proteins of most oil and legume seeds, are of particular importance. The molecule of 11S globulins has a molecular weight of about 300 kDa and consists of six subunits located in the vertices of a trigonal antiprism. Each subunit contains two polypeptide chains linked by a disulfide bond. Upon thermal denaturation, the molecule of 11S globulin behaves as an ensemble of 12 independent cooperative units (domains). The unfolding mechanism of domains is consistent with the two-state model (Grinberg et al. 1988). This two-state model can therefore be used to analyze the denaturation of 11S globulins. The quaternary structure of 11S globulins depends on pH. The pHinduced changes in the quaternary structure of 11S soybean globulin (taken as an example) were compared with changes in conformational stability of the protein (Danilenko et al. 1987).

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The sedimentation velocity data show that at room temperature 11S globulin undergoes the following structural changes upon decreasing pH from 7.6 to 2.0: 11S (dodecamer) ↔ 7S (hexamer) → 3S (dimer). The thermograms of 11S globulin at different pH values are given in Figure 5.1a. With deceasing pH, the denaturation heat capacity peak splits into two peaks that are shifted to lower temperatures. The hightemperature peak decreases, and the low-temperature peak increases; only the low-temperature peak is observed at pH 3.0. The 11S globulin converts into the 3S dimer at pH ≤ 2.75, and the thermograms do not display any peaks. Hence, the low-temperature and hightemperature peaks in the bimodal thermograms of 11S globulin can be assigned to the denaturation of the hexamer and the dodecamer, respectively. Figure 5.1b shows that the denaturation temperatures of both forms of 11S globulin decrease with decreasing pH. A deconvolution of the thermogram of 11S globulin can be performed in terms of the two-state model if one considers the dodecamer and the hexamer as ensembles of 12 and 6 identical cooperative units, respectively. At pH 3.5, it provides values of the denaturation enthalpy and heat capacity increment for both forms of the protein: ΔdH7S = 12.2 ± 0.5 J g−1, ΔdCp,7S = 0.50 ± 0.08 J g−1 K−1 and ΔdH11S = 20.7 ± 1.5 J g−1, ΔdCp,7S = 0.46 ± 0.08 J g−1 K−1. A difference in the denaturation heat capacity increments does not seem to be significant. However the denaturation enthalpy of the dodecamer is significantly larger than that of the hexamer. This difference reflects the dependence of the denaturation enthalpy on temperature. According to Kirchhoff ’s law, it is a linear function with a slope Figure 5.1. Thermal denaturation of 11S soybean globulin at different pH values. (a) Thermograms at different pH values (shifted arbitrary along the heat capacity axis). Points represent approximation of the thermogram at pH 7.6 by a two-state model considering the protein molecule as an ensemble of 12 thermodynamically equivalent domains. (b) Denaturation temperatures of dodecamer and hexamer forms of the protein versus pH. The insert shows the pH dependences of weight fractions of the dodecamer and hexamer forms, 11S and 7S, respectively, from velocity sedimentation data. (c) Correlation between values of the denaturation enthalpy and the denaturation temperature for different forms of the protein according to Kirchhoff ’s law (see explanations in the text). (d) Excess denaturation free energy per constituent chain of the protein versus pH at temperature T0 = 352 K.

91

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equal to the denaturation heat capacity increment, ΔdCp (Privalov 1979). Figure 5.1c demonstrates a correlation between the denaturation enthalpy and the denaturation temperature of the different forms of 11S globulin at pH 3.5 and pH 7.6. This dependence is approximated by a straight line with a slope of 0.46 ± 0.10 J g−1 K−1. It coincides with the average value of the denaturation heat capacity increment of 11S globulin, ΔcCp = 0.46 ± 0.04 J g−1 K−1. This result reveals a thermodynamic consistency of the denaturation parameters of 11S globulin. The denaturation parameters of the 11S globulin forms can be used to calculate their denaturation free energies at a standard temperature as a function of pH (Prigogine and Defay 1954): T0 ⎤ ⎡ Δ d G (T0 , pH ) = Δ d H ( pH ) ⎢1 − + Δ d C p [T0 − Td ( pH )0 ] − ⎣ Td ( pH ) ⎥⎦ ⎡ T0 ⎤ T0 Δ d C p ln ⎢ ⎣ Td ( pH ) ⎥⎦

(5.1)

Here, the standard temperature T0 = 352 K is the denaturation temperature at a reference value of pH (pH0 3.5). The excess denaturation free energy, that is, a general criterion of pH effects on protein denaturation, can then be determined: Δ d G E ( pH ) = Δ d G (T0 , pH ) − Δ d G (T0 , pH 0 )

(5.2)

Dependences ΔcGE(pH) of the dodecamer and hexamer forms of 11S globulin seem to be in close agreement and can be approximated by a single straight line (Figure 5.1d). According to the two-state model (Ptitsyn and Birstein 1967), a slope of this line is directly linked to a change in the number of the protein-bound protons upon denaturation, ΔdνH+: 1 ⎛ ∂Δ d G E ⎞ H+ ⎜⎝ ⎟⎠ = 2.303 × Δ d ν RT ∂pH T

(5.3)

In the case of 11S globulin, ΔdνH+ ≅ 3 per each constituent polypeptide chain. This value is close to the similar estimates for small globular proteins (Tanford 1968, 1970; Nicoli and Benedek 1976). The origin of the denaturation proton adsorption by proteins at acid pH values is well-known (Tanford 1968, 1970). In the native protein

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molecule, there are often abnormal carboxyl groups with pKa ≅ 1.5 (Nicoli and Benedek 1976) localized in the nonpolar interior of the molecule. Their ionized state is stabilized by hydrogen bonds with neighboring residues of tyrosine, lysine, or histidine. Several of these bonds decrease with decreasing pH, which leads to a decrease in the conformational stability of the protein globule. In the course of the denaturation, the hydrogen bonds tyrosyl (histidyl)-carboxylate are broken, and the abnormal carboxyl groups become normal groups with pKa 3 to 4. They turn to the nonionized state by the adsorption of protons. Typically, several of the abnormal carboxyl groups in the protein molecule are rather small; for example, they do not exceed 2 to 3 for some small globular proteins (Nicoli and Benedek 1976). The considered approach was used to analyze the pH effects on the stability of a number of other oligomeric and small food proteins, such as ribulose 1,5 biphosphate carboxylase (RBPC) of alfalfa (Burova et al. 1989B), tobacco (Burova et al. 1991), and other green leaves; 7S globulin of French beans (Burova et al. 1989b, 1992); soybean trypsin Kunitz inhibitor (Varfolomeeva et al. 1989; Burova et al. 1990; Grinberg et al. 2000); and porcine β-lactoglobulin (Burova et al. 2002a). The 7S globulin of French beans, phaseolin, is an oligomeric storage protein with the molecular weight of about 150 kDa. It contains three subunits (Paaren et al. 1987). Each subunit involves two domains (Lawrence et al. 1990). The thermal denaturation of 7S globulin was studied in the range of pH 2.0 to 10.9 (Burova et al. 1992). The quaternary structure of the protein is stable within this pH range; however the denaturation thermogram of phaseolin has a complex profile. In addition to the main heat capacity peak, there is a lower temperature shoulder. The thermogram recalculated per polypeptide chain can be deconvoluted into two independent two-state transitions that represent unfolding of the domains of phaseolin. Dependences of the denaturation temperature and enthalpy of both domains on pH pass through a maximum at pH 5.4. The latter corresponds to the isoelectric point of phaseolin. For both domains, the temperature dependences of the denaturation enthalpies strictly follow the Kirchhoff ’s law. The excess denaturation free energies of the domains are maximal in the vicinity of the isoelectric point of phaseolin. The analysis of the excess denaturation free energies of the domains as a function of pH showed that there are at least two types of the side-chain hydrogen bonds:

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tyrosyl-carboxylate and histidyl-carboxylate. There are six bonds in the high-temperature domain and four in the “low-temperature” domain. All hydrogen bonds are presumably localized in the hydrophobic interiors of the domains. The Kunitz inhibitor (KI) is one of several trypsin inhibitors of soybean (Kunitz 1947; Koshiyama et al. 1981). It is a small globular protein with the molecular weight of 21.5 kDa (Wu and Scheraga 1962; Koide and Ikenaka 1973) belonging to globulins. Its polypeptide chain consists of 181 amino acid residues and contains two disulfide bonds. A remarkable feature of this protein is a very low rate of the thermal denaturation (Kunitz 1948). For example at 45 °C, a time of the half– conversion of the denaturation process is about 6 h, that is, of 1 to 2 orders of magnitude longer than that of other globular proteins (Joly 1965). The thermal denaturation of KI was studied in the range of pH 2–12 (Varfolomeeva et al. 1989; Burova et al. 1990; Grinberg et al. 2000). Due to the slow denaturation rate of KI, the heating rate affects calorimetric curves within a wide region of pH values. An increase in the heating rate from 0.01 up to 2.0 K min−1 increases the apparent denaturation temperature by about 20 °C without any significant changes in the denaturation enthalpy and heat capacity increment. It is important that an increment of the apparent denaturation temperature induced by pH alteration does not depend on the heating rate. This allows one to calculate the denaturation free energy of KI as a function of pH. The dependence of the true thermodynamic denaturation temperature on pH has a rather wide plateau at pH 4.4–8.0 and rapidly decreases at both lower and higher pH values. It is important that a point of the maximal conformational stability of KI (about pH 7) does not coincide with its isoelectric point (pH 4.5). It means that a contribution of electrostatic effects to the conformational stability of KI is rather small. The pH dependence of the excess denaturation free energy of KI shows that the native protein molecule contains two pH-sensitive side-chain hydrogen bonds of the tyrosyl-carboxylate and lysylcarboxylate types. Bovine β-lactoglobulin is one of the most important milk proteins (Hambling et al. 1992). Its thermal behavior, including the thermal denaturation and postdenaturation aggregation, is of significance for many food technologies (Relkin 1996; Holt 2000). The presence of the free thiol (Cys121) is a structural feature of bovine β-lactoglobulin. This functional group of high reactivity to thiol-disulfide exchange

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becomes accessible in the course of the thermal denaturation and induces the postdenaturation aggregation (Burova et al. 1998). Porcine β-lactoglobulin is a close homolog of bovine β-lactoglobulin (66% identity of their primary structures), but it does not contain a free thiol group (Hoedemaeker et al. 2002). It was therefore of interest to compare the thermal denaturation behavior of porcine and bovine β-lactoglobulins (Burova et al. 2002a). The thermal denaturation of porcine β-lactoglobulin is reversible at pH 2–10, while that of bovine β-lactoglobulin is reversible only below pH 3.5. This difference supports the assumed postdenaturation aggregation of β-lactoglobulin initiated by the free accessible thiol in the unfolded protein. The aggregation is responsible for irreversibility of the thermal denaturation of bovine β-lactoglobulin. The denaturation temperature and enthalpy of porcine β-lactoglobulin are maximal at a pH of about 6.5. With increasing or decreasing pH relative to pH 6.5, both denaturation parameters decrease, and more rapidly in the acid region. The maximal stability of bovine β-lactoglobulin coincides with its isoelectric point (pH ∼4.5), diminishes upon increase and decrease in pH values, and does so more rapidly in the alkaline region. Thus the denaturation parameters of bovine β-lactoglobulin exceed the denaturation parameters of porcine β-lactoglobulin in the acid region and are significantly lower within the alkaline region. The analysis of pH dependence of the excess denaturation free energy of both proteins shows their considerable difference in the conformational stability. The latter seems to reflect the different role of carboxyl groups in the formation of pH-sensitive hydrogen bonds stabilizing the native proteins. These carboxyl groups are proton donors in bovine β-lactoglobulin and proton acceptors in porcine β-lactoglobulin.

Effects of Salts on Thermal Denaturation of Food Proteins Normally, the effects of salts on thermal denaturation of food protein corresponds to the Hoffmeister lyotropic series (Danilenko et al. 1986a; Yamasaki et al. 1991; Komsa-Penkova et al. 1996; Pico 1996; Kim et al. 2004). The kosmotropic salts (“salting-out” salts) increase the denaturation temperature of protein, whereas chaotropic salts (“salting-in” salts) decrease it.

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Effects of neutral salts on the protein thermal denaturation were studied using 11S globulin of broad beans as an example (Danilenko et al. 1986A). Figure 5.2a shows thermograms of the 11S globulin at different NaCl concentrations. When the salt concentration increases, the denaturation heat capacity peak shifts to higher temperatures, increases in height, and becomes narrower. Both the denaturation temperature and enthalpy increase with increase in the salt concentration (Figure 5.2b). There is a strict linear correlation between values of the denaturation enthalpy and the denaturation temperature obtained at different salt concentrations (Figure 5.2c). Its slope, 0.42 ± 0.02 J g−1 K−1, agrees well with the denaturation heat capacity increment, 0.40 ± 0.02 J g−1 K−1, determined directly in accordance with the Kirchhoff ’s law. A general criterion of salt effects on protein denaturation is the excess denaturation free energy: Δ s G E (Cs ) = Δ d G (T0 , Cs ) − Δ d G (T0 , 0 )

(5.4)

where Cs is the salt concentration and T0 is the reference temperature. It can be calculated as a function of the salt concentration at the reference temperature by Equation 5.1 using experimental values of the denaturation temperature, enthalpy, and heat capacity increment. The excess denaturation free energy of 11S globulin is plotted against the NaCl concentration in Figure 5.2d. It is positive and increases with increasing salt concentration. Thus the salt enhances the conformational stability of 11S globulin. The salt-induced changes in the conformational stability of 11S globulin are determined by the effect of screening of electrostatic interactions of surface charges of the native (N) form of the protein Figure 5.2. Thermal denaturation of 11S broad bean globulin in the presence of sodium chloride at pH 7.6. (a) Thermograms at the different salt concentrations, Cs. (b) Denaturation temperature and enthalpy versus the salt concentration. (c) Correlation between values of the denaturation enthalpy and the denaturation temperature obtained at the different salt concentrations, according to Kirchhoff ’s law. The slope of the correlation line coincides with the denaturation heat capacity increment, ΔdCp, determined directly. (d) Excess denaturation free energy per constituent chain of the protein versus NaCl concentration at temperature T0 = 352 K. The solid line curve is calculated by a two-state model, taking into account the electrostatic screening and lyotropic effects.

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and by the lyotropic effect of the salt on the structure of water. The excess denaturation free energy can be determined as a sum of contributions of these two effects: Δ d G E (Cs ) = Δ d GeE (Cs ) + Δ d GsE (Cs )

(5.5)

where Δ d GeE (Cs ) and Δ d GsE (Cs ) are the contributions of the electrostatic and lyotropic effects, respectively. The contribution of the electrostatic effect can be expressed in the Debye-Hückel approximation (Tanford 1965): Δ d GeE (Cs ) = RT × Be [ F (κ ) − F (κ 0 )]

(5.6)

where F (κ ) =

n κ × 2 1 + RNκ

(5.7)

and Be is the electrostatic interaction parameter; n = 12 is the number of constituent chains of 11S globulin; κ = κ(Cs) and κ0 = κ(Cs = 0) are the values of the Debye-Hückel parameter; RN ≅ 5 nm is the molecular radius of 11S globulin. The contribution of the lyotropic effect can be considered in the form (Schellman 1978) equivalent to the Setschenow equation (Setschenow 1889): Δ d GsE = RT × Δ d BsCs

(5.8)

where ΔdBs is the difference in the second virial coefficients of the unfolded (D) and native (N) forms for salt-protein interactions. Equations 5.5 to 5.8 describe well the dependences of the excess denaturation free energy of 11S globulin on salt concentration for NaCl, KCl and (NH4)2SO4 (for example, see Figure 5.2d). The determined parameters Be and ΔdBs are presented in Table 5.1. The values of the parameter Be correspond to an apparent 11S globulin charge of 4–6 proton units per constituent chain. Note that many globular proteins carry about the same charge in the vicinity of the isoelectric point (Tanford 1965). The parameter Be for (NH4)2SO4 is larger than that for NaCl. It reflects an increase in the protein charge due to more strong binding of sulfate anions to the protein (Record et al. 1978).

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Table 5.1. Salt-protein interaction parameters Be and ΔdBs for 11S globulin from broad beans. ΔdBs, L mol−1 Salt NaCl KCl (NH4)2SO4

106Be, cm

Experimental

Group contribution method

MelanderHorvath theory

1.2 1.2 2.3

8.2 8.2 10.6

8.4 — —

8.8 7.8 11.0

According to the obtained ΔdBs values, the salts can form a series (NH4)2SO4 >> NaCl = KCl in agreement with general features of the lyotropic effect (von Hippel and Schleich 1969). It is possible to calculate the parameter ΔdBs by the group contribution method (Nandi and Robinson 1972) and the Melander-Horvath theory (Melander and Horvath 1977). In both cases, the theoretical estimates of the parameter ΔdBs of 11S globulin are very close to its experimental values (Table 5.1).

Effects of Alcohols on Thermal Denaturation of Food Proteins Effects of alcohols on thermal denaturation of food proteins can be of importance for food technology, specifically, for control of functional properties of food proteins (Grozav et al. 1985). Generally, alcohol can decrease the denaturation temperature and, consequently, the protein conformational stability (Grozav et al. 1985; Danilenko et al. 1986b; Stepuro et al. 1991; Cinelli et al. 1997; Grinberg et al. 1998; van Koningsveld et al. 2002; Michnik 2007). It is, however, noteworthy that an extrapolation to low temperatures of calorimetric data on thermal protein denaturation reveals that the presence of low alcohol concentrations can stabilize the native conformation of protein (Danilenko et al. 1986B). This effect can be undoubtedly of interest for food storage. The most detailed information about alcohol effects on thermal denaturation of food proteins was obtained for the 11S globulin from broad beans (Danilenko et al. 1986b). In aqueous ethanol solutions, the denaturation heat capacity peak of 11S globulin decreases, broadens, and moves to lower temperatures

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(Figure 5.3a). A new, small high-temperature peak is resolved at the ethanol concentrations higher than 0.5 M. It can be assumed that this peak is associated with a molten globule-coil transition, since it has shown a significant increase in lifetime of the molten globule structure of proteins in the presence of alcohols (Biringer and Fink 1982; Burova et al. 2000). The denaturation temperature and enthalpy decrease linearly with the ethanol concentration (Figure 5.3b). The denaturation heat capacity increment does not depend practically on the ethanol concentration. Its average value amounts to 0.31 ± 0.02 J g−1 K−1. The dependence of the denaturation enthalpy on the denaturation temperature is generally in accordance with the Kirchhoff ’s law (Figure 5.3c). Figure 5.3d shows the dependence of the excess denaturation free energy of 11S globulin on the ethanol concentration. It can be approximated by a linear function that is convenient to represent in a form equivalent to the Setschenow equation: Δ d G E = RT × Δ d BAC A

(5.9)

where ΔdBA is the difference in the second virial coefficients of the D and N forms of the protein for alcohol-protein interactions and CA is the molar ethanol concentration. Here, ΔdBA = −3.46 ± 0.06 L mol−1. Hence, ethanol considerably decreases the conformational stability of 11S globulin in the temperature range, where the thermal denaturation can be observed.

Figure 5.3. Thermal denaturation of 11S broad bean globulin in the presence of ethanol at pH 7.6. (a) Thermograms at the different alcohol concentrations, CA. (b) Denaturation temperature and enthalpy versus the alcohol concentration. (c) Correlation between values of the denaturation enthalpy and the denaturation temperature obtained at the different alcohol concentrations according to Kirchhoff ’s law. The solid line corresponds to the average value of the experimental denaturation heat capacity increment, ΔdCp = 0.31 ± 0.02 J g−1 K−1. The dashed lines represent a prediction range of the correlation. (d) Excess denaturation free energy per constituent chain of the protein versus ethanol concentration at temperature T0 = 352 K. The line is calculated by a two-state model using the linear approximation of alcohol-protein interactions in terms of the denaturation increment of the second virial coefficient, ΔdBA. The insert gives a correlation between values of ΔdBA determined experimentally and those calculated by an additive scheme based on independent group contributions of amino acids for lysozyme, ribonuclease, and 11S broad bean globulin. The correlation coefficient is more than 0.999.

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Effects of Odorants on Thermal Denaturation of Food Proteins Binding of odorants to proteins is one of the key factors of food flavoring. Normally, binding of odorant is due to its hydrophobic interactions with the accessible apolar groups of the protein. Consequently, a close link should exist between the odorant affinity to the protein and the conformational state of the protein. Odorant-protein interactions could therefore be studied using the effect of odorant binding on thermal denaturation of proteins (Burova et al. 1999, 2003; Grinberg et al. 2002). Figure 5.4a shows that vanillin decreases, broadens, and moves the denaturation heat capacity peak of ovalbumin to lower temperatures (Grinberg et al. 2002). The denaturation temperature and enthalpy of the protein decrease linearly with the vanillin concentration (Figure 5.4b). Since the thermal denaturation of ovalbumin is a nonequilibrium process, its denaturation parameters determined by DSC depend on the heating rate. The kinetic factor complicates interpretation of the calorimetric data on the ovalbumin thermal denaturation. Nevertheless, Figure 5.4c shows that there is a good linear correlation between values of the denaturation enthalpy and the denaturation temperature determined at different heating rates, pH values, and vanillin concentrations. Its slope is close to the denaturation heat capacity increment of ovalbumin (Sochava and Smirnova 1993). This is a rare example of validity of the thermodynamic Kirchhoff ’s law concerning the nonequilibrium data.

Figure 5.4. Thermal denaturation of ovalbumin in the presence of vanillin. (a) Thermograms at the different vanillin concentrations, L, at pH 6.7. (b) Denaturation temperature and enthalpy versus vanillin concentration at pH 6.7. (c) Generalized correlation between values of the denaturation enthalpy and the denaturation temperature obtained at different vanillin concentrations (0–75 mM), heating rates (0.125– 2.0 K min−1), and pH values (pH 3.0 and pH 6.7). The slope of the correlation line, 0.43 ± 0.1 J g−1 K−1, is close to the denaturation heat capacity increment of ovalbumin (Sochava and Smirnova 1993). The dashed lines represent a prediction range of the correlation. (d) Excess denaturation free energy of ovalbumin versus the vanillin concentration at pH 3.0 (T0 = 333.4 K) and pH 6.7 (T0 = 351.2 K). The solid line curves are calculated by a model of ligand binding to polymer matrix with independent identical sites at the following binding parameter values: the denaturation increment of the number of binding sites, Δdν = 3.0 ± 0.1 (pH 3.0) and 20.0 ± 0.1 (pH 6.7); the binding constant, Kb = 32.9 ± 1.3 M−1 (pH 3.0) and 5.3 ± 0.1 M−1 (pH 6.7).

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The Lumry-Eyring model (Lumry and Eyring 1954) can be applied for estimation of equilibrium values of the denaturation temperature and enthalpy of ovalbumin at different vanillin concentrations. Using Equation 5.1, these data can be converted into the dependence of excess denaturation free energy on the vanillin concentrations, ΔdGE(L) (Figure 5.4d). According to the model of ligand binding to identical independent sites, this dependence has a form (Schellman 1975): Δ d G E = − RT × Δ d n ln (1 + K b L )

(5.10)

where Δdn is the denaturation increment of the binding site number; Kb is the binding constant; L is the free ligand concentration that is approximately equal to the total ligand concentration at an excess of the ligand. This equation fits well to the experimental dependence of the excess denaturation free energy of ovalbumin on the vanillin concentration at the following values of the binding parameters: Δdn = 20.0 ± 0.1; Kb = 5.3 ± 0.1 M−1 (pH 6.7), and Δdn = 3.0 ± 0.1; Kb = 32.9 ± 1.3 M−1 (pH 3.0) (Figure 5.4d). A positive value of the parameter Δdn means an increase in the number of binding sites caused by the denaturation and also indicates that vanillin binds preferentially to the unfolded D form of the protein. The relative low value of this parameter at pH 3.0 could, however, indicate a noticeable binding of vanillin to the N form of the protein. This may possibly be due to the conformational transition of ovalbumin into a molten globule-like state at the acid pH values (Tatsumi and Hirose 1997). The vanillin binding to ovalbumin is nonspecific because of the extremely low values of the binding constants. Apparently, it can be liken to solubilization of apolar compounds by surfactant micelles. In contrast to ovalbumin, bovine serum albumin (BSA) binds the odorants, vanillin, and octanone at pH 6.4 preferentially in the native state (Burova et al. 2003). Therefore, the denaturation temperature and enthalpy, and hence the free energy of the protein, increase upon binding of these ligands. Analysis of the experimental dependence of the excess denaturation free energy of BSA on the vanillin or octanone concentration by Equation 5.10 shows that the native form of the protein carries two to three sites of strong binding of these odorants with the binding constants of about 103 M−1. These estimates agree well with the results of direct determination of the binding parameters using the data of equilibrium dialysis.

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Effects of Polysaccharides on Thermal Denaturation of Food Proteins Biopolymers, polysaccharides in particular, are the most widespread food ingredients that can crucially modify functional properties of proteins and foods. Accordingly, the effects of polysaccharides on functional properties of food proteins were intensively studied (Burova et al. 1992; Delben and Stefancich 1998; Baeza and Pilosof 2002; Burova et al. 2002b; Zhang et al. 2004; Ibanoglu 2005; Ibanoglu and Ercelebi 2007). A large variety of partially contradictory data were described, however. These contradictory data are due to the fact that the interaction of polysaccharides with food proteins greatly depends on the polysaccharide nature (charged or neutral) and on the solution conditions, such as pH, salt, and polymer concentration. For example, polysaccharides can be either thermodynamically incompatible or able to form soluble and insoluble complexes with proteins (Grinberg and Tolstoguzov 1997). We consider effects of polysaccharides on the conformational stability of proteins under conditions of both thermodynamic incompatibility and complexation of proteins with polysaccharides. The thermal denaturation of 11S globulin from broad beans was investigated in the presence of various anionic (carboxyl- and sulfatecontaining) and neutral polysaccharides (Burova et al. 1992). It has been shown that 11S globulin forms polyelectrolyte complexes with anionic polysaccharides (both carboxyl- and sulfate-containing) at pH below the protein isoelectric point (pI 4.8). In neutral and weakly basic medium (pH 7.6) at low salt concentration (0.01 M NaCl) 11S globulin is incompatible with the neutral and carboxyl-containing polysaccharides. It is however able to form noncooperative complexes with the sulfate-containing polysaccharides. At higher salt concentration (0.4 M NaCl), the complexation is suppressed, and 11S globulin becomes incompatible with the sulfate-containing polysaccharide. Table 5.2 represents the denaturation temperature of 11S globulin in the presence of polysaccharides at pH 7.6 and pH 4.2. It is noteworthy that under these conditions the thermograms of 11S globulin alone have a single denaturation peak. In the presence of polysaccharides, two different situations are observed. First, the denaturation peak (peak 1) is close to that of free 11S globulin. Second, there is an additional peak (peak 2) that is shifted to lower temperatures relative to peak 1.

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Table 5.2. Denaturation temperature of 11S globulin from broad beans (°C) in the presence of polysaccharides.* pH 7.6; q = 20

Polysaccharide Dextran Sodium alginate Pectin Carboxymethylcellulose Methyl cellulose Arabic gum Dextran sulfate ı-Carrageenan κ-Carrageenan Free protein

0.01 M NaCl

0.4 M NaCl

Peak 1

Peak 2

Peak 1 Peak 2 Peak 1 Peak 2

74.9 75.1 74.3 75.4 74 74.3 76.3 77.9 76.4 76.0

— — — — — — 63.0 61.0 64.0 —

— — — — — — 90.3 90.8 93.1 93.0

— — — — — — — — — —

pH 4.2; q = 1

— — — — — — — — — 77.0

— 59.0 62.0 — — — 50.5 — — —

* q is the protein/polysaccharide weight ratio.

Table 5.2 shows that the polysaccharides incompatible with 11S globulin (pH 7.6; 0.01 M NaCl), such as dextran, alginate, pectin, methyland carboxymethylcellulose, and Arabic gum, do not affect the thermal denaturation of the protein. Under the same conditions, the sulfatecontaining polysaccharides can form complexes with 11S globulin. The result is a destabilized form of 11S globulin coexisting with the free protein. When the complexation is inhibited by 0.4 M NaCl, the peak 2 disappears, and only the peak 1, corresponding to the denaturation of the free 11S globulin, remains in the thermogram. These data imply that the 11S globulin-polysaccharide incompatibility does not significantly affect the protein unfolding. On the contrary, the 11S globulin-polysaccharide complexation results in the destabilization of the protein. At pH 4.2 11S globulin is able to form cooperative electrostatic complexes with anionic polysaccharides, both sulfate- and carboxylcontaining. Under these conditions in the presence of alginate, pectin, and dextran sulfate, the thermograms of 11S globulin have a single denaturation peak (peak 2) at temperatures well below the denaturation temperature of the free 11S globulin (Table 5.2). No free protein (peak

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107

1) was observed in studied systems. Consequently, 11S globulin bound to the polysaccharide matrix is substantially destabilized. The effects of incompatibility and complexation of various polysaccharides on the thermal denaturation and renaturation of a small globular protein from soybean seeds (the Kunitz trypsin inhibitor, KI) were investigated (Burova et al. 2002b). It was shown that such polysaccharides as dextran, pectin, Arabic gum, dextran sulfate, and ı- and κ-carrageenans do not affect the denaturation parameters of KI under conditions of the protein-polysaccharide incompatibility (pH 8, 0.1 M NaCl). Variation of the protein/polysaccharide weight ratio from 0.01 to 20 did not change this tendency. The effects of interpolyelectrolyte complex formation of KI with anionic polysaccharides (dextran sulfate, pectin) upon the denaturation of the protein were studied at pH 3.0. Figure 5.5a shows the denaturation thermograms of KI in the presence of dextran sulfate at different values of the protein/polysaccharide weight ratio, q. With increasing content of the protein, the denaturation peak is shifted to the lower temperatures at small q and moves back at higher q. According to the velocity sedimentation data, the composition of the system was characterized by two components (Figure 5.5b). The sedimentation coefficient of component 1 exceeded that of the free KI (2S). It increased abruptly as the parameter q increased. Obviously, component 1 corresponded to the KI-dextran sulfate complex. The sedimentation coefficient of component 2 did not depend significantly on the parameter q and was approximately equal to 3S. It may correspond either to “light” complexes of approximately constant composition (i.e., having a relatively low content of the bound protein) or to the free dextran sulfate that was characterized by the sedimentation coefficient of 2S. It is important that at high q values free KI could not be detected in the system. The result implied that the denaturation transition observed calorimetrically (Figure 5.5a) could be attributed to the protein bound to the polysaccharide. The denaturation temperature and enthalpy of KI in complexes depended on the parameter q (Figure 5.5c). At low protein content, the denaturation temperature of KI was about 15 °C lower than that of the free protein. When the protein concentration increased, the denaturation temperature of KI raised monotonically and at high protein concentrations reached the value close to the denaturation temperature of free KI. The denaturation enthalpy of KI in complexes with dextran

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Thermal Analysis of Denaturation and Aggregation

109

sulfate was lower than that of the free protein in the whole range of q values and decreased slightly with increasing q values (Figure 5.5c). Let us analyze possible mechanisms responsible for changes in conformational stability of a protein bound to a polymer matrix. The protein stability to denaturation is determined by the free energy of denaturation, ΔdG, which is the difference between free energies of the protein in unfolded, GD, and native, GN, form: ΔdG = GD − GN. When N or D forms of the protein are bound to a matrix, the free energy of each form decreases by a value of the free energy of binding, ΔbGN or ΔbGD, respectively. Reasonably, the free energy of binding depends on the number of contacts formed by the protein upon its fixation on the matrix. As a rule, the D form of a protein possesses a higher number of accessible binding sites than the N form, consequently ΔbGD Keq, most of denatured proteins can be converted irreversibly into aggregates and the thermal behavior of the system is kinetically controlled by the rate-limiting denaturation step reaction. Following this mechanism, the decrease in QD (apparent heat of reaction) and increase in Tmax (major peak temperature) observed from solutions containing increased dry matter compositions (of which 2% to 25% protein) could be explained by increasing values of the equilibrium constant (Keq) and increased con-

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Calorimetry in Food Processing 24

74

Peak temperature, °C

72

20 18

70

16 68

14 12

66

0

5

10 15 20 % Protein concentration

25

Apparent heat of denaturation, J·g–1

22

10 30

Figure 6.3. Variations of peak temperatures (Tmax in °C) and heat of reaction (Qcal in J.g−1) obtained from solutions of whey proteins at the indicated concentrations (pH 6.6, 0.1 °C.min−1).

centration of denatured states (shift to the right of the equilibrium reaction). Similar trends were observed for globular protein solutions at a pH close to the protein isoelectric pH or in the presence of added salts for which the net protein surface charge is either close to zero or screened by electrolyte opposite charges, respectively (Relkin and Launay 1990; Relkin 1996; Fiszman, Lluch, and Salvador 1999). In both of these cases, peak temperature could increase and QD decreases. Aggregation and denaturation mechanisms involving proteinprotein interactions (exothermic reaction) and breaking down of internal low-energy forces between amino acids (endothermic reaction) can be superimposed in the same temperature range. This may explain the overall trends in the calorimetric energy, Qcal, and temperatures, Tmax, as due to denaturation-aggregation changes during the DSC runs. Thus, among the heat-induced reactions occurring in globular protein solutions, aggregation and denaturation mechanisms may be overlapped, leading to either one single endothermic curve at scan rate < 0.5 °C.min−1 for protein concentrations up to 25%, or to successive endothermic and exothermic reactions, which became distinguishable at higher heating rates. At neutral pH, whey proteins are negatively charged, and increasing the protein and salt concentrations may favor

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129

interaction properties between proteins as they denature during the DSC run. Thus, dissociation of noncovalently bound protein aggregates and their unfolding mechanism followed by irreversible aggregation could explain the increase in peak temperature in parallel with the decrease in the apparent heat of reaction with increasing protein concentration. Thermodynamics and Kinetics of Heat-Induced Transformations If the heat involved in the aggregation step D ⇒ A is much lower compared to that of the first denaturation step N ⇔ D, then the calorimetric heat of reaction could be very close to that of the enthalpy change value of denaturation. In an earlier study, the activation energy of protein heat-induced denaturation was determined by using peak temperature values obtained from thermograms registered at different scan rates or by using partial completion of DSC reaction as a function of temperature (Relkin and Launay 1990). The activation energy (kJ. mol−1) of heat-induced denaturation of whey protein varied with protein concentration (400 < EA < 550 kJ.mol−1 for protein concentrations ranging between 3.5% and 24%). Considering the effects of protein concentration on Tmax, Qcal, and EA, it was suggested that the reaction mechanism of denaturation may involve a fast initial step of partial dissociation-unfolding, followed by a slow interchain hydrophobic reaction. In another study (Sanchez-Ruiz 1992), the recording of thermograms at different scan rates (β) and corresponding Tmax values were applied to the Lumry-Eyring model (Lumry and Eyring 1954) for evaluation of the activation energy (EA) and pre-exponential factor (Z) using the following relation: ZR ⎞ EA β ⎞ = ln ⎛ − ln ⎛ ⎝ Tmax ⎠ ⎝ E A ⎠ RTmax

(6.5)

Activation enthalpy (ΔH#) and entropy (ΔS#) were deduced from the following relations: ΔH # = E A − RT

(6.6)

ΔS # = ln( Zh ) − ln(ekbT ) R

(6.7)

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where, kB is the Boltzmann constant (1.38 10−23 J.K−1), T temperature in K, and h is the Planck constant (6.62 10−34 J.s). Plots of ln (β/Tmax) = f(1000/Tmax) shown in Figure 6.4 were obtained by application of Equation 6.5 to Tmax recorded from a protein solution (5.15%; pH 6.6) using a small volume sample (45 μL) and scanning rates ranging from 15 °C.min−1 to 2.5 °C.min−1 (curve a), or using a large volume sample (750 μL) and scanning rates ranging from 1 °C.min−1 to 0.1 °C.min−1 (curve b). By using the same protein solution but two different calorimeters (classical DSC working at high scan rates and small volume pans or highly-sensitive DSC working at low scan rates and large-volume vessels), EA (activation energy) values (Table 6.1) deduced from the slope of the linear parts of these two representations were very similar. However, the differences between the calorimetric parameter, Qcal (determined from the surface area under the transition peak), and the thermodynamic parameter, ΔH# (deduced from Equations 6.5 and 6.6), were very much lower when evaluated from DSC measurements at scanning rates ranging between 0.1 ≤ β ≤ 1 °C.min−1 than between 2.5 ≤ β ≤ 15 °C.min−1. Following the Lumry-Eyring theory, –3

ln (β/Tmax)

–4

–5

–6

–7

–8 2.86

2.88

2.9 1000/Tmax

2.92

2.94

Figure 6.4. Lumry-Eyring representation obtained from protein solutions (4.15%, pH 6.6) at two different ranges of scanning rates: classical DSC working in a high scan rate range (empty circles), and highly sensitive DSC working in a low scan rate range (filled circles). See text.

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Table 6.1. Examples of calorimetric (Tmax, Qcal) and thermodynamic (EA, ΔH#) parameters, deduced from DSC heating curves obtained from a protein solution (4.15% concentration, pH 6.6) using two weight samples and two ranges of scanning rates. Lumry-Eyring theory was applied to DSC curves obtained at the indicated scan rates, and activation energy values, EA, were calculated from the linear part of plots in Fig. 5.4 (see text). Sample weight (mg) 750 45

dt/dT (°C.min−1)

Tmax (°C)

Qcal (kJ.mol−11)

EA (kJ.mol−11)

ΔH# (kJ.mol−11)

0.1 5

68.2 76.3

283 232

342 345

340 342

these differences could indicate differences in heat-induced conformation transitions and reaction mechanisms, depending on the heating rate and constant rate of reactions, as suggested previously (Relkin and Launay 1990; Sanchez-Ruiz 1992; Relkin 2004). Milk proteins are composed by approximately 80% caseins in micelle form and 20% whey proteins, of which half are β-lactoglobulin. The DSC curves (1 °C.min−1) in Figure 6.5 were obtained from solutions in simulated milk ultrafiltrate (SMUF, pH 6.6) of a whey protein isolate, alone or in mixture with 20% casein or 40% casein at 5.3% total protein concentration. These curves showed the presence of an exothermic reaction occurring at T > 80 °C. Partial replacement of whey proteins by 20% or 40% casein micelles gave DSC curves composed of a major endothermic peak at Tmax, accompanied by an exothermic effect at T > Tmax. The intensity of the exothermic event seems to increase, whereas Tmax seems to decrease with increasing casein-to-whey protein weight ratio. The lowering of Tmax with increased proportion of casein to whey proteins could be explained by an increase in the rate of irreversible aggregation mechanism between casein and unfolded whey proteins. Upon heating, some of the hydrophilic interactions (hydrogen bonds, van der Waals interactions, electrostatic interactions between charged groups, specific binding) are weakened, whereas some of the hydrophobic amino acids (initially buried in the interior core of whey proteins) become more exposed at the surface. In the example of Figure 6.1 (2% protein in water; β > 0.5 °C.min−1), the exothermic signal is shown at T < Tmax, whereas in the example of Figure 6.5 (5.3% protein

132

Calorimetry in Food Processing Heat flow (mW)

3.5 3

(c)

2.5

(b)

2

(a)

1.5 1 40

60 80 Temperature (°C)

Figure 6.5. Heating curves obtained from solutions of whey proteins, alone (a), or in mixtures with either 20% casein (b), or 40% casein (c). 5.3% total protein, pH 6.6 in simulated milk ultrafiltrate, 1 °C.min−1.

in SMUF, β = 1 °C.min−1) it is seen at T > Tmax. This difference may be due to the presence of lactose, which increases the protein resistance to heat-induced denaturation (Park and Lund 1984; Ruegg, Morr, and Blanc 1987).

Heat-Induced Transformations in Oil-in-Water Emulsions Crystallization and Melting of Fat Droplets Oil-in-water emulsions are constituted by a dispersing aqueous medium, oil-water interface, and dispersed fat droplets (Dickinson 1992; Dalgleish 1996; Walstra 1998). Globular proteins, due to their amphiphilic (polar/nonpolar) nature and their marginal conformational stability, may adsorb from aqueous solutions to solid surfaces and fluid-fluid interfaces. They act as surfactants by reducing the interfacial tension and forming a cohesive film (Walstra 1988; Dickinson 1997; Sourdet et al. 2002). Denatured proteins, compared with “native” proteins, which are characterized by a less-ordered structure related to higher flexibility and surface hydrophobic index (due to a greater exposure to the aqueous medium of initially buried hydrophobic), were shown to accommodate easier to oil-solution interfaces. Monitoring heat-induced transformations of fat droplets in oil-inwater emulsions as a function of their composition is of great technological interest in relevance to their physical stability against coalescence (Walstra and van Beresteyn 1975; Boode Walstra and de Groot-Mostert 1993; Relkin and Sourdet 2005). In the formulation of many oil-in-

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133

water food emulsions, proteins are used in combination with smallmolecular-weight emulsifiers (surfactants) and polysaccharides (Dickinson 1998). Proteins compete with surfactant molecules for the adsorption to the oil-water interface, giving appropriate interfacial properties, whereas polysaccharides are used as thickeners of the aqueous continuous phase. In addition to these parameters that have effects on colliding properties of fat droplets and their resistance to coalescence, crystallization behavior of fat droplets was shown to play a major role in stability and instability of food emulsions. Several techniques may be used to study thermal behavior of fat in bulk and emulsified phases (Dickinson and McClements 1995; Hindle, Povey, and Smith 2000; Garti and Sato 2001). Because crystallization of fat releases a large amount of heat, numerous studies were performed using DSC in nonisothermal and isothermal modes. It was shown that crystallization temperature of dispersed fat droplets is lowered, compared to bulk fat (Skoda and van den Temple 1963; Walstra and van Beresteyn 1975; Walstra, Kloeck, Vliet Ton van 2001). In addition to droplet curvature, supercooling needed to initiate crystallization of fat globules depends on several other factors, including the origin and composition of fat, adsorbed materials, and, particularly, added lipophilic or hydrophilic emulsifiers (Dickinson and McClements 1995; Garti and Sato 2001; Relkin and Sourdet 2005; Relkin et al. 2008). Besides oil-in-water food emulsions, such as sauce or mayonnaise where polyunsaturated lipids are used, there are other types of emulsions prepared from anhydrous milk fat (AMF) that are used for fabrication of dairy whipped cream or ice creams. AMF is constituted by a wide diversity of saturated and unsaturated triacylglycerols (TG), each characterized by its own melting temperature (Hartel and Kaylegian 2001). The physical properties of AMF, resulting mainly from its extraction processing and TG composition, have different temperature dependency. AMF has broad melting and crystallization temperature ranges from approximately −40 °C to 45 °C, and it may contain more than 50% crystalline fat when stored at 5 °C (refrigerator temperature). Therefore, the manufacturing process of dairy emulsions consists of successive steps. In the first step, AMF is heated above its melting temperature (50 °C), and lipophilic emulsifiers are dispersed in the lipid melt. In the second step, this lipid-melt phase is mixed by stirring with the aqueous phase, which contains water-soluble ingredients (proteins and polysaccharides). In the third step, the premix is

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passed through a high-pressure homogenizer, and the resulting emulsion is cooled down to a storage or ageing temperature (Relkin, Sourdet, and Fosseux 2003). Emulsions used to prepare dairy whipped cream or ice creams are aged at 4 °C for a time ranging from 10 to 18 h. During the aging step, in addition to complete hydration of polysaccharides and partitioning properties of surfactants and proteins between the oil-in-water interface and the aqueous continuous phase, the behavior of fat droplets and their heat-induced transformations are considered as key factors for the development of desired structures in the final product (Abd El Rahman et al. 1997; Goff 2002). DSC is used to study thermal behavior of ingredients (fat, surfactant, polysaccharide) as a function of processing parameters, and especially for evaluation of supercooling and kinetics of fat crystallization from a liquid emulsified phase. Supercooling, needed to initiate fat crystallization from melted systems can be evaluated from the cooling and re-heating DSC signals registered in a scanning mode. Examples of DSC curves obtained from AMF in bulk phase (∼20 mg) or fat droplets in protein-stabilized emulsions (∼80 mg) are shown in Figures 6.6 and 6.7. All the samples were heated to 50 °C (crystal melting) before cooling. The curves in Figures 6.6 and 6.7 were obtained from cooling and reheating cycles at 0.5 °C.min−1. In the Figure 6.7, besides the DSC cooling (a) and reheating (b) curves (0.5 °C.min−1), we present also the melting curve (c) obtained after cooling to 4 °C and holding the emulsion at this temperature for 10 h. These curves present distinguishable exothermic and endothermic events corresponding to crystal formation and melting of crystals (or polymorphs), respectively. Comparison of the temperatures of the initial scan and scans after cooling and reheating indicated a higher supercooling in emulsified samples than in bulk fat samples (Table 6.2). The shape of the melting curves obtained by reheating (0.5 °C. min−1) just after cooling at the same scan rate was very similar for all the fat samples (Figures 6.6 and 6.7). However, the shape of the crystallization curves differed depending on the fat sample (bulk- or emulsified-fat sample) and ingredient composition. Melting curves (0.5 °C. min−1) obtained after a holding step at 4 °C for 10 h applied to bulk (AMF-0 and AMF-S) or emulsified (E-0 and E-S) fat samples in the absence or presence of surfactant, respectively, show a broad endothermic curve (Figure 6.7, curve c), with Tmax (maximum peak temperature) located at around 20 °C and Qcal (apparent heat of reaction)

Heat flow, mW

AMF

endo AMF-S

0

10

20 Temperature, °C

30

40

Figure 6.6. Cooling and heating curves (0.5 °C.min−1) obtained from anhydrous milk fat alone (AMF) or with 1.75 wt% added surfactant (AMF-S).

Heat flow, mW·g–1

endo

(c)

(b) (a)

0

10

20 Temperature, °C

30

40

Figure 6.7. Cooling (a) and first reheating (b) curves, and second reheating curve (c) observed at 0.5 °C.min−1 from protein-stabilized AMF emulsion containing added surfactant (E-S). The second reheating curve (c) was registered after 10 h holding of the emulsion at 4 °C.

135

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Calorimetry in Food Processing

Table 6.2. Calorimetric parameters observed from cooling and heating thermograms (0.5 °C.min−1) obtained from anhydrous milk fat sample in the absence of added surfactant (AMF-0) or in presence of 1.75% surfactant (AMF-S), from protein-stabilized emulsion in the absence of surfactant (E-0), and in protein-stabilized emulsion in the presence of surfactant E-S. Tmax values correspond to temperature of peak maximum observed in the melting curve of emulsions, which were held at 4 °C for 10 h (see text). Sample

Tcris (°C)

Tend (°C)

Tmax (10 h) (°C)

AMF AMF-S E-0 E-S

22.4 19.7 19.9 19.1

38.0 38.0 38.0 35.5

20.6 20.6 21.1 20.2

close to 65 J.g−1. This indicates that for both bulk and emulsified fat samples there was formation of a similar amount of crystalline fat during the 10-h aging at 4 °C. Application of DSC in isothermal mode (4 °C) to the same bulk fat and emulsions led to observation of a single exothermic heat flow signal as seen in Figures 6.8 and 6.9. The maximum heat flow deviation of this exothermic peak occurs after different holding times at 4 °C. Compared with the bulk AMF-0 sample, this event seemed to occur after a similar holding period (27 min) in E-S (emulsion with surfactant). However, it seems to be anticipated for AMF-S (bulk fat in presence of surfactant) and more delayed (∼5 min) in the proteinstabilized emulsion without added surfactant. Kinetics of Fat Droplet Crystallization in Oil-in-Water Emulsions Analysis of the heat flow pattern involved during the isothermal step could be used for evaluation of crystal growth characteristics, such as the induction time and growth rate values. From the determination of the partial apparent heat of reaction at time t (calculated from the partial area under the exothermic heat flow), it is possible to obtain the fractional fat crystallization, X(t), from the following relation: X (t ) =

A(t ) Qcal

(6.8)

Endothermic heat flow, mW·g–1

AMF-0

AMF-S

0

10

40 60 Holding time at 4°C, min

80

Endothermic heat flow, mW·g–1

Figure 6.8. Isothermal curves registered at 4 °C from anhydrous milk fat alone (AMF-0) or with 1.75 wt% added surfactant (AMF-S).

E-0 2

E-S

0

20

40 60 Holding time at 4°C, min

80

Figure 6.9. Isothermal curves registered at 4 °C from protein-stabilized emulsion, without added surfactant (E-0) or with added surfactant (E-S).

137

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Calorimetry in Food Processing

where Qcal is the total calorimetric heat of reaction calculated from the area under the exothermic peak registered during the holding time. The mechanism of fat crystal growth can be described by the Avrami equation (Avrami 1939): n X ( t ) = 1 − exp ⎡⎣ −α( t ) ⎤⎦

(6.9)

or can be linearized: ln [ − ln(1 − X ( t ))] = ln(α ) + n ln( t )

(6.10)

In this expression, α represents the nucleation rate of homogeneous crystallization and n (Avrami index) represents the rate of crystal growth, with n ∼ 3 for a disklike crystal growth mechanism and n ∼ 4 for a spherulic crystal growth mechanism (Tore-Vazquez et al. 2002). Avrami plots obtained by applying Equation 6.10 to DSC data obtained for AMF-0, E-0, and E-S fat samples under isothermal condition at 4 °C are shown in Figure 6.10. They present a linear variation in a short time region, with different slope values (2.7 < n ≤ 4), suggesting different crystal growth mechanisms. Application of the Avrami model to fat crystallization is valid for a homogeneous mechanism, whereas noninteger values might suggest heterogeneous and secondary nucleation. Results in Table 6.3 could suggest a spherulic crystal growth mechanism (n ∼ 4) in fat droplets in the protein-stabilized emulsion, without added surfactant. The growth of fat crystals with a sigmoidal time variation may also be modeled using the modified Gompertz equation, as follows (Walstra, Kloeck, Vliet Ton van 2001):

{

}

eμ X( t ) = X max ∗ exp − exp ⎡⎢ max ( t ind − t ) + 1⎤⎥ ⎣ X max ⎦

(6.11)

In this model, Xmax is the asymptotic value of fractional completion of crystallization, μmax is the slope at the time when the growth of crystals becomes exponential (steepest ascent of the sigmoid curve), and tind is the induction time (intersecting this line with the t-axis). μmax and tind are adjustable parameters determined from partial integration of the heat flow signal registered by the DSC isothermal method.

Heat-Induced Phase Transformations

139

4

2

ln (–ln (1-TS))

0

–2

–4

–6

–8 1.5

2

2.5

3 ln (t), min

3.5

4

4.5

Figure 6.10. Avrami plots obtained from anhydrous milk fat alone (square symbols), from protein-stabilized emulsion without added surfactant (diamonds), or with added surfactant (circles).

Table 6.3. Kinetic parameters of fat crystallization at 4 °C in anhydrous milk fat sample (AMF-0), in protein-stabilized emulsion (E-0), without addition of surfactant, and in protein-stabilized emulsion with added surfactant (E-S). The kinetic parameters were deduced by application of Gompertz and Avrami models (see text). Gompertz model

AMF-0 E-0 E-S

Avrami index

tmax min

tinduction min

μmax min−1

n

R2

27.2 ± 2.1 32.7 ± 1.2 27.0 ± 3.0

14.2 ± 0.12 22.4 ± 0.04 13.5 ± 0.02

3.48 4.00 3.28

3.461 4.038 2.654

0.999 0.992 0.999

The experimental curve obtained from partial integration of the heat flow signal as a function of holding time and the sigmoid curve, obtained by applying Equation 6.11 to X(t), are compared in Figure 6.11. Values of tmax (maximum deviation of the exothermic DSC signal), n (Avrami index), and Gompertz parameters (values of induction time, tind, and maximum growth rate, μmax) are reported in

140

Calorimetry in Food Processing 120

50

80

60

X (t)

Endothermic heat flow, mW·g–1

100

40 20

0

20

40 60 Holding time at 4°C min–1

80

0 100

Figure 6.11 Examples of time evolution of X(t) and partial completion of crystallization reaction at 4 °C, determined from anhydrous milk fat sample (experimental values, circles) and its Gompertz representation (sigmoidal dashed curve), as deduced from the endothermic heat flow signal (see text).

Table 6.3. According to the Gompertz method, AMF in the bulk phase and in the emulsion containing milk proteins with surfactant (E473) have very close tmax (27 min) and tind values (14 min), an n value of 3.5 and 2.7, respectively, and an μmax value of 3.5 min−1 and 3.3 min−1, respectively. On the other hand, the protein-stabilized emulsion (E-0) without added surfactant exhibits a higher value of n ∼ 4 (indication of a spherulic crystal growth) and higher values of tmax, tind (delay in nucleation), and μmax (increase in the crystal growth rate). Emulsification procedure and ingredient complexity have a dominant role in characteristics of fat droplets, such as particle average diameters and size distributions, composition and physical properties of surrounding surface layers, and crystalline fat content and polymorphism (Skoda and van den Tempel 1963; Walstra 1975; McClements et al. 1993; Dickinson and McClements 1995; Kaneko et al.1999; Hindle, Povey, and Smith 2000; Relkin, Sourdet, and Fosseux 2003; Relkin and Sourdet 2005). The supercooling effect (temperature needed to initiate fat crystallization in globules) has been shown to differ depending on fat composition and mean droplet size of fat droplets and on the concentration and lipophilic or hydrophilic nature of emulsifiers

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141

(Garti and Jano 2001). D50 values (average median diameters of fat droplets) determined from laser light scattering measurements (Relkin and Sourdet 2005) were close to 3.1 μm for E-0 and E-S emulsions. However, whereas a similar supercooling was determined by DSC in scanning mode (Table 6.2), kinetic parameters (tmax, tind, μmax) and n (Avrami index) deduced from the DSC isothermal method were different (Table 6.3). These results indicate, as expected from numerous studies, that hydrophobic surfactant acts as a catalyzer for crystal nucleation in fat globules, where crystallization mechanism is considered as homogeneous.

Conclusion DSC has been used for several years to investigate heat-induced conformational or structural changes of a broad range of food ingredients (biopolymers, proteins, fats, sugars, emulsifiers) in various physicochemical conditions. Most of the previous DSC studies showed the ability of food ingredients in heat-induced structural or physical state changes, which are of great importance for manufacturing of food products with controlled structures. The examples described in this chapter indicate that combining DSC in nonisothermal and isothermal methods can provide thermodynamic and kinetic data to contribute to better understanding and control of structure-forming mechanisms in food systems.

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Privalov P.L. 1979. Stability of proteins. Small globular proteins. Adv Protein Chem, 33:167–241. Relkin P. and Launay B. 1990. Concentration effects on the kinetics of β-lactoglobulin heat denaturation: A differential scanning calorimetric study. Food Hydrocolloids, 4:19–32. Relkin P. 1996. Thermal unfolding of β-lactoglobulin, α-lactalbumin and bovine serum albumin. A thermodynamical approach. Crit Rev Food Sci Nutr, 36:565–601. Relkin P., Meylheuc T., Launay B., and Raynal K. 1998. Heat-induced gelation of globular protein mixtures. A DSC and a SEM study. J Thermal Anal, 51: 747–755. Relkin P., Hagolle N., Dalgleish D.G., and Launay B. 1999. Foam formation and stabilisation by pre-denatured ovalbumin. Colloids Surfaces B Biointerfaces, 12: 409–416. Relkin P., Sourdet S., and Fosseux P.Y. 2003. Fat crystallization in complex food emulsions. Effects of adsorbed milk proteins and of a whipping process. J Thermal Anal Cal, 71:187–195. Relkin P. 2004. Using DSC for monitoring protein conformation stability and effect of fat droplets crystallinity in complex food emulsions. In: The Nature of Biological Systems as Revealed by Thermal Analysis, D. Lırincz, editor, pp 99–126. Kluwer Academic Publishers: Londres. Relkin P. and Sourdet S. 2005. Factors affecting fat droplets aggregation in whipped frozen protein-stabilized emulsions. Food Hydrocolloids, 19:503–511. Relkin P., Bernard C., Meylheuc T., Vasseur J., and Courtois F. 2007. Production of whey protein aggregates with controlled end-use properties. Le Lait, 87:337–348. Relkin P., Yung J.M., Kalnin D., and Ollivon M. 2008. Structural behaviour of lipid droplets in protein-stabilized nano-emulsions and stability of α-tocopherol, Food Biophys, 3:163–168. Roos Y. H. 1995. Phase Transitions in Foods. Academic Press: London. Ruegg M.P., Morr U., and Blanc B. 1987. A calorimetry study of the thermal denaturation of whey proteins in simulated milk ultrafiltrate. J Dairy Res, 44:509–520. Sanchez-Ruiz J.M. 1992. Theoretical analysis of Lumry-Eyring models in differential scanning calorimetry. Biophys J, 61:921–935. Singh H. and Hevea P. 2003. Thermal denaturation, aggregation, and gelation of whey proteins. In: Advanced Dairy Chemistry, Part B, Vol. 1, P.F. Fox and P.L.H. McSweeney editors, pp. 1261–1287. Kluwer Academic Publishers: New York. Skoda W. and van den Tempel M. 1963. Crystallization of emulsified triglycerides. J Colloid Sci 18:568–584. Sourdet S., Relkin P., Aubry V., and Fosseux, P.Y. 2002. Composition of fat protein layer in complex food emulsions at various weight ratios of casein-to-whey proteins, Le Lait, 82:567–578. Sourdet S., Relkin P., and Cesar B. 2003. Effects of milk protein type and pre-heating on physical stability of whipped and frozen emulsions. Colloids Surfaces B, 31:55–64.

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Tore-Vazquez J.F., Dibildox-Alvarado E., Charo-Alonso, M.,Herea-Coronado V.,Gome-Aldapa C.A. 2002. The Avrami index and the fractal dimension in vegetable oil crystallization. J Am Oil Chem Soc, 79:855–866. Walstra P. 1988. The role of proteins in the stabilization of emulsions. In: Gums and Stabilisers for the Food Industry 4, G.O. Phillips, D.J. Wedlock, P.A. Williams, editors, pp. 323–336. IRL Press: Oxford, England. Walstra P. and van Beresteyn E.C.H. 1975. Crystallization of milk fat in the emulsified state. Netherlands Milk Dairy J, 29:35-65. Walstra P., Kloeck W., Vliet Ton van. 2001. Fat crystal network In: Crystallization Processes in Fats and Lipid Systems, N. Garti and K. Sato, editors, pp. 289. Marcel Dekker: New York. Wright D.J. 1984. Thermo-analytical methods in food research. In: Biophysical Methods in Food Research, H.W.S. Chan editor, p 1–36. Blackwell Scientific Publications: Oxford.

Chapter 7 Analysis of Foodborne Bacteria by Differential Scanning Calorimetry Michael H. Tunick, John S. Novak, Darrell O. Bayles, Jaesung Lee, and Gönül Kaletunç

Introduction C. perfringens and L. monocytogenes Analysis by DSC Sample Preparations C. perfringens Results L. monocytogenes Results Effect of Antibiotics on Bacteria E. coli and Lactobacillus plantarum Analysis by DSC Sample Preparations E. coli and L. plantarum Results Application of DSC for Evaluation of Food-Processing Treatments Determination of Heat Inactivation Parameters of Bacteria from Calorimetric Data Determination of Efficacy of Nonthermal Treatments from Calorimetric Data Determination of Impact of Antimicrobials on Bacteria from Calorimetric Data Conclusions References

147 149 149 150 152 153 155 156 156 158 158 161 163 164 164

Introduction The World Health Organization estimates that 325,000 hospitalizations and 5000 deaths result from foodborne illness in the United States each 147

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year (WHO 2007). Tens of thousands of cases of foodborne illness in the United States each year are the result of contamination by Clostridium perfringens (Mead et al. 1999), a spore-forming anaerobe that may initiate spore production in response to acidic conditions in the gastrointestinal tract (Novak, Tunick, and Juneja 2001). Illness due to Listeria monocytogenes is much less prevalent but far more serious, leading to 500 deaths in the United States annually (Mead et al. 1999). These and other foodborne pathogens can be inactivated by heat or antibiotics, which alter the efficacy of protein synthesis in ribosomes. Ribosomes, which are organelles found in the cytoplasm of all cells, assemble amino acids into proteins by using the directions supplied by messenger RNA molecules (Borman 2007). In bacteria, ribosomes consist of a small 30S subunit and a large 50S subunit about twice the size of the smaller subunit, which fit together to form the 70S ribosome. An Escherichia coli cell contains thousands of ribosomes, each made up of three RNA components and over 50 proteins weighing 2.5 × 106 Da (Borman 2007). Stressing microorganisms at relatively high or low temperatures, known as heat shocking or cold shocking, decreases their thermal tolerance by impairing the 30S subunit (Stephens and Jones 1993). This decreased thermal tolerance can be measured by determining the microorganism’s D60 value, which is the length of time required for the viable population to decrease 10-fold at 60 °C. About 35%–40% of the mass of the ribosome consists of proteins, which are analyzable by differential scanning calorimeter (DSC) if the sample is sufficiently concentrated. Ribosomal proteins are similar to many other proteins in that they are irreversibly denatured when heated, producing an endothermal effect that disappears upon reheating. In addition to ribosomes, bacterial cells contain other macromolecular components, such as the cell envelope, nucleic acids, and proteins. These components in whole cells go through conformational transitions upon exposure to heating in DSC. The transitions are recorded as endothermic (heat absorption) or exothermic (heat release) peaks in the thermogram. The area under the peak (enthalpy of transition, ΔH) and the thermal stability (transition temperature, Tm), of each cellular component present on a typical DSC thermogram have been used to characterize bacterial cells. The first application of DSC on bacterial thermal analysis was the study on the physical properties of biomembranes. The physical prop-

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erties of lipids in cell membranes of Mycoplasma laidlawii were investigated by Steim et al. (1969) with DSC, by using whole cells, cell membranes, and extracted lipids. DSC thermograms of both isolated cell membranes and extracted membrane lipids showed an endothermic transition around 40 °C, suggesting extraction of lipids did not change the stability. However, whole-cell thermogram did not exhibit any distinguishable peaks (Bach and Chapman 1980). The first successful DSC on whole cells was the study on heat inactivation and spontaneous germination of bacterial spores. Maeda and colleagues (1974) observed that germinated Bacillus megaterium spores had endothermic peaks at about 100 °C and 130 °C. For vegetative cells, Verrips and Kwast (1977) reported eight endothermic peaks on the whole-cell thermogram of Citrobacter freundii. It is necessary to obtain distinguishable and reproducible transitions to identify the origin of the transitions and to examine their stability. The resolution of peaks can be enhanced by increasing viable cell density in the sample, by increasing sample size, and by improving the sensitivity of DSC instrument. Recent studies showed that larger and more distinguishable peaks can be obtained by using cell pellets instead of cell suspensions and by using the cells at a late logarithmic growth stage (Mackey et al. 1991; Lee and Kaletunç 2002a,b). This chapter focuses on the characterization of bacterial inactivation by using DSC-relevant conditions on precooking, refrigerating, or high-pressure processing of food to ensure its safety.

C. perfringens and L. monocytogenes Analysis by DSC DSC was used to examine changes in temperatures of endothermal effects of ribosomal proteins under cold- and heat-shocked conditions to determine thermal tolerance of ribosomes in C. perfringens and L. monocytogenes. In addition, L. monocytogenes cells were exposed to several antibiotics that bind to ribosomes to mimic cold-shock responses. Sample Preparations Enterotoxin-producing strains of C. perfringens were grown in fluid thyoglycolate bacteriological medium. The ribosomes from C. perfrin-

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gens were isolated according to procedure of Novak, Tunick, and Juneja (2001). Harvested vegetative cells were concentrated by centrifugation and resuspended in buffer consisting of 25 mM Tris (pH 7.5), 1 mM EDTA (pH 7.5), 5 mM β-mercaptoethanol, 6 mM MgCl2, and 30 mM NH4Cl. Cells were broken in a French pressure cell at 82.7 MPa, and DNase was added. Pellets of crude ribosomes were produced by further centrifugation at 32,500 g. Whole cells of L. monocytogenes were concentrated by centrifugation and resuspended in buffer consisting of 10 mM Tris (pH 7.5), 6 mM MgCl2, and 30 mM NH4Cl using the procedure of Bayles et al. (2000). Investigations of antibiotic-treated cultures were performed after exposing cells to antibiotics for 30 min at 37 °C and then centrifuging and resuspending in buffer. Antibiotics used included chloramphenicol, erythromycin, kanamycin, puromycin, rifampin, streptomycin, and tetracycline (Sigma Chemical Co., St. Louis, MO). Cold shocking was performed by incubating at the specified temperature for 3 h. C. perfringens cells and ribosomes were analyzed using a PerkinElmer DSC-7 equipped with an intercooler cooling accessory, and DSC of L. monocytogenes cells was performed in a Perkin-Elmer Pyris I with a liquid nitrogen cooler (Perkin-Elmer Corp., Norwalk, CT). Samples weighing approximately 12–20 mg were hermetically sealed in volatile sample pans, and the appropriate Tris buffer was used as a reference. After placing the pan in the instrument, C. perfringens samples were cooled to 10 °C and L. monocytogenes samples were cooled to 0 °C. After 2 min, samples were scanned to 100 °C at 10 °C/ min, and the baseline obtained from scanning the sample a second time was subtracted, producing the final curve. At least three replicate analyses of each sample were performed. Peak temperatures were calculated by using the instruments’ software. Helium was used as the flow gas in both instruments, which were regularly calibrated with an indium standard. Thermal tolerance studies and determination of D60 values were conducted by dilution, submerged-coil heating, plating, and enumeration as described previously (Bayles et al. 2000). C. perfringens Results A DSC scan of ribosomes isolated from C. perfringens vegetative cells is shown in Figure 7.1, curve A. There was an endothermal effect with

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Figure 7.1. DSC of C. perfringens vegetative cells. Curve A, isolated ribosomal proteins; curve B, whole cells treated at 46 °C for 60 min; curve C, whole cells treated at 28 °C for 60 min; curve D, technique for B followed by storage at 4 °C for several days; curve E, technique for C followed by storage at 4 °C for several days.

a peak around 72 °C, which corresponded to the 50S subunit and 70S particle (Miles, Mackey, and Parsons 1986). A shoulder at 66 °–67 °C was due to the 30S subunit (Mackey et al. 1991). The peak and shoulder disappeared with a subsequent scan without a change in baseline, proving that the ribosomal proteins were denatured by heat. The denaturation peaks of whole cells kept at 46 °C (heat-shocked) and 28 °C (control) were several degrees higher (Figure 7.1, curves B and C). The heat-shocked sample exhibited an increased resistance to heat, indicating that the structure or conformation of the protein was altered at elevated temperatures (Novak, Tunick, and Juneja 2001). Additional endothermal effects were observed around 81 °–85 °C, which have been attributed to bacterial DNA (Miles, Mackey, and Parsons 1986). Storage at 4 °C for several days, mimicking refrigeration in a

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supermarket or by a consumer, caused the peaks of both the heatshocked and control samples to flatten and shift to lower temperatures (Figure 7.1, curves D and E). The increased heat resistance of the heatshocked cells was lost, supporting the theory that this resistance is transient (Heredia, Labbé, and García-Alvarado 1998). Heat is uniformly distributed in a cell, resulting in damage to the most sensitive molecules within it. The results suggest that conformational changes in ribosomal proteins in response to temperature differences alter protein synthesis in C. perfringens and that refrigeration will destroy this organism in food. These conformational changes, which may involve changing the shape and structure of the protein, are readily discerned by evaluation of DSC scans. L. monocytogenes Results The DSC curve of L. monocytogenes cells (Figure 7.2A) exhibited melting transitions at 67.5 ° ± 0.4 °C, corresponding to thermal denaturation of the 30S subunit, and at 73.4 ° ± 0.1 °C, corresponding to the combined 50S subunit and 70S particle (Bayles et al. 2000). Cold shocking the cells at 0 °C for 3 h caused a shift in the 50S/70S peak denaturation temperature to 72.1 ° ± 0.5 °C (Figure 7.2B). The position

Figure 7.2. DSC of L. monocytogenes cells. Curve A, control grown at 37 °C; curve B, grown at 37 °C and cold shocked at 0 °C for 3 h.

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of the 30S peak did not shift significantly. Similar results were observed with a cold shock to 5 °C. Peak shoulders observed around 81 °C were due to bacterial DNA (Miles, Mackey, and Parsons 1986), as with the Clostridium samples. The results indicate that intracellular changes in the ribosomes, such as an alteration in the association status of the 70S particles, are correlated with changes in the thermal properties of L. monocytogenes. The 30S and 50S subunits are more thermally labile than the associated 70S particle, so any change that causes dissociation of 70S would make the ribosome more sensitive to heat (Stephens and Jones 1993). Effect of Antibiotics on Bacteria Certain antibiotics inhibit protein synthesis by selectively targeting bacterial 70S ribosomes while leaving eukaryotic ribosomes unaffected (Weisblum and Davies 1968). The effects of seven antibiotics, six active against the ribosome and one (rifampin) active against RNA polymerase, were tested on the cells to determine whether the antibiotic treatment produced alterations in peak denaturation temperatures corresponding to ribosomes or their subunits. Figure 7.3, curve A, is

Figure 7.3. DSC of L. monocytogenes cells treated with antibiotic. Curve A, control; curve B, kanamycin-treated cells; curve C, tetracycline-treated cells.

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the DSC curve of a control similar to that in Figure 7.2, curve A, and Figure 7.3, curve B, is the curve of cells treated with kanamycin. The 50S/70S peak shifted from 73.3 ° ± 0.1 °C to 72.1 ° ± 0.7 °C, a shift that was similar to the temperature reduction in cells that had been cold shocked (Figure 7.2). Treatment with tetracycline removed the 30S transition that had been observed around 67 °C (Figure 7.3, curve C). Thus, DSC analysis showed evidence of structural changes in the ribosomal protein. Treatment with chloramphenicol, erythromycin, puromycin, rifampin, or streptomycin produced results that were similar to those of the control. Cells were also cold shocked from 37 ° to 0 °C for 3 h and then thermally challenged at 60 °C to determine thermal tolerance. Previous research revealed that the D60 value of L. monocytogenes is 75.6 s (Miller, Bayles, and Eblen 2000). Kanamycin and tetracycline, which measurably altered the DSC curves of L. monocytogenes cells, were the antibiotics that caused reductions in thermal tolerance; chloramphenicol, erythromycin, puromycin, rifampin, and streptomycin did not alter the D60 values. Compared with the controls, kanamycin and tetracycline each reduced the D60 value by 20 s. These 26% reductions were approximately the same as those observed following cold shocks of 37 °–0 °C (Miller, Bayles, and Eblen 2000) and 37 °–5 °C. The antibiotic treatment data indicate that ribosomal changes have a significant impact on the thermal resistance of L. monocytogenes. Cold shock and certain antibiotics alter the state and modify the structure of ribosomes, as reflected by changes in the DSC curves. The results are probably due to disassociation of the 30S subunits, which are more thermally labile and more effectively denatured by heat. Similar results were observed in Dr. Kaletunç’s laboratory when erythromycin-treated E. coli cells were analyzed by DSC (Figure 7.4). E. coli cells suspended in HEPES buffer were treated with erythromycin for 40 min. With increasing concentration of erythromycin, it appears that major ribosomal transition shifts to a higher temperature in comparison with the thermogram of untreated cells. Furthermore, the shape of the peak changes and becomes less broad. Erythromycin is known to bind the 50S of bacterial ribosome, blocking the exit of the growing peptide chain, thus inhibiting the translocation of peptide. It can be speculated that treatment with erythromycin removes the 50S transition from the 50S/70S peak observed in the control cell thermo-

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Figure 7.4. Thermograms of whole cells of E. coli treated with erythromycin, control (thick dashes), 10 μ/ml erythromycin (thin dashes), 50 μg/ml erythromycin (dots).

gram as one broad peak. The thermogram after a treatment at a 50 μg/ ml erythromycin level therefore shows the endothermic transition being shifted to a higher temperature because it belongs to denaturation of 70S ribosomes, which is expected to have the highest thermal stability among the ribosomal subunits.

E. coli and Lactobacillus plantarum Analysis by DSC When microorganisms are heated in DSC, thermograms exhibit a number of overlapping transitions with a net endothermic effect (Miles, Mackey, and Parsons 1986; Anderson et al. 1991; Mackey et al. 1991; Mohacsi-Farkas et al. 1999; Lee and Kaletunç 2002a). Mackey et al. (1991) investigated the origins of apparent individual transitions on the thermogram of E. coli. Individual peaks observed in thermograms of whole cells of E. coli were assigned to cell components by comparing the transition temperatures of isolated cell components with corresponding transitions in whole cells.

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Sample Preparations E. coli cells were grown in trypticase soy broth, and Lactobacillus plantarum cells were grown in MRS broth at 37 °C to late exponential growth phase. The final concentration of cells in the medium was 1.3 ± 0.1 × 109 cfu ml−1 for E. coli and 9.0 ± 0.1 × 108 cfu ml−1 for L. plantarum. The cells were harvested by centrifugation at 10,000 g for 10 min at 4 °C. The supernatant was discarded and the pellets were washed with sterile distilled water and centrifuged for a second time before transferring into DSC crucibles. A differential scanning calorimeter (DSC 111, Setaram, Lyon, France) was used to record thermograms of microorganisms heated at a 3 °C min−1. All DSC measurements were conducted using fluid-tight, stainless steel crucibles. For each DSC run, the reference crucible was filled with distilled water equivalent to the water content of the sample. After heating in the DSC, samples were cooled rapidly by liquid nitrogen and rescanned to evaluate the reversibility of transitions. DSC thermograms were corrected for differences in the empty crucibles by subtracting an empty crucible baseline.

E. coli and L. plantarum Results DSC thermograms for E. coli and L. plantarum whole cells are shown in Figure 7.5 (Lee and Kaletunç 2002a). The peaks on the thermograms correspond to the thermally induced transitions of cellular components. Several differences exist between the DSC thermograms of E. coli and L. plantarum. The major peak, peak a2, shows up at a higher temperature in the E. coli thermogram (70 °C) in comparison with the L. plantarum thermogram (63 °C). Another visible difference between the E. coli and L. plantarum thermograms is a high-temperature endothermic transition (peak d) observed only in the DSC thermogram of E. coli whole cells. Based on the other DSC studies in Dr. Kaletunç’s laboratory for Gram-negative (Pseudomonas fluorescens) and Gram-positive (Staphylococcus aureus and Leuconostoc mesenteroides) bacteria, Lee and Kaletunç (2002a) suggested the origin of this peak is a cellular component of Gram-negative bacteria, more likely to be due to lipopolysaccharide transition. Lee and Kaletunç (2002a) also evaluated the thermal stabilities and the reversibility of individual transitions by a second temperature scan

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Figure 7.5. Thermograms of whole cells of E. coli (dashes) and L. plantarum (dots) obtained by DSC (1 ° to 150 °C with 3 °C min−1 heating rate). From Lee and Kaletunç (2002a).

after preheating in the DSC to various temperatures between 40 °C and 130 °C. They correlated with calorimetric data viability of bacteria subsequent to a heat treatment between 55 °C and 70 °C in the DSC. The fractional viability based on calorimetric data defined as the reduced apparent enthalpy [(ΔH – ΔHf)/(ΔH0 – ΔHf)] and plate count data defined as (N/N0) show a linear relationship. Viability loss and the irreversible change in DSC thermograms of pretreated whole cells are highly correlated between 55 °C and 70 °C. Comparison of DSC scans for isolated ribosomes shows that the thermal stability of ribosomes from E. coli is greater than the thermal stability of L. plantarum ribosomes, consistent with the greater thermal tolerance of E. coli observed from viability loss and DSC scans of whole cells. The denaturation of the ribosomal subunits occurred at the 50 °–80 °C range in both thermograms. The result indicated that the ribosomal denaturation by the DSC was associated with the 30S and 50S ribosomal subunits in increasing order of thermal stability. This study demonstrated that calorimetric data can be used to evaluate the viabilities of microorganisms exposed to thermal treatments. Furthermore, the relative thermal stabilities of different organisms to heat treatment can be compared. The calorimetric data also show that the heat denaturation of DNA might not be a major factor of vegetative cells’ death because the event

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is only partially irreversible and requires a higher temperature (85– 100 °C) than bacterial death (Lee and Kaletunç 2002a).

Application of DSC for Evaluation of Food-Processing Treatments Food preservation treatments are used to inactivate microorganisms and to enhance the shelf life of food products. The food industry uses thermal processing as the main technology for food preservation. However, alternative thermal processes, nonthermal processes, and processes using mild heating in conjunction with antimicrobial agents also have been used to preserve nutritional and textural qualities of food materials. Preservation treatments affect cellular components of foodborne microorganisms, resulting in physiological changes in cells and eventually the death of bacteria. DSC thermograms of whole bacterial cells exhibit differences in thermally induced transitions, revealing the response of bacteria to heat. Thus, DSC technique allows one to monitor and to detect the impact of thermal treatment on cellular components of bacterial cells, including ribosomal subunits, nucleic acids, and cell wall components. The differences in ribosomal thermal stabilities of various bacteria are shown to be related to the thermal tolerances of bacterial cells to heat (Lee and Kaletunç 2002a; Mackey et al. 1993; Miles, Mackey, and Parsons 1986). In this section, we will focus on the quantitative evaluation of cell viability from calorimetric data and the evaluation of impact of nonthermal treatments using calorimetric data. Determination of Heat Inactivation Parameters of Bacteria from Calorimetric Data The efficacy of a given treatment for inactivation of foodborne pathogenic and spoilage microorganisms depends on the inactivation kinetics of a target microorganism. In general, bacterial inactivation is considered as a first-order kinetics process. Therefore, the bacterial inactivation kinetics can be described by the D value (the time needed to reduce the population by 1 log) and z value (temperature change required for a 1-log reduction in D value. The D and z values are determined under isothermal conditions. However, in industrial applications, processing

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temperature is reached over a period of time during that a significant reduction of microbial population may occur as the temperature rises (Peleg 1999). Therefore, it is important to determine the D and z values under conditions similar to those used in processing. There are several studies in the literature modeling microorganism inactivation during increasing temperature protocols (Reichart 1979; Thompson et al. 1979a,b; Van Impe et al. 1992). DSC is ideally suited to achieve heat treatment under controlled conditions of linearly increasing temperature. Some investigators have used DSC to determine the thermally induced transitions and to evaluate the relationship between the stability of cellular components and cell injury or death (Miles, Mackey, and Parsons 1986; Mackey et al. 1988, 1991, 1993). An equation describing the rate of microorganism inactivation as a function of linearly increasing temperature was used to determine the temperature at which the maximum death rate occurred for vegetative cells (Miles, Mackey, and Parsons 1986) and to predict the number of surviving microorganisms as a function of temperature at a constant heating rate (Miles and Mackey 1994). The results demonstrated that the temperatures required to inactivate L. monocytogenes increased with the heating rate. Miles and Mackey (1994) stated that the derived equation can also be used to calculate the D and z values under linearly increasing temperature protocols. Lee and Kaletunç (2002b) used a novel approach to obtain the kinetic parameters of E. coli K12 inactivation using calorimetric data. E. coli pellets were preheated in the DSC to preset temperatures, were cooled immediately by liquid nitrogen, equilibrated at 1 °C, and were rescanned to 140 °C. The rescan contained the thermally induced transitions associated with the bacterial cells surviving after the preheat. Peak areas (apparent enthalpies, ΔH, J g−1) corresponding to the contributions of survivors were determined from the apparent heat capacity versus temperature profile by integrating the area under the curve (Figure 7.6). Miles and Mackey (1994) derived a mathematical model describing the number of surviving cells under linear heating conditions (Equation 7.1). N 2.303 z 2.303 ln ⎡⎢ − ln ⎛ ⎞ ⎤⎥ = T + ln − Te ⎝ ⎠ N0 ⎦ z Der z ⎣

(7.1)

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20

30

40

50

60

70

80

90 100 110 120 130

Figure 7.6. DSC thermogram for whole cells of E. coli K12 displaying curve baseline used to determine the apparent enthalpy value. From Alpas et al. (2003).

where N is the number of survivors at time t, N0 is the initial number of viable cells, r is the heating rate, and De is the D value at an arbitrary temperature Te. The value N/N0 represents the fraction of survivors as a result of heat treatment. Lee and Kaletunç (2002b), assuming that ΔH is proportional to the number of survivors, wrote Equation 7.2 to describe the fraction of surviving cells in terms of the DSC observable: ΔH − ΔH f N ≈ N 0 ΔH 0 − ΔH f

(7.2)

By substituting Equation 7.2 into Equation 7.1, Lee and Kaletunç (2002b) obtained an equation that enables one to obtain kinetic parameters of bacterial inactivation from calorimetric data. z 2.303 ⎡ ⎛ ΔH − ΔH f ⎞ ⎤ 2.303 ln ⎢ − ln ⎜ = T + ln − Te ⎟ ⎥ ⎝ ΔH 0 − ΔH f ⎠ ⎦ z Der z ⎣

(7.3)

This novel approach demonstrated that calorimetric data obtained with linearly rising temperature in DSC can be used not only for qualitative evaluation of bacterial inactivation kinetics but also quantitative evaluation. The D and z values for E. coli K12 determined from the calorimetric data and the corresponding values from plate count data

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obtained after heat treatment in the DSC and after isothermal treatment displayed close agreement. This approach provides reproducible and accurate results in a short time compared with the plate count technique because the DSC approach eliminates the incubation time normally used for plating, which might take 2 days or more. Determination of Efficacy of Nonthermal Treatments from Calorimetric Data There is a growing interest in using techniques alternative to thermal processing for food preservation to enhance safety and shelf life of perishable foods (Hoover et al. 1989; Knorr 1993). Among nonthermal treatment processes, high hydrostatic pressure (HHP) appears to be the most promising technology. HHP processing has the advantage over conventional heat treatments in that, while this technique is effective in inactivation of non–spore-forming microorganisms, substantial food quality retention can be retained by avoiding the destruction of small molecular compounds such as vitamins. It is reported that cell death increases as the level of the pressure applied increases, implying that critical cellular activities or processes have been irreversibly damaged (Hoover et al. 1989; Cheftel 1995). However, the pressure tolerance varies among the species of bacteria and even among the various strains of the same species (Styles et al. 1991; Patterson et al. 1995; Hauben et al. 1997; Alpas et al. 1999; Benito et al. 1999). Although DSC is a thermal analysis technique, it has been applied to evaluate the impact of HHP processing on inactivation of bacteria by comparing the pre- and postprocess thermograms (Niven, Miles, and Mackey 1999; Alpas et al. 2003; Kaletunç et al. 2004). The comparison of various final states as a function of various physical and chemical factors, starting from the same initial state, makes it possible to use DSC to predict the effectiveness of methods to inactivate microorganisms. Niven and colleagues (1999) demonstrated by DSC studies that cell death due to high-pressure treatment may also be related to irreversible ribosomal damage. Alpas et al. (2003) confirmed quantitatively that cell viability decreases as the extent of ribosomal denaturation assessed by calorimetry increases. The ribosomal denaturation was evaluated by comparing the total apparent enthalpy of the control and pressure-

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treated cells and was related to the log reduction in viability. Furthermore, they demonstrated quantitatively that the relative sensitivities to high hydrostatic pressure treatment of bacterial strains from E. coli O157:H7 and S. aureus can be assessed from calorimetric data (Table 7.1). The results showed that pressure and thermal tolerances of bacteria can be different as can be the mechanism of denaturation. Table 7.1. Apparent enthalpy and viability data for untreated control and pressure-treated cells.

Bacteria S. aureus 485 Control S. aureus 485 345 MPa S. aureus 765 Control S. aureus 765 345 MPa E. coli O157:H7 933 Control E. coli O157:H7 933 275 MPa E. coli O157:H7 931 Control E. coli O157:H7 931 275 MPa

Apparent enthalpy (J/g wet weight)

Fractional reduction in apparent enthalpy (ΔH0– ΔH)/ΔΔH0

4.0 2.7

0.32

3.8 2.4

0.37

3.7

2.8

0.24

3.7

2.7

From: Alpas et al. 2003

0.27

Viable cells (cfu/ml)

Log reduction in viability −log10(N/N0)

1.6 × 109

—

5.0 × 106

2.5

2.0 × 109

—

1.6 × 106

3.1

2.0 × 109

—

2.0 × 107

2.0

1.3 × 109

—

6.3 × 106

2.3

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Whereas S. aureus 765 had a relatively higher resistance to thermal treatment in comparison with S. aureus 485, S. aureus 485 was determined to be more resistant to pressure than S. aureus 765. This information can be used in the design of processes specific to targeting certain cellular components by using different physical stresses. Determination of Impact of Antimicrobials on Bacteria from Calorimetric Data Hurdle technology, which involves mild heating in conjunction with antimicrobial agents, has been used by the food industry to preserve nutritional and textural qualities of food while maintaining its extended shelf life (Leistner 2000). Acids, salt, and ethanol are the most commonly employed preservatives used to reduce the intensity of the heat treatment (Cameron, Leonard, and Barret 1980; Adams et al. 1989; Casadei et al. 2001). The effectiveness of hurdle technology can be enhanced if hurdles target different cellular components, thereby reducing the tolerance of bacteria to heat treatment and preventing cellular repair mechanisms during the storage of the food product. DSC can be used to monitor changes in cellular components induced by chemical agents in vivo by comparing the thermograms of bacteria before and after treatment. Lee and Kaletunç (2005) investigated the influence of organic (acetic acid) and inorganic (hydrochloric acid) acids, ethanol, or NaCl treatment on the cellular components of E. coli by using calorimetry and compared the calorimetric data with viability results obtained by the plate count method. All chemical treatments resulted in shifting of ribosomal denaturation transition to a lower temperature, an indication of the increasing sensitivity of the bacteria prior to heat treatment. The comparison of the DSC thermograms of control cells with the thermograms of ethanol or acetic acid-treated cells showed, in addition to thermal stability decrease, a major reduction in size of the ribosomal subunit transition peak, which can be interpreted as the lower energy requirement for denaturation of ribosomes. The observed changes in the DSC profiles were irreversible and were associated with the loss of viability assessed by a plate count method. The decrease in thermal tolerance of the bacterial cell to heat treatment was chemical-specific and a function of the chemical concentration. The heat sensitivity of bacterial cells following an acid treatment was observed to be greater

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than in the cells treated with ethanol and salt. Differences also were observed in the DSC profiles of bacterial cells treated with organic or inorganic acid, suggesting that the mechanism of reduced thermal tolerance of bacterial cells by these acids may be different. For design of hurdle technology application in food processing, DSC studies in vivo provide valuable information relevant to the effectiveness of hurdles. Conclusions DSC is a valuable tool when investigating the effect of physical or chemical treatments applied during food preservation on inactivation of bacteria. Among the cellular components in a bacterial cell, the damage to ribosomal proteins due to thermal, nonthermal, chemical, or antibiotic treatments appears to be related to loss of cell viability. DSC scans show that protein synthesis in C. perfringens and L. monocytogenes ribosomes is more efficiently destroyed during heating when conformational changes and disassociation of the 30S subunits are induced by temperature shocks. DSC thermograms display information about the cellular components affected by various preservation treatments, thereby providing insight into the mechanism of bacterial inactivation. Furthermore, the calorimetric data can be analyzed to obtain quantitative information about bacterial inactivation, including thermal stability, thermal energy required for bacterial inactivation, and the kinetic parameters of inactivation. Calorimetric data can be used to optimize the processing conditions of food preservation in a rational manner. References Adams, M. R., O’Brien, P. J. and Taylor, G. T., 1989. Effect of ethanol content of beer on the heat resistance of a spoilage Lactobacillus. J Appl Bacteriol, 66:491–495. Alpas, H., Kalchayanand, N., Bozoglu, F., Sikes, A., Dunne, C.P. and Ray, B., 1999. Variation in resistance to hydrostatic pressure among strains of food-borne pathogens. Appl Environ Microbiol, 65(9):4248–4251. Alpas, H., Lee, J., Bozoglu, F. and Kaletunç, G. 2003. Differential scanning calorimetry of pressure-resistant and pressure-sensitive strains of Staphylococcus aureus and Escherichia coli O157:H7. Int J Food Microbiol, 87:229–237.

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Anderson, W.A., Hedges, N.D., Jones, M.V. and Cole, M.B. 1991. Thermal inactivation of Listeria monocytogenes studied in differential scanning calorimetry. J Gen Microbiol, 137:1419–1424. Bach, D. and Chapman, D. 1980. Calorimetric studies of biomembranes and their molecular components. In Biological microcalorimetry ed. Beezer, A.E. pp. 275– 309. Academic Press: London. Bayles, Darrell O., Tunick, Michael H., Foglia, Thomas A. and Miller, Arthur J. 2000. Cold shock and its effect on ribosomes and thermal tolerance in Listeria monocytogenes. Appl Environ Microbiol, 66(10):4351–4355. Benito, A., Ventoura, G., Casadei, M., Robinson, T. and Mackey, B. 1999. Variation in resistance of natural isolates of Escherichia coli O157 to high hydrostatic pressure, mild heat, and other stresses. Appl Environ Microbiol, 65(4):1564–1569. Borman, Stu 2007. Protein factory reveals its secrets. Chem Eng News, 85(8): 13–16. Cameron, M. S., Leonard, S. J. and Barret, E.L. 1980. Effect of moderately acidic pH on heat resistance of Clostridium sporogenes spores in phosphate buffer and in buffered pea puree. Appl Environ Microbiol, 39:943–949. Casadei, M. A., Ingram, I., Hitchings, E., Archer, J. and Gaze, J. E. 2001. Heat resistance of Bacillus cereus, Salmonella typhimurium and Lactobacillus delbrueckii in relation to pH and ethanol. Int J Food Microbiol, 63:125–134. Cheftel, J.-C., 1995. High pressure, microbial inactivation and food preservation. Food Sci Technol, 1:75–90. Hauben, K.J.A., Bartlett, D.H., Soontjens, C.C.F., Cornelis, K., Wuytack, E.Y. and Michiels, C.W., 1997. Escherichia coli mutants resistant to inactivation by high hydrostatic pressure. Appl Environ Microbiol, 63(3):945–950. Heredia, Norma L., Labbé, Ronald G. and García-Alvarado, José Santos. 1998. Alteration in sporulation, enterotoxin production, and protein synthesis by Clostridium perfringens type A following heat shock. J Food Prot, 61(9): 1143–1147. Hoover, D.G., Metrick, C., Papineau, A.M., Farkas, D.F. and Knorr, D., 1989. Biological effects of high hydrostatic pressure on food microorganisms. Food Technol, 43(3):99–107. Kaletunç, G., Lee, J., Alpas, H. and Bozoglu, F. 2004. Evaluation of structural changes induced by high hydrostatic pressure in Leuconostoc mesenteroides. Appl Environ Microbiol, 70:1116–1122. Knorr, D., 1993. Effect of high hydrostatic pressure processes on food safety and quality. Food Technol, 47(6):156–161. Lee, J. and Kaletunç, G. 2002a. Evaluation by differential scanning calorimetry of the heat inactivation of Escherichia coli and Lactobacillus plantarum. Appl Environ Microbiol, 68:5379–5386. Lee, J. and Kaletunç, G. 2002b. Calorimetric determination of inactivation parameters of microorganisms. J Appl Microbiol, 93:178–189. Lee, J. and Kaletunç, G. 2005. Evaluation by differential scanning calorimetry of the effect of acid, ethanol, and NaCl on Escherichia coli. J Food Prot, 68:487–493.

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Leistner, L. 2000. Basic aspects of food preservation by hurdle technology. Int J Food Microbiol, 55:181–186. Mackey, B.M., Miles, C.A., Parsons, S.E. and Seymour, D.A. 1991. Thermal denaturation of whole cells and cell components of Escherichia coli examined by differential scanning calorimetry. J Gen Microbiol, 137 (10):2361–2374. Mackey, B.M., Miles, C.A., Seymour, D.A. and Parsons, S.E. 1993. Thermal denaturation and loss of viability in Escherichia coli and Bacillus stearothermophilus. Lett Appl Microbiol, 16:56–58. Mackey, B.M., Parsons, S.E., Miles, C.A. and Owen, R.J. 1988. The relationship between base composition of bacterial DNA and its intracellular melting temperature as determined by differential scanning calorimetry. J Gen Microbiol, 134: 1185–1195. Maeda, Y., Noguchi, S. and Koga, S. 1974. Differential scanning calorimetric study of spontaneous germination of Bacillus megaterium spore by water vapor. J Gen Microbiol, 20:11–19. Mead, P.S., Slutsker, L., Dietz, V., McCaig, L.F., Bresee, J.S., Shapiro, C., Griffin, P.M. and Tauxe, R.V. 1999. Food-related illness and death in the United States. Emerg Infect Dis, 5:607–625. Miles, C.A. and Mackey, B.M. 1994. A mathematical analysis of microbial inactivation at linearly rising temperatures: calculation of the temperature rise needed to kill Listeria monocytogenes in different foods and methods for dynamic measurements of D and z values. J Appl Bacteriol, 77:14–20. Miles, C.A., Mackey, B.M. and Parsons, S.E. 1986. Differential scanning calorimetry of Bacteria. J Gen Microbiol, 132(4):939–952. Miller, Arthur J., Bayles, Darrell O. and Eblen, B. Shawn. 2000. Cold shock inactivation of thermal sensitivity in Listeria monocytogenes. Appl Environ Microbiol, 66(10):4345–4350. Mohacsi-Farkas, Cs., Farkas, J., Meszaros, L., Reichart, O. and Andrassy, E. 1999. Thermal denaturation of bacterial cells examined by differential scanning calorimetry. J Therm Anal Calorim, 57:409–414. Niven, G.W., Miles, C.A., Mackey, B.M., 1999. The effects of hydrostatic pressure on ribosome conformation in Escherichia coli: an in vivo study using differential scanning calorimetry. Microbiol, 145:419–425. Novak, John S., Tunick, Michael H. and Juneja, Vijay K. 2001. Heat treatment adaptations in Clostridium perfringens vegetative cells. J Food Prot, 64(10): 1527–1534. Patterson, M.F., Quinn, M., Simpson, R., Gilmore, A. 1995. Sensitivity of vegetative pathogens to high hydrostatic pressure treatment in phosphate-buffered saline and foods. J Food Prot, 58:524–529. Peleg, M. 1999. On calculating sterility in thermal and non-thermal preservation methods. Food Res Int, 32:271–278. Reichart, O. 1979 A new experimental method for the determination of the heat destruction parameters of microorganisms. Acta Alimentaria, 8:131–155. Steim, J.M., Tourtellotte, M.E., Reinert, J.C., McElhaney, R.N., Rader, R.L. 1969. Calorimetric evidence for the liquid-crystalline state of lipids in a biomembrane. Proc Nut Acad Sci, 63:104–109.

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Stephens, Peter J. and Jones, Martin V. 1993. Reduced ribosomal thermal denaturation in Listeria monocytogenes following osmotic and heat shocks. FEMS Microbiol Lett, 106(2):177–182. Styles, M.F., Hoover, D.G., and Farkas, D.F., 1991. Response of Listeria monocytogenes and Vibrio parahaemolyticus to high hydrostatic pressure. J Food Sci, 56: 1404–1407. Thompson, D.R., Willardsen, R.R., Busta, F.F and Allen, C.E. (1979a) Clostridium perfringens population dynamics during constant and rising temperatures in beef. J Food Sci, 44:646–651. Thompson, W.S., Busta, F.F., Thompson, D.R. and Allen, C.E. (1979b) Inactivation of salmonellae in autoclaved ground beef exposed to constantly rising temperatures. J Food Prot, 42:410–415. Van Impe, J.F., Nicolai, B.M., Martens, T., De Baerdemaeker, J. and Vandewalle, J. (1992) Dynamic mathematical model to predict microbial growth and inactivation during food processing. Appl Environ Microbiol, 58:2901–2909. Verrips, C.T. and Kwast, R.H. 1977. Heat resistance of Citrobacter freundii in media with various water activities. Eur J Appl Microbiol, 4:225–231. Weisblum, Bernard and Davies, Julian 1968. Antibiotic inhibitors of the bacterial ribosome. Bacteriol Rev, 32(4):493–528. World Health Organization (WHO). 2007. Food safety and foodborne illness. Fact Sheet No. 237. WHO, Geneva, Switzerland.

Chapter 8 Coupling of Differential Scanning Calorimetry and X-Ray Diffraction to Study the Crystallization Properties and Polymorphism of Triacylglycerols Christelle Lopez, Daniel J.E. Kalnin, and Michel R. Ollivon*

Introduction Thermal and Crystallographic Properties of Triacylglycerols Polymorphism of Triacylglycerols Differential Scanning Calorimetry X-Ray Diffraction Coupling of XRD and DSC: MICROCALIX Applications and Results Cocoa Butter and Its Components Milk Fat Lard Conclusion References

169 170 170 173 175 176 179 179 184 190 193 194

Introduction Lipids from vegetable or animal origins are widely consumed in food products, for example, in chocolate, shortenings, margarine, and butter. The composition of triacylglycerols (TG), which are the main constituents of natural fats and oils or hydrogenated and interesterified fats (i.e., such as in margarine), their suprastructure, and the physical prop*This chapter is dedicated to Michel Ollivon who passed away on June 16th, 2007.

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erties of fat determine the mouth-feel, flavor release, and functional properties of high-fat content food products. Moreover, the lipid phase of food stuff is sometimes partially crystallized at the temperature of storage (in a freezer or fridge at −20 and 4 °–7 °C, respectively) and consumption, including cocoa butter in chocolate, hydrogenated margarine, and milk fat in dairy products. Studying the properties of TG is important to better understand and then control the physical properties of fats. Increasing the knowledge of both the physical and thermal properties of fats (i.e., solid fat content and type of crystals as a function of temperature) in anhydrous state as well as in situ in food products is of tremendous importance with respect to functional, sensorial, and nutritional properties. Moreover, the increased knowledge of TG crystallization and polymorphism in fats is of value for technical applications as well as for the development of new processes and products. The polymorphism of TG renders the study of the thermal and structural properties of lipids very complex. Both types of properties, largely depending on sample history, are conveniently determined using differential scanning calorimetry (DSC) and X-ray diffraction (XRD) techniques. In this chapter, we focus on the thermal and crystallographic properties of TG investigated by these two techniques, which are coupled using the microcalorimeter MICROCALIX. Thermal and Crystallographic Properties of Triacylglycerols Polymorphism of Triacylglycerols Fatty acids have various melting points, which mainly depend on the number of carbon atoms and their level of unsaturation (Table 8.1). The melting point of a TG molecule (triester of fatty acids and glycerol) depends on the three fatty acids esterified and on their position on the glycerol (sn-1, sn-2, sn-3) (Table 8.1). Thus, the thermal behavior of natural fats, constituted by several types of TG molecules, is really complex. Moreover, the assignment of the thermal properties of fats is complicated by the existence of a polymorphism of monotropic type for each TG (Small 1986; Ollivon and Perron 1992). Each TG can exhibit several crystalline forms, the occurrence of which strongly depends on its thermal history. Each polymorphic form

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Table 8.1. Melting point of the main fatty acids found in natural fats and oils and of triacylglycerols. Fatty Acids Formula C4:0 C6:0 C8:0 C10:0 C12:0 C14:0 C16:0 C18:0 C18:1 9c C18:2 9c, 12c C18:3 9c, 12c, 15c

Triacylglycerols Name (Abbreviation)

Melting Point (°C)

TG Abbreviation

Melting Point (°C)

Butyric acid (B) Caproic acid Caprylic acid Capric acid Lauric acid (L) Myristic acid (M) Palmitic acid (P) Stearic acid (St) Oleic acid (O) Linoleic acid Linolenic acid

−8

BBB

−75

−4 16 31 44

OOO StOO StStO StOSt

5 24 38 44

54

PPO

34

63

POP

36

70

LLL

47

16 −5 −14

MMM PPP StStSt

58 66 73

of a given TG molecule is characterized by its own melting point (Table 8.2). Then, TG mixtures exhibit multiple melting points depending on their composition, which makes the overall melting behavior of fat even more complex because some TG can cocrystallize. TG polymorphism relates to the ability of molecules to arrange themselves within a crystal lattice in several different ways of lateral packing of the fatty acid chains (Figure 8.1B) and of longitudinal stacking of molecules (Figure 8.1B) in lamellar structures (Hagemann 1988). Thus, pure TG and mixtures of TG can adopt several crystalline arrangements. TG molecules have a polymorphism of monotropic type, which means that the transitions between the polymorphic forms are irreversible and are only possible from the least to the most stable species as characterized by a higher melting point. Moreover, polymorphic transitions are only possible by way of a liquid phase (Small 1986). The three main polymorphic forms frequently observed for the lateral packing of fatty acid chains correspond to different subcells that

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Table 8.2. Some crystallographic and energetic properties of the three main polymorphic forms of a selection of TGs. Polymorphic Form Property of TG* Main short spacings (Å) Melting point (°C) of StStSt Enthalpy of fusion (J g−1) of StStSt Melting point (°C) of OOO Melting point (°C) of LLL Melting point (°C) of POP

Hexagonal α 4.15

Orthorhombic Perpendicular β′

Triclinic Parallel β

3.8 and 4.2

4.6

55

64

72

163

180

230

−32

−12

5

15

35

46

21

30

36

*TG, triacylglycerols; St, stearic acid; O, oleic acid; L, lauric acid; P, palmitic acid. Adapted from Mulder and Walstra 1974.

have been described in detail (Small 1986; Ollivon and Perron 1992): hexagonal (α form), orthorhombic perpendicular (β′ form), and triclinic parallel (β form) (Figure 8.1B). The density, enthalpy of fusion, melting point, and stability increase in the order α, β′, and β, according to the monotropic character of the polymorphism. In the α form, the lateral packing of the fatty acid chains is not very tight and the chains have considerable rotational freedom, whereas in the β form, the chains are very densely packed. TG crystals are made by the stacking of TG molecules layers, the thickness of which depends on the length and unsaturation of the fatty acid chains and their angle of tilt with respect to the basal planes formed by the methyl end groups of the TG (Figure 8.1C). The longitudinal organization of TG in lamellar structures is primarily related to the number of chains stacked in the crystalline cell. For TG in natural fats, the number of fatty acid chains frequently observed is two or three and corresponds to the stacking of double (2L)- or triple (3L)-chain length lamellar structures (Small 1986; Hagemann 1988). Roughly, 3L forms are usually related to lowmelting, long-chain monounsaturated and mixed long- and short-chain

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Figure 8.1. Main types of triacylglycerol (TG) packings. (A) Lamellar structure formed by TG molecules in the solid state: for example, β form of trilaurin. (B) Left: The stable conformation of the hydrocarbon chains of saturated fatty acid (FA) is a planar zigzag shown here as a 3D view along its main axis. Right: Three main types of lateral chain packings (only carbon atoms are drawn): hexagonal, orthorhombic perpendicular (O + upside down T), and triclinic parallel (T//) subcell in the order of their stability, which are named α, β′, and β for TG. (C) Two main types of TG longitudinal chain stacking (fatty acids are drawn as straight lines), 2L and 3L.

TG, whereas 2L forms are generated mostly by similar long-chain, high-melting, trisaturated TG (Small 1986). The techniques most frequently used for the study of the thermal and crystallographic properties of TG are DSC and XRD. Differential Scanning Calorimetry The thermal properties of fats are generally studied using DSC. In DSC, the difference between the heat flow (J/s or W) of a reference and a sample is measured as a function of temperature or time while they are subjected to a controlled temperature–time program. DSC is thus a form of differential thermal analysis (DTA). In any DSC instrument, the sample and reference are placed in small individual pans or cruci-

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bles, which may be opened or hermetically sealed, of 10–100 μl capacity. Because sample size is small, accurate weighing is essential for quantification of the thermal properties of fats. The reference is often an empty pan, so as to not exhibit thermally induced transitions within the temperature range of interest. Transitions that occur in the sample during the applied temperature program appear as peaks or troughs, depending on whether they are exothermic or endothermic, on the plot of differential heat flow versus temperature or time (the thermogram) that is the output of the instrument. Conventionally, the temperature program used in DSC is a linear change in temperature with time, with various cooling and heating rates. After heating a lipid sample to 20 °C over its final melting point to erase its thermal history/memory (Ollivon and Perron 1992), different rates of cooling permit the investigation of the crystallization properties of TG, polymorph formation, and transformation. Cooling and heating can also be performed from a temperature at which the fat is partially crystallized (e.g., 4 °C for milk fat). Isothermal DSC, in which the temperature is kept constant at a value at which a transition of interest is known to occur, is especially useful for studying the polymorphic evolutions as a function of time (Lopez et al. 2002a). The crystallization properties of fats depend on their thermal history and on the rates of cooling and heating applied. A combination of cooling, heating, and isothermal DSC scans can be used to study polymorphism of TG and fats in complex products. DSC is a useful tool (1) to record the crystallization and melting profiles recorded on cooling and heating, respectively; (2) to determine the characteristic temperatures, such as the temperatures of initial crystallization (Tonset) and final melting (Toffset); (3) to monitor polymorphic evolutions and measure the heat of transitions; and (4) to quantify the solid fat content that is proportional to the enthalpy of melting (ΔH) of fat. The effects of different rates of cooling and heating on polymorph formation and polymorphic transitions recorded as a function of temperature or time are easily studied by DSC. DSC studies have given an insight into the thermodynamics of fat phase transition in bulk and in emulsions. However, because the complex DSC recordings are often difficult to interpret and it is not possible to identify unequivocally polymorphs using DSC, the experiments must be coupled with other techniques, such as XRD or other techniques yielding structural information. Moreover, DSC allows the characterization of the physical

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state changes if a change of energy is involved. However, this technique does not provide information on the structure that exists before and after the phase transition. X-Ray Diffraction XRD is a powerful technique to use to provide structural information. As explained above, characteristic lengths of structures formed by TG range from atomic distances up to hundreds of ångstroms. The whole size range can be investigated by means of X-ray scattering. Generally, X-ray scattering reflects periodical differences of the electron density within a sample. Thus, X-rays are the ideal direct probe for determining the internal structure of crystalline material because they provide information on the repetitive patterns of the electron density of the array of atoms. In their solid state, TG molecules are arranged periodically in planes at repetitive distance d, which can be identified using XRD (Figure 8.1A). Wide-angle X-ray scattering (WAXS) monitors the structure at atomic scale (from about 1 to 10 Å). It provides information on intraand intermolecular distances called short spacings. For crystallized TG, the hydrocarbon chains are arranged in regularly spaced planes. Crystallographic planes give rise to a reflection line at a distinct angle θ, satisfying the well-known Bragg relation: 2d sin θ = nλ, where λ is the X-ray wavelength, d is the repetitive distance between planes, n is an integer, and θ is half the angle between the incident and diffracted beam (Guinier 1964; Small 1986). The actual data registered during an XRD experiment is the scattered intensity as a function of 2θ. It is often convenient to use the scattering vector q instead of the scattering angle 2θ because the former is independent of the wavelength of the incident beam. They are related as follows: q = (4π/λ) sin θ. Thus, the distance d between planes can be deduced from the position q of the diffraction peak by d = 2π/q. The different packing of aliphatic chains in the three crystalline TG subcells leads to characteristic wide-angle X-ray reflections enabling their identification. Short spacings are widely used for identifying the various crystalline subcells characterizing the polymorphic forms (Figure 8.1B). A single line around 4.15 Å characterizes the α form (hexagonal subcell). A strong line at 4.6 Å and two small lines at about 3.85 and 3.7 Å identifies the β form (triclinic parallel subcell), whereas

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the β′ form (orthorhombic subcell) pattern exhibits two lines at about 4.2 and 3.8 Å. Small-angle X-ray scattering (SAXS) monitors the lamellar organization (e.g., 2L or 3L) of a TG sample in a range from 10 to about 1000 Å. Reflection maxima appear in the scattering profile. The Bragg relation applies again, where d yields the mean thickness of adjacent lamellae (called TG long spacing). From the measurement of d (Å) and with the knowledge of the fatty acid composition (chain length, unsaturation), it is then possible to deduce if the stackings correspond to 2L or 3L organization (Figure 8.1C). In conclusion, the two levels of organization of crystallized TG, for example, the lateral packing of the fatty acid chains and the longitudinal stacking of TG molecules in lamellae, are easily identifiable from the short and long spacings observed by X-ray scattering at wide and small angles, respectively. Recent use of synchrotron radiation, which provides X-ray flux 103–106 times more intense than that generated by usual X-ray sources, permits recordings to be performed in times ranging from a few milliseconds to seconds. Thus, direct continuous recordings can be achieved as a function of time (XRDt) or temperature (XRDT). Moreover, synchrotron radiation permits studying the organization of TG in water-dispersed systems such as emulsions and complex food products and to quantitatively monitor phase changes within emulsion droplets. This is especially interesting when relating the textural and rheological properties of fats and high-fat food products to the thermal and crystallographic properties of TG. Therefore, the functionality of fat in many food products cannot be understood without knowing both its composition and physical properties their dependencies.

Coupling of XRD and DSC: MICROCALIX A new differential microcalorimeter, called MICROCALIX, has been developed within Centre National de la Recherche Scientifique (CNRS) group UMR 8612 (Ollivon et al. 2006) to perform simultaneous thermal and X-ray measurements (Figure 8.2). Small volumes of samples (from about 1 to 20 μl) are loaded in glass or quartz capillaries (external diameter 1.5 mm) ensuring minimum attenuation of the X-ray beam and parasitic scattering. The microcalorimeter allows, in its last version,

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Figure 8.2. Experimental setup of the microcalorimeter MICROCALIX in the timeresolved synchrotron XRD environment. (A) Schematic representation: The cell is positioned with the capillary containing the sample perpendicular to the beam in such a way that the diffraction patterns are recorded in the vertical plane by one or two one-dimensional proportional detectors (LD) at small and wide angles. Counting electronic (Counting Elect.), nanovoltmeter (nVmeter), and temperature controller (T Ctrl) are monitored by a single computer. The temperature-controlled cryostat (TCC) is kept at constant temperature (e.g., 6 °C). (B) Setup on the D22 bench of synchrotron (LURE, Orsay, France). (C) Setup on the D24 bench of synchrotron (LURE, Orsay, France).

thermal scans in the temperature range −30 °C to +230 °C, with scanning rates between 0.01 ° and 10 °C/min and with sensitivity comparable with that of a modern commercial apparatus (>100 μV/mW). Scanning temperature is controlled with a resolution of 0.01 °C, and the microcalorimeter is calibrated with lauric acid. MICROCALIX was used on synchrotron radiation X-ray benches. The lastest version of the instrument has been adapted for laboratory bench and conventional source, but it is preferably used with rotating anode or multilayered mirrors. MICROCALIX inserted in a laboratory conventional X-ray source The microcalorimeter can be installed on a laboratory source designed for simultaneous SAXS and WAXS measurements, such as that of CNRS group at UMR 8612 in Châtenay-Malabry (France; Lopez et al.

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2008). The X-ray source of this setup is a Diffractis 586 generator (ENRAF-NONIUS) equipped with a long-fine-focus, Cu anode, sealed tube operated at 40 kV and 20 mA. CuKα (λ = 1.54 Å) radiation is selected and the line focused by a graded, elliptically bent, multilayer mirror (OSMIC-Rigaku, Troy, Michigan). SAXS and WAXS patterns are recorded by two linear, position-sensitive, gas detectors using ASA2.4 software (HECUS-Braun, Graz, Austria). The detector recording SAXS data is placed at the focus point of the mirror. The scattered intensity is reported as a function of the scattering vector q = 4π sin θ/λ, where θ is half the scattering angle and λ the wavelength. The detectors are calibrated at wide angles with the crystalline β form of high-purity tristearin (characteristic repeat spacing 4.59, 3.85, 3.70 ± 0.01 Å) (Ollivon and Perron 1992) and at small angles with silver behenate (long spacing of 58.380 ± 0.001 Å) (Blanton et al. 2000). At small angles, this instrument covers the range of scattering vectors 0.05 < q < 0.4 Å−1 and is thus well suited for measurements on lipids. Line focusing of the beam increases the flux on the sample in such a way that measuring time can be reduced to a few minutes (about 2 min), allowing study of lipid phase transition kinetics and mechanism at a rather low scanning rate. MICROCALIX inserted in synchrotron radiation XRD bench The availability of a synchrotron radiation source with a brilliant beam of variable wavelength with very small vertical angular divergence has opened new opportunities. The high collimation of the beam allows the investigation of much larger structural features, with better spatial resolution. Increase in the flux by several orders of magnitude, compared to flux of conventional sources, enables the study of very dilute or weakly scattering samples such as TG emulsions (Lopez et al. 2007) and aerated food products (Kalnin et al. 2002). For bulk TG samples, time-resolved measurements can be performed with a time resolution down to a few milliseconds; thus, possible intermediate states in the course of a transition can be assessed (Lopez et al. 2006b). Generally, the timescale that may be reached during a time-resolved experiment depends on instrumental factors such as source and detector characteristics and sample properties. For conventional sources, the time resolution is usually determined by the flux at the sample, whereas for synchrotron radiation source it is rather limited by the counting rate of detectors, because statistically significant information must be col-

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lected. Note that the time resolution is also limited by the heat conductivity of the sample. If the high flux of synchrotron radiation source is used to follow very fast transitions, very good heat transfer to a small sample must be ensured. Experiments using MICROCALIX have been carried out at three synchrotron radiation sources, Laboratoire pour l’Utilisation du Rayonnement Electromagnétique (Orsay, France), European Synchrotron Radiation Facility (Grenoble, France), and Elettra (Trieste, Italy); further experiments are planned at SOLEIL (Saclay, France).

Applications and Results Examples of the study of crystallization properties and polymorphism of TG in natural fats and complex food products are presented below as major applications of the coupling of DSC with XRD. Cocoa Butter and Its Components Polymorphism of cocoa butter (CB), which is a vegetable fat used mainly by chocolate manufacturers, has often been discussed in the literature because it is related to the organoleptic and physical characteristics of the final products (snap, molding contraction, gloss, and blooming during the storage). In fact, the quality of chocolate bars and pralines strongly depends on their physicochemical properties and on the polymorphic form of CB. The polymorphism of CB can be compared with that of the main TG, POP, POSt and StOSt (where P is palmitic acid, O is oleic acid, St is stearic acid), which represent about 17%, 37%, and 27% of CB composition, respectively, and that of their mixtures (Wille and Lutton 1966; Kunutsor and Ollivon 1983; Sato 1987; Sato et al. 1989; Arishima et al. 1991; Loisel et al. 1998a). CB polymorphism is commonly described in the literature in terms of six different polymorphic forms, noted as forms I to VI in Figure 8.3 (which are in fact sub-α, α, β′, and β forms) in increasing order of melting points, according to the nomenclature of Wille and Lutton (1966). These six forms have been confirmed by other authors (Loisel et al. 1998a; Chapman et al. 1971; Adenier et al. 1975; Huyghebaert and Hendrickx 1971; Lovegren et al. 1976; Merken and Vaeck 1980; Davis and Dimick 1986). However,

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Figure 8.3. Summary of the possible arrangements of cocoa butter. Upper left shows a table resuming the notation, melting point, lateral and longitudinal packing of TGs. An overlay of two DSC recordings shows the closeness of two thermal events, notably the occurrence for V and form VI. Underneath is the XRD pattern of what is believed to be the pure crystalline species of forms V to VI.

the existence of some of them is debated, as well as the fact of the purity of some forms, especially I, III, and VI. In fact, it is very hard to obtain monocrystalline samples; rather, one obtains polycrystalline powders from pure TG as a solid state (Van Malssen 1994). Thus, it should be considered that complex mixtures of TG will occur either at a molecular level. It also has to be considered that lipid crystals of sub-α, α, and β′ do not necessarily correspond to one homogenous crystalline state (Marangoni and McGauley 2003). Lipid crystals can be seeded to achieve the commercially desired form V (Figure 8.3) (Davis and Dimick 1989a,b; Chaiseri and Dimick 1995a,b). However, it is apparent from Figure 8.3 that this crystalline form is not the most stable one, and it is necessary to avoid polymorphic transition toward the more stable form VI, which is responsible for fat bloom. This can be achieved by the addition of minor components such as glycolipids, phospholipids, and saturated TG, which promote the

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crystallization of CB. However, good practice has to be used during tempering since seeding can also hinder formation of the desired form V of CB. To better understand the phase behavior of CB, a MICROCALIX calorimeter has been used, together with synchrotron radiation, for the characterization of coexisting organizations resulting from the phase separations of TG and to follow the competition between the different polymorphic species quantitatively, even at fast scanning rates. Cocoa butter polymorphism Using temperature-resolved XRD with MICROCALIX, it has been shown that phase separation systematically occurs during CB crystallization (Loisel et al. 1998). A trisaturated fraction of TG partially phase-separates from the mono- and polyunsaturated TGs by crystallizing first on cooling. Segregation of TG molecules in the solid state has been observed when CB crystallizes under the forms II and V (the form under which chocolate is usually commercialized). It also can occur with some other forms, such as VI (Loisel et al. 1998a). This behavior results from the poor solubility of trisaturated TG within the monounsaturated ones. The use of MICROCALIX for the monitoring of the formation and then transformation of the different forms, including form III, during heating of CB confirmed unambiguously the results of phase transitions previously reported by Wille and Lutton (1966). Other approaches for obtaining the desired form V have been undertaken using the knowledge of these phase transitions upon heating and using the influence of shear and additives (Loisel et al. 1997a, 1998b), providing a better understanding of the polymorphism of CB. The resulting knowledge is also relevant for the study of fat bloom (Loisel et al. 1997b), which must be hindered by means other than additives. The very fast cooling (about 100 °C/s) of melted CB results in the formation of a phase that is less organized than the α form, since it transforms irreversibly into the latter on heating (Loisel et al. 1998a). It is believed that metastable forms can be used for the rapid formation of desired polymorphic form V. Partly liquid crystalline structure, in which hydrocarbon chains are organized as in a “brush” may occur even in a partially crystalline state (Loisel et al. 1998a). Such an organization also can be compared with that shown by phospholipids, such

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as phosphatidylcholine, in the liquid crystalline state (Lα). This degree of freedom in the molecular moiety left by the liquid part of the organization allows the other moiety to crystallize very rapidly and into a very compact subcell. On the other hand, the presence of a liquid crystalline moiety in the structure would also explain its progressive transition into the α form, since it is well-known that the presence of a liquid favors the transition of unstable species toward more stable forms. This is well-known for fats (Timms 1984). This preorganization assumes that (1) the liquid state of CB, as other liquid fats, is liquid crystalline and already organized in lamellae in their liquid state (van den Tempel 1979), and (2) its organization corresponds to layers (or flat aggregates) made from saturated chains while other parts contain the unsaturated ones. Polymorphism of 1,2-dipalmitoyl-3-oleoylglycerol As an example of the rapid liquid-mediated phase transitions, the heating of a crash-cooled sample of 1,2-dipalmitoyl-3-oleoylglycerol (PPO) illustrates the importance of using the coupled techniques such as MICROCALIX (Ollivon et al. 2006). With the cooling of PPO from a melted state at 65 °C down to 0 °C, about 20 mg of pure PPO (99%) at the rate of 5 °C/min leads to the crystallization of a metastable crystalline variety (type 3Lα). On heating, this metastable form transforms into more stable varieties (Figure 8.4, bottom). The DSC thermograms and the XRD patterns observed at small and wide angles on heating at 1 °C/min are presented in Figure 8.4. Four steps can be identified on both DSC and XRD traces (Figure 8.4, middle). Three endothermic and one exothermic event are observed in the domain 20 ° to 40 °C on the DSC heating scan. The metastable structure (3Lα) initially formed with a period of about 75 Å (only order 2 line at 37.6–37.7 Å is shown on Figure 8.4) transforms into a mixture of two forms (both of β′ type). One form has intermediate stability and the other form with smaller d spacing is stable (close to 33 and 40 Å). The two forms melt consecutively (vertical dashed lines delimit the domain of existence of phases; Figure 8.4, middle). DSC thermograms display several overlapping thermal events. As a consequence, in the intermediate part of this double transition, between the maximum of both peaks, only the result of both phenomena is recorded and schematically explained (Figure 8.4, top). Such behavior is frequently observed in monotropic systems during melting of metastable crystalline forms when more stable nuclei

Coupling of Differential Scanning Calorimetry 20°C

unstable form a (3L) 75 Å

intermediate form b’2 (2L) 39–41 Å 30°C

183

liquid 35°C

50

3 2

10

4

40 30

1

20

5

10 0 0

1000

0 3000

2000

Temperature (°C)

Peak Area (A.U)

21°C 15

Long Spacings (Å)

stable form b’1 (3L ?) 64–66 Å 42

3 1 32 0

10

time (sec)

3 2 1

0.2

q (Å–1)

30

40

50

endo

50 45 40 35 30 25 20 15 10 5 0

60 45 40 35 30 25 20 15 10 5 0 0.1

20

temperature (°C)

0.3

50 Temperature (°C)

4

4

2

37

1.4

1.6

q (Å–1)

1.8

4

40

3 2

30 20

1

10 0 –100 –50

0

50

100

Heat flow (mV)

Figure 8.4. MICROCALIX recording of SAXS/WAXS and DSC. Heating from 0 ° to 50 °C at 1 °C/min of a 20-mg sample of PPO. Three-dimensional representation of the evolutions of diffraction patterns recorded at small (left) and wide (middle) angles and shown as intensity as a function of scattering vector q and temperature T during the heating of sample. The four types of structures that are clearly visible in the domain 0 ° to 50 °C are delimited by the transitions evidenced. The line corresponding to order 2 of the structural period of 75 Å is voluntarily cut to allow a better visualization of other lines at high temperature.

formerly entrapped in this metastable variety are allowed to grow at the expense of the metastable variety. This type of complicated thermal recording can be elucidated only if coupling of a structural technique is provided. By using MICROCALIX, it is confirmed that the transition toward the more stable crystalline form goes through an intermediate stability form (Ollivon et al. 2006). This monotropic transition is liquid mediated. This intermediate form, more stable than the initial form, melts between 27 ° and 30 °C. A schematic of the proposed mechanism of

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the first reaction is seen in Figure 8.4 (top); however, mechanism of the second reaction is more complex. Only the quantification of the evolution of the line intensities (Figure 8.4, middle), corresponding to the long spacings and measured as peak surface areas, allows to interpret the second transition. It can then be seen that two forms coexist in the 20 ° to −30 °C range, while the periods of the crystalline arrangement change (Figure 8.4). In fact, the mechanism of transition is similar to the preceding one, except no exothermic event is recorded. The existence of a liquid crystalline phase obtained by very fast cooling was not found in pure TG such as triolein, tristearin, or even POP, which is nevertheless one of the major constituents of CB, because they all readily crystallize in α phase. The usefulness of MICROCALIX is underlined by the explanation of complex phase changes as they frequently occur in lipids. Only clear attribution of thermal events as they are given in the examples above can lead to a thorough understanding of polymorphism of lipids and other materials showing polymorphism. Milk Fat Milk fat is consumed in dairy products, that is, milk, cream, whipped cream, cheeses, and butter, and also in powders, pastries, and cooked foods. Milk fat can in be partially crystallized form (e.g., a mixture of crystals and oil) over a wide range of temperatures, including the temperature of storage (4 °–7 °C) and consumption. This thermal behavior results from its fatty acid composition and polymorphism of TG. Milk fat is the most complex fat found in nature, with more than 400 different fatty acids (about 70% of saturated fatty acids and 25% of monounsaturated fatty acids, mainly oleic acid) and 200 different TG identified. DSC and XRD studies have given an insight into the thermodynamics of milk fat phase transition in bulk (Timms 1980; Lavigne 1995; ten Grotenhuis et al. 1999), in emulsions (Lopez et al. 2002b), and in complex food products such as cheese (Rowney et al. 1998; Famelart et al. 2002; Lopez et al. 2006a). Anhydrous milk fat Anhydrous milk fat (AMF), which is the fat isolated from butter, has a broad melting range, from −40 °C to +40 °C, and no true melting

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185

point as do pure compounds. DSC was used to demonstrate the presence of polymorphism in anhydrous milk fat (Mulder and Walstra 1974). When polymorphism is present, the thermograms for samples of the same fat preconditioned thermally in different ways will have different features. DSC studies of anhydrous milk fat show that it crystallizes and melts in several steps (Lopez et al. 2007). A typical melting curve of AMF shows three endothermic peaks, corresponding to low melting point (LMP), medium melting point (MMP), and high melting point (HMP) fractions (Timms 1980). These peaks correspond to large groups of TG that melt separately and behave as solid solutions. The number of thermal transitions in DSC thermograms, the partial overlapping of the melting peaks, and their respective enthalpies and transition temperatures, depend strongly on the thermal treatments (e.g., heating and cooling rates, tempering) and on the entire thermal history of the sample (Ollivon and Perron 1992; Ali and Dimick 1994). Recently, the use of DSC coupled to synchrotron radiation XRD with MICROCALIX allowed identification of the crystalline structures formed by TG molecules as a function of temperature and time in anhydrous milk fat (Lopez et al. 2001a,b, 2005) and its fractions (Lavinge 1995; Lopez 2006b). The samples were melted completely (heated to 60 °C for 5 min) to ensure that all crystals and nuclei were melted and to erase the thermal history of fat. The samples were then cooled with cooling rates in the range of 0.15 °C. min−1 ≤ Rcooling ≤ 1000 °C.min−1. The most rapid Rcooling was obtained by rapid introduction of the capillary into the calorimeter MICROCALIX precooled to the temperature, for example, 4 °C. Tempering in isothermal conditions were also performed, for example, at −8 °C, 4 °C, and 20 °C. Then, XRD patterns were recorded as a function of time and on subsequent cooling or heating (in general at 2 °C.min−1). Figure 8.5 shows the crystallization curve of anhydrous milk fat recorded on cooling at 1 °C/min, with the XRD patterns recorded as a function of temperature, which allows the relation between the thermal events and the crystalline structures. The crystallization properties of milk TG were studied after quenching (∼1000 °C.min−1) to characterize the most unstable crystalline structures and their reorganization as a function of time (Figure 8.6). The samples were cooled rapidly from 60 °C to 4 °C to ensure crystallization of fat, and after temperature equilibration, the thermal

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Figure 8.5. Crystallization properties of anhydrous milk fat. Left: Three-dimensional plots of the XRD patterns recorded at small and wide (insert) angle during cooling from 60 °C to −7 °C at 1 °C.min−1. Right: Evolution of the maximal intensity of the XRD peaks recorded at small angles, allowing relation of the structural data to the thermal properties recorded simultaneously by DSC.

properties were investigated as a function of time under isothermal conditions. Isothermal DSC was performed, with the temperature being kept constant at 4 °C to study the thermal and the structural properties of TG. The heat of crystallization released as a function of time resulted in exothermic signals corresponding to the polymorphic evolution in the fat (α to β′ polymorphic transition as indicated in Figure 8.6). The nucleation time (the time at which a peak starts forming), time of maximum crystallization rate (the time of peak maximum), and heat of crystallization (proportional to peak area) can all be determined from the thermogram. The absence of exotherm recorded by DSC for cream at 4 °C indicated that no polymorphic reorganizations occurred in milk fat globules during the 30 min after their quenching from 60 °C (Figure 8.6). These studies indicated differences in the polymorphic behavior of TG as a function of their organization, in bulk or dispersed in emulsion.

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187

Figure 8.6. Left: Three-dimensional plot of the isothermal evolution of small-angle and wide-angle (insert) XRD patterns recorded at 4 °C after rapid quenching from 60 °C of anhydrous milk fat (AMF). Upper left: Time evolution of the intensities, taken at the peak maximum and normalized to 100%, of the XRD patterns recorded at small angles; DSC recordings of AMF and cream obtained simultaneously with XRD experiments.

DSC and XRD investigations showed that the fat phase of dairy products displays a complex polymorphism. Depending on the cooling rate, six different types of crystals were identified, several of them in coexistence, and their time- and temperature-dependent evolutions were quantitatively monitored. They correspond to lamellar structures with 2L (40.5–48 Å) and 3L (54–72 Å) organizations of TG. At least five crystalline subcell species were observed at wide angles: α and sub-α, two β′, and one β. All these crystalline structures coexist with a liquid phase even at low temperature (T < 4 °C). Thermal events recorded by DSC were related to the structural information on the organization of TG obtained by XRD. These experiments focusing on the crystallization properties of whole milk fat and fat fractions characterized by different composition and thermal properties contributed to the development of spreadable butters with defined solid fat content as a function of temperature.

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Polymorphism of TG in milk fat globules In milk, lipids are naturally dispersed as small droplets (4 μm) called the milk fat globules. Studying the crystallization of TG in milk fat globules is of prime importance because it affects many properties, such as (1) rheological properties, (2) resistance of fat globules to disruption and then to coalescence, (3) susceptibility of globules to churning for the manufacture of butter, (4) stability of whipped cream, and (5) consistency and mouth feel of high-fat products. Thus, it is important to understand better the physical properties of fat globules, for example, their thermal and crystallographic properties, for industrial applications and to improve the quality of food products. Moreover, it is interesting to compare crystallization of fat dispersed in an emulsion such as milk or cream (which is the concentration of fat globules from milk) in which fat globules are surrounded by a membrane rich in phospholipids with crystallization of bulk milk fat. Lopez et al. (2002a,b) showed that the dispersion state of milk fat, for example in bulk as anhydrous milk fat or dispersed in fat globules, alters both its thermal and structural properties. The use of DSC coupled to synchrotron radiation XRD allowed identification of the crystalline structures formed by TG molecules as a function of temperature and time in dispersed systems such as milk fat globules (Lopez et al. 2000, 2001c, 2002a, b). Figure 8.7 shows that slow cooling of cream (0.15 °C.min−1) leads to the recording of a single exotherm corresponding to crystallization of TG in fat globules. The organization of TG molecules in the solid state (e.g., in fat crystals) investigated using XRDT allowed the identification of four crystalline structures that are successively formed as a function of the decrease in temperature (Lopez et al. 2001c). Studies on milk fat globules showed that the temperature of the beginning of crystallization is lowered as a function of the decrease of their size (Lopez et al. 2002b; Michalski et al. 2004). XRD permitted the identification of different crystallization behavior in natural milk fat globules with different sizes, which could be implicated in the manufacture of dairy products involving tempering periods in the technological process (butter, ice cream, whipped products). The examination of TG polymorphism in milk fat globules is much more challenging than for fat in bulk and especially difficult because (1) both small- and wide-angle XRD should be considered at the same time and compared to determine the evolution of each of the species

Coupling of Differential Scanning Calorimetry Small-angle XRD

2L1 46.5Å 3L2 65Å

b’

0.15°C/min

3L2(002)

0

3L1(003)

10 20 Temperature (°C)

30

a

3L1(005)

–5

16 27

6

T (°C)

22°C

–4

Liquid

6

16

37

26

47 0.1

0.2

0.3

q (Å–1)

0.4

0.5

T (°C)

3L1(001) 2L2 71.3Å 40Å

Wide-angle XRD

Endo > (u.a.)

3L1(002)

12°C 20°C

DSC

189

36 1.1

1.3

1.5

1.7

q (Å–1)

Figure 8.7. Structural evolution, expressed in scattering vector q (Å−1), of TGs dispersed in milk fat globules during cooling from 60 °C to −8 °C at 0.15 °C.min−1. Three-dimensional plots of the XRD patterns recorded at small angles (left) and at wide angles (right). DSC curve recorded simultaneously.

as a function of time; (2) the X-ray intensity diffracted by each of the crystalline structures is proportional to the fraction of particular crystal in the structure; (3) the whole XRD signal is largely absorbed by the surrounding water and its solutes (e.g., casein micelles, minerals, lactose); and (4) the peak broadening results from the crystallization constraints in dispersed systems and the smaller size of the crystals. Crystallization properties of fat in dairy products The crystallographic and thermal properties of fat in complex food products have also been investigated. The melting properties of butters with different fatty acid composition showed different DSC profiles, which have been related to the textural properties of the butters (Lopez at al. 2007). Lopez et al. (2008) identified the crystalline structures formed by TG in Emmental cheese at 4 °C and their melting behavior upon heating. Recently, DSC was used to investigate the thermal properties of fat in cheese. Lopez et al. (2006a) showed that the liquid-tosolid phase transition recorded by DSC upon cooling is sensitive to the

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destabilization of fat globules and the formation of nonemulsified fat during the manufacture of Emmental cheese. Moreover, these authors developed a protocol to determine the solid fat content in cheese at 4 °C and the evolution of the ratio of solid to liquid fat as a function of temperature (Lopez et al. 2006a). Lard Pork production is estimated at about 93 × 106 tons, of which approximately 50% and 22% are produced in China and Europe, respectively. Pork represents almost 40% of worldwide daily meat protein intake. Lard is the fat obtained by rendering fatty tissue of the hog, the domestic pig. Natural lard has a characteristic waxy texture and exhibits unsatisfying bakery qualities that are frequently corrected by fat blending, partial hydrogenation, or interesterification in making commercial shortenings. The composition of lard varies with the hog’s food and is mainly composed of a few long-chain major fatty acids, including C16:0 (∼24%), C18:0 (∼14%), C18:1 (∼41%), and C18:2 (∼10%). Although composed of only a few TGs, lard, as many other fats, exhibits complex thermal properties. DSC thermograms of lard exhibit several peaks upon heating or cooling of samples (Figure 8.8). These peaks reflect the occurrence of numerous thermal transitions, the temperatures and enthalpies of which vary as a function of sample thermal history. The underlying polymorphic transitions of DSC peaks have been identified (Table 8.3) and shall be illustrated for fast cooling and heating rates at 5 °C/min. For each of the thermal events recorded upon crystallization, a distinct structure of TG molecules is evident (Figure 8.9, top). Even at fast cooling rates, the c1 thermal event is associated with the formation of a 2L α form, whereas c2 and c3 can be attributed to two 2L β′ structures from the evolution of the peak intensities as a function of temperature (Figure 8.9, bottom). It is important to point out that that c1 crystallization did not occur at cooling rates lower than 2 °C/min. In addition, at lower cooling rates the structure formed during c2 exotherms crystallizes in a 2L β structure (Kalnin et al. 2005). Upon heating, more than seven endotherms have been observed and attributed to the polymorphic forms (Figure 8.9) by using MICROCALIX (Kalnin et al. 2005). Two main melting endotherms, the temperature positions of which vary widely, are observed around 0 ° and 30 °C.

e4 e3

5.0

e2

0.2 (× 12)

Normalized Heat Flow (W/g) →

4.0

0.5 (× 5)

3.0

1 (× 2.5)

2.0

2 (× 2.5) endo

cooling

1.0

c2

c3 Tonset (c3)

0.0

5 (× 2)

c1 Tonset (c1)

Tonset (c2)

10 cooling rate (rc) in K/min

–1.0 –40

–20

0 20 Temperature (˚C)

40

60

Figure 8.8. Characteristic DSC curves of the crystallization of lard. The influence of the cooling rate (rc) is evidenced in the range of −0.2 up to −10 K/min as indicated. The normalized heat flow is scaled to the cooling rate of −10 K/min. DSC curves are shifted relatively to each other and multiplied for clarity as indicated in brackets. When not associated to Tonset, arrows indicate minor exothermic events.

Table 8.3. Main crystallographic parameters of the fat crystals in lard and their attribution to major DSC peaks. Crystalline Form/ Subcell α (hexagonal) β′1 (orthorhombic) β′2 (pseudoorthorhombic β triclinic)

SAXS Peaks d (Å)

WAXS Peaks d (Å)

DSC Exotherms

48.2 35.1

4.18 4.14, 3.83

c1 c2

h3 h4, h5

43.8

3.95, 4.4, and 4.3

c3

h7, h6

43.8

4.6

c2 (only upon slow cooling rates)

h1, h2

191

DSC Endotherms

40.0°C 500

34.6°C 40.0°C 34.4°C 29.2°C 18.9°C

29.2°C 400 18.9°C 8.4°C l (cps)

300

Heating at rH = 5 K/min

c2 U-shaped

200

43.6Å c2 35.1Å

100 c1 48.2Å 0

0.05

0.10

0.15

4.14Å c3

–20.5°C

Crystallization at rC = 5 K/min –15.9°C –12.7°C –4.8°C 8.9°C 13.9°C 19.2°C

0.20

0.25

0.30

c1 4.18Å

1.10

1.20

1.30

1.40

1.50

8.4°C

WAXS

c2

q (Å–1)

c2 β’ = 3.83Å

1.60

–20.5°C –12.7°C –4.8°C 8.9°C 13.9°C 19.2°C

1.70

1.80

q (Å–1)

60

DSC

SAXS

WAXS

h 2 + h1

40

h3

h4 h5

20

Temperature (°C)

c3 3.95Å β’ = 3.79Å

c3

rH = –5 K/min

h6

0

h7 –20 0

c3 rc = –5 K/min

c1

c2

20 48.2 Å 43.8 Å 35.1 Å

endo

40

4.15 Å 3.8 Å 3.95.1 Å + 4.3 Å + 4.4 Å

60 –6

0 Heat Flow (mW)

6

1.0

0.5 0.0 1.0 0.5 Relative Peak Intensity (%)

0.0

Figure 8.9. A selection of small and wide angle X-ray scattering (SAXS) patterns at the average temperature indicated is redrawn on top to illustrate crystallographic properties of lard. Pure tristearin (SSS) WAXS pattern is shown for comparison (dashed line). All X-ray patterns were recorded for 60 s and shifted relatively to each other for clarity. The corresponding DSC curves are drawn for comparison next to SAXS and WAXS relative peak intensity plots vs. T. Evolution of three SAXS and five WAXS lines were followed and plotted with normalizations on the peak maximum intensity (figure redrawn from Kalnin et al. 2005). Main crystallographic parameter of the crystals in lard and their attribution to major DSC peaks (right).

192

Coupling of Differential Scanning Calorimetry

193

They could be attributed to the mono- and di-unsaturated fractions crystallizing in 2L β′ forms according to the variation of the peak intensities as a function of temperature (Figure 8.9, bottom). The two overlapping endotherms, h1 and h2, the importance of which increases with decreasing cooling rate, are only recorded at T > 40 °C and show that transition of 2Lα 2L β′ form. The observed transition at high temperatures, however, is dependent on the whole thermal history. Thus, h1 is only observed at low cooling rates or after a longer time. It can be concluded from this study that, in general, saturated, monosaturated, and polyunsaturated TGs do not cocrystallize at all cooling rates, but intersolubility is temperature and time dependent. The phase separation between layers of saturated-saturated-saturated TG, saturated-saturated-oleic acid TG, and saturated-oleic acid-saturated TG was supposed to lead to an alternate structure due to oleic acid esterified in sn-2 and sn-3 positions, which might explain the diffraction patterns for the observed pseudo-orthorhombic structure (Kalnin et al. 2005). Moreover, structural transitions take place after rapid crystallizations even at very low temperatures due to α to β′ transition based on isothermal recordings using MICROCALIX (Kalnin et al. 2005). Coupling of DSC and XRDT using MICROCALIX allows the partial identification of the structures developed during the thermal treatments, both fast and slow rates, and permits the assignment of the thermal events recorded by DSC. Once this identification has been established, conventional not “coupled” DSC analysis can be undertaken using commercially available DSC. This knowledge is further used for fractionation of lard.

Conclusion Elucidating the polymorphism of lipids is important to better understand the properties of fat-rich food products and to improve food technology. The presence of one or several long fatty acid chains and their different packings lead to a variety of polymorphic forms that cause the thermal and structural behaviors of lipids to be rather complex to study. Both types of properties are currently measured by XRD and DSC

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alone. The thermal and structural characterizations of fats are generally obtained on an independent apparatus, which does not facilitate the correlation between both types of phenomena. The new instrument, MICROCALIX, allowing simultaneous (time-resolved synchrotron) XRD at both wide and small angles as a function of temperature (XRDT) or time (XRDt), coupled with high-sensitivity DSC, permits the study of lipid properties for food applications. The technique has demonstrated the power of coupling for the investigation of the properties and structures of lipids. MICROCALIX has also shown its usefulness in domains such as chemistry and biology for cosmetic and pharmaceutical applications.

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Famelart M.H., Le Graet Y., Michel F., Richoux R., and Riaublanc A. 2002. Evaluation des méthodes d’appréciation des propriétés fonctionnelles des fromages d’Emmental de l’ouest de la France. Lait, 82:225–245. Guinier A. 1964. Théorie et Technique de la Cristallographie, 3rd edition. Dunod: Paris. Hagemann J.W. 1988. Thermal behaviour and polymorphism of acylglycerides. In: Crystallisation and Polymorphism of Fats and Fatty Acids, Garti N. and Sato K., editors, pp. 9–9. Marcel Dekker: New-York. Huyghebaert A. and Hendrickx H. 1971. Polymorphism of cocoa butter, shown by differential scanning calorimetry. Lebensm-Wiss U Technol, 4:59–63. Kalnin D., Garnaud G., Amenitsch H. and Ollivon M. 2002. Monitoring fat crystallization in aerated food emulsions by combined DSC and time-resolved synchrotron x-ray diffraction. Food Res Int, 35:927–934. Kalnin D., Lesieur P., Artzner F., Keller G., and Ollivon M. 2005. Systematic investigation of lard polymorphism using combined DSC and time-resolved synchrotron x-ray diffraction. Eur J Lipid Sci Technol, 107:594–606. Kunutsor S.K. and Ollivon M. 1983. Ternary phase diagram of β stable forms of major triglycerides of cocoa butter (POP, POS, SOS). 16th ISF research, World Congress: Budapest. Lavigne F. 1995. Polymorphisme et transitions de phases des triglycerides. Applications aux propriétés thermiques et structurales de la matière grasse laitière anhydre et ses fractions. PhD thesis, Univ Paris VII, Paris XI and ENSIA, France. Loisel C., Keller G., Lecq G., Launay B., and Ollivon M. 1997a. Tempering of chocolate in a scraped surface heat exchanger. J Food Sci, 62:773–780. Loisel C., Lecq G., Ponchel G., Keller G., and Ollivon M. 1997b. Fat bloom and chocolate structure studied by mercury porosimetry. J Food Sci, 62:781–788. Loisel C., Keller G., Lecq G., Bourgaux C., and Ollivon M. 1998a. Phase transitions and polymorphism of cocoa butter. J Am Oil Chem Soc, 75:425–439. Loisel C., Lecq G., Keller G., and Ollivon M. 1998b. Dynamic crystallization of dark chocolate as affected by temperature and lipid additives. J Food Sci, 63:73–79. Lopez C., Lesieur P., Keller G., and Ollivon M. 2000. Thermal and structural behavior of milk fat: 1. Unstable species of cream. J Colloid Interface Sci, 229:62–71. Lopez C., Lavigne F., Lesieur P., Keller G., and Ollivon M. 2001a. Thermal and structural behavior of milk fat: 1. Unstable species of anhydrous milk fat. J Dairy Sci, 84:756–766. Lopez C., Lavigne F., Lesieur P., Keller G., and Ollivon M. 2001b. Thermal and structural behavior of anhydrous milk fat: 2. Crystalline forms obtained by slow cooling. J Dairy Sci, 84:2402–2412. Lopez C., Lesieur P., Bourgaux C., Keller G., and Ollivon M. 2001c. Thermal and structural behavior of milk fat: 2. Crystalline forms obtained by slow cooling of cream. J Colloid Interface Sci, 240:150–161. Lopez C., Bourgaux C., Lesieur P., Bernadou S., Keller G., and Ollivon M. 2002b. Thermal and structural behavior of milk fat: 3. Influence of cooling rate and droplet size on cream crystallisation. J Colloid Interface Sci, 254:64–78.

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Lopez C., Bourgaux C., Lesieur P., and Ollivon M. 2002a. Crystalline structures formed in cream and anhydrous milk fat at 4 °C. Lait, 82:317–335. Lopez C., Lesieur P., Bourgaux C., and Ollivon M. 2005. Thermal and structural behavior of anhydrous milk fat. 3. Influence of cooling rate. J Dairy Sci, 88:511–526. Lopez C., Briard-Bion V., Camier B., and Gassi J.Y. 2006a. Milk fat thermal properties and solid fat content in Emmental cheese: A differential scanning calorimetry study. J Dairy Sci, 89:2894–2910. Lopez C., Bourgaux C., Lesieur P., Riaublanc A., and Ollivon M. 2006b. Milk fat and primary fractions obtained by dry fractionation 1. Chemical composition and crystallisation properties. Chem Phys Lipids, 144:17–33. Lopez C., Bourgaux C., Lesieur P., and Ollivon M. 2007. Coupling of time-resolved synchrotron x-ray diffraction and DSC to elucidate the crystallisation properties and polymorphism of triglycerides in milk fat globules. Lait, 87:459–480. Lopez C., Briard-Bion V., Beaucher E., and Ollivon M. 2008. Multiscale characterization of the organization of triglycerides and phospholipids in Emmental cheese: From the microscopic to the molecular level. J Agric Food Chem, 56:2406–2414. Lovegren N.V., Gline M.S., and Feuge R.O. 1976. Polymorphic changes in mixtures of confectionery fats. J Am Oil Chem Soc, 53:83–88. Marangoni A.G. and McGauley S.E. 2003. Relationship between crystallization behavior and structure in cocoa butter. Crystal Growth & Design, 3:95–108. Merken G.V. and Vaeck S.V. 1980. Etude du polymorphisme du beurre de cacao par calorimétrie DSC. Lebensm-Wiss U Technol, 13:314–317. Michalski M.C., Ollivon M., Briard V., Leconte N., and Lopez C. 2004. Native fat globules of different sizes selected from raw milk: Thermal and structural behaviour. Chem Phys Lipids, 132:247–261. Mulder H. and Walstra P. 1974. The milk fat globule. In: Emulsion Science as Applied to Milk Products and Comparable Foods. Commonwealth Agricultural Bureauxz: Farnham Royal, Bucks, UK. Ollivon M. and Perron R. 1992. Propriétés physiques des corps gras. In: Manuel des Corps Gras, Karleskind A., Wolff J.P., and Guttman J.F., editors, pp. 433–442. Lavoisier: Paris. Ollivon M., Keller G., Bourgaux C., Kalnin D., Villeneuve P., and Lesieur P. 2006. DSC and high resolution x-ray diffraction coupling. J Therm Anal Calorim, 85:219–224. Rowney M., Roupas P., Hickey M., and Everett D.W. 1998. Milkfat structure and free oil in Mozzarella cheese. Aust J Dairy Technol, 53:110. Sato K. 1987. Physical and molecular properties of lipid polymorphs: A review. Food Microstruct, 6:151–159. Sato K., Arishima T., Wang Z.H., Ojima K., Sagi N., and Mori H. 1989. Polymorphism of POP and SOS. I. Occurence and polymorphic transformation. J Am Oil Chem Soc, 66:664–674. Small D.M. 1986. In: Handbook of lipid research. The physical chemistry of lipids. From alkanes to phospholipids. Plenum Press: New York.

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ten Grotenhuis E., van Aken G.A., van Malssen K.F., and Schenck H. 1999. Polymorphism of milk fat studied by differential scanning calorimetry and realtime x-ray powder diffraction. J Am Oil Chem Soc, 76:1031–1039. Timms R.E. 1980. The phase behavior and polymorphism of milk fat, milk fat fractions, and fully hardened milk fat. Aust J Dairy Technol, 35:47–53. Timms R.E. 1984. Phase behavior of fats and their mixtures. Prog Lipid Res, 23:1–38. van den Tempel M., 1979. Crystallization in dispersed systems. In: Physico-chimie des composeés amphiphiles, R. Perron R., P. Bothorel P., editors, pp. 261–264. Colloques nationaux du C.N.R.S. n 938. van Malssen K.F. 1994. Real-time x-ray powder diffraction applied to cocoa butter and graphite intercalates. Ph.D., Amsterdam University, The Netherlands. Wille R.L. and Lutton E.S. 1966. Polymorphism of cocoa butter. J Am Oil Chem Soc, 43:4914–496.

Part 2 Calorimetry as a Tool for Process Design

Chapter 9 Overview of Calorimetry as a Tool for Efficient and Safe Food-Processing Design Alois Raemy, Corinne Appolonia Nouzille, Pierre Lambelet, and Alejandro Marabi

Introduction Generalities About Thermal Analysis and Calorimetry Techniques Methods Samples Thermal Behavior of Food Constituents Carbohydrates (sugars) Lipids Proteins Water Thermal behavior of Raw and Reconstituted Food Safety Aspects Other Thermodynamic Parameters Heat of Solution Specific Heat Heat of Combustion Related Techniques Interest of Calorimetry for the Food Industry Conclusion References

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Introduction In the investigation of foods using thermal analysis and calorimetric techniques, many physicochemical effects can be observed in the temperature range between −50 °C and 300 °C. These thermal phenomena may be either endothermic, such as melting, denaturation, and vaporization, or exothermic, such as crystallization, oxidation, and fermentation. Some exothermic reactions present a hazard in industrial operations or during storage. They can lead either to self-ignition, causing fires or dust explosions in open systems such as spray-dryers, or to pressure increase and bursting in closed vessels such as autoclaves or extraction cells. Glass transitions are observed as a shift in the baseline; this information, associated with humidity and water activity determination, is of particular interest in relation to storage of food powders, but also for gas retention in powders predicted to foam when dissolved. This is the safe-processing aspect of thermal analysis and calorimetry. The thermal behavior of food strongly depends on its composition. We therefore consider primarily thermal characteristics of the major food constituents: carbohydrates, lipids, proteins, and water. Specific thermal phenomena of minor constituents (e.g., caffeine), as well as those of additives such as emulsifiers, are mentioned. Raw and reconstituted foods and finally interactions between food constituents will be considered. Some of these aspects will only be mentioned and not fully discussed. The reader should refer to previous work by the same authors (Raemy and Lambelet 1991; Raemy et al. 2000, 2004). Emulsifiers (endogenous or exogenous) are often used in the food industry to stabilize interfaces in emulsions and foams. When added to an aqueous phase, emulsifiers often spontaneously form self-assembly structures. Such structured fluids can be used as active ingredients for encapsulation or as microreactors for flavor formation. In this context, differential scanning calorimetry (DSC) instruments, especially micro-DSC, can help detect liquid crystal phase transitions and establish phase diagrams. This is the material science aspect of thermal analysis and calorimetry. Other thermodynamic parameters, such as heat of solution, specific heat, and heat of combustion can be determined; they are also important for efficient food-processing design. Here, we focus on heat of a solution that is of great interest for food powder dissolution.

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Some related or complementary techniques also are mentioned to situate thermal analysis and calorimetry in the physicochemistry domain.

Generalities About Thermal Analysis and Calorimetry Thermal analysis and calorimetry are extensively described in the literature (Miller 1982; Hemminger and Höhne 1984; Sestak 1984; Widmann and Riesen 1987; Hemminger and Cammenga 1989; Haines 2002; Claudy 2005). See also Chapter 1. Techniques The most currently used technique today is DSC, which often replaces the older differential thermal analysis (DTA). DSC instruments are classified into power-compensated DSC instruments (Perkin-Elmer instruments) and heat flow calorimeters. Heat flow calorimeters can in turn be classified into Calvet-type calorimeters, where the thermopiles surround the sample and reference cells (Setaram Instruments, Caluire, France) and those where the thermopiles are below the sample and reference crucibles (suppliers being Mettler-Toledo AG, Schwerzenbach, Switzerland; Netzsch-Gerätebau GmbH, Selb, Germany; TA Instruments, New Castle, DE). For isothermal dissolution measurements a Calvet-type calorimeter equipped with a specially designed membrane cell can be used as shown in Figure 9.1. The liquid and the powder sample are placed in the measuring cell separated by a membrane. The same amount of liquid is also placed in the reference cell. The cells are then introduced in the calorimeter, and thermal stabilization is achieved after some minutes. The measurement is then initiated, and after checking for a stable baseline, the membrane is pierced and mixing is started, bringing the solid and the liquid into close contact. The heat released or absorbed is measured in relation to the reference cell. A calorimetric curve is obtained, and the area under the curve is automatically integrated, yielding the heat (J/g) absorbed or released during dissolution of the solid sample. A negative value for the enthalpy of dissolution indicates the emission of heat (exothermic process), and positive values indicate an absorption of heat (endothermic process).

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c

Mixing rod Liquid reservoir Aluminum membrane Recipient for solid sample

Figure 9.1. Instrumentation used for performing heat of solution measurements (Calorimeter Setaram C80 and cell with membrane): (a) general view, (b) the heating block showing the geometry for the reference and measurement cells, and (c) the membrane mixing cell. The solid sample and the liquid are efficiently separated during the thermal stabilization step, without risk of moisture transfer. From Marabi et al. (2007b). Courtesy of Setaram.

Other calorimeters are used for specific applications, for example, adiabatic calorimeters and the accelerating rate calorimeters, or ARC (Thermal Hazard Technology, Piscataway, NJ). These are particularly useful for process safety as adiabatic conditions are the most dangerous thermal conditions for a product. The more recent technique of modulated DSC (MDSC) or alternating DSC (ADSC) is that in which a modulated temperature signal is superimposed on the temperature ramp to help separate reversible phenomena (e.g., glass transition) from nonreversible phenomena (e.g., relaxation). Adiabatic or isoperibolic bomb calorimeters are used to determine the heat of combustion of foods. Although the values may be important in the context of process safety, they are mainly used to calculate the caloric value of food for human nutrition or when foods are used as energy sources (e.g., bio-ethanol) for engines. Some thermal analysis instruments or microcalorimeters allow either working under virtually fixed pressures (a high-pressure autoclave surrounds the cells) or give thermomanometric information (the cells are linked to a high-pressure system or even directly fitted with pressure sensors). Even if thermomanometric information is rarely given in the literature, these techniques are of great interest for process

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safety applications, as increased pressure is responsible for bursting of autoclaves. For high-sensitivity measurements, various microcalorimeters, working only in isothermal mode (Thermometric AB, Jarfalla,Sweden), or micro-DSC, working in isothermal and scanning mode (Setaram Instruments) are available today. Another way to increase sensitivity is to use high heating rates: 10 °C/min with a standard DSC instrument or up to 500 °C/min with new DSC instruments (sometimes called hyperDSC). For high-resolution measurements, best results are obtained with small samples and slow heating (cooling) rates. Resolution of DSC instruments can be checked with the help of chemical products (Marti et al. 2004). A more extensive classification of the available calorimeters is given elsewhere in the literature (Rouquerol et al. 2007). The criteria for choosing an instrument include temperature range, type of application, ability to work under pressure or under gas flow, sample size, resolution or sensitivity needed, software performances, and budget. Methods DSC measurements can be performed in isothermal or scanning (heating and cooling) mode depending on the instrument and on the goals of the study. The temperature range of the scans has to be decided according to the phenomena of interest. Heating food samples above 100 °C can lead to pressure increase due to water vaporization; there is therefore a risk of cell rupture if sealed cells are used. Cooling food below 0 °C also can provoke a cell rupture due to volume expansion upon crystallization. Generally, to obtain clearer interpretation of the thermal transitions, consecutive scans (generally first and second scans) are performed; they allow identification of which phenomena are reversible and which are not. Sequences of heating-cooling-heating scans are often helpful. Concerning heating rates, the specialist will generally select the value giving the best possible curves (often 5 °C/min with a DSC and 0.5 °C/min with a micro-DSC instrument). However, to use (Arrhenius type) kinetic models to fit the curves, measurements at different heating rates are sometimes required (Roduit 2002). The ARC uses a special

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heat-search procedure to find exothermic phenomena (Raemy and Ottaway 1991). Calibration is checked with metals (e.g., In, Sn, Pb) or chemicals (naphthalene, which has been contested due to health issues; benzoic acid). With Calvet-type calorimeters Joule effect measurements can also be performed with specially designed cells. Samples Generally, food products are available in large quantities and are easy to handle. The size of the samples to study is very important. The samples must be representative of the food of interest; if it is a lipid, a very thin layer (2–5 mg) is sufficient, so most DSC instruments can be used. If the samples are beans (e.g., coffee, cocoa, cereals), large cells are required, thus there is a need for special instruments allowing study of some hundreds of milligrams or even grams. When small sample sizes are used, the reference cell can be empty. When the sample size is larger, the reference cell must be loaded with a material that is inert in the temperature range of interest (generally Al2O3 when studying powders or sometimes water when studying carbohydrate or protein solutions). Reference and sample cells are thus equilibrated.

Thermal Behavior of Food Constituents Foods are mainly composed of carbohydrates, lipids, proteins, and water. In addition, they contain small proportions of minerals and various organic substances. Minerals are often analyzed globally as ash. The organic substances can be vitamins, caffeine, emulsifiers, acids, antioxidants, pigments, polyphenols, or flavors. Before presenting the thermal behavior of raw and reconstituted foods, we first describe the thermal behavior of the main food constituents. Carbohydrates (Sugars) The main phenomena observed during heating of carbohydrates are release of crystallization water, melting, decomposition, gelatinization of starch in the presence of water, and retrogradation of the gel. In

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Figure 9.2. Calorimetric curves of amorphous sucrose at increasing water activities. Setaram Micro-DSC III, 1 °C/min. From Raemy et al. (1993), with permission.

addition, glass transition, relaxation, and crystallization of amorphous samples occur (Raemy and Schweizer 1983; Blanchard and Lillford 1993; Raemy et al. 1993; Roos 1995; Vuataz 2002). Glass transition indicates that amorphous carbohydrates change from the glassy state to the rubbery state during heating; glass transition is often superimposed on the relaxation phenomenon. A glass transition is reversible and observed as a change in baseline, whereas relaxation is a nonreversible endothermic transition. As shown in Figure 9.2, the glass transition (superimposed with relaxation in the first scan) temperature and the crystallization temperature diminish rapidly with increasing water activity. Amorphism, even at low levels (down to about 0.5%), can be detected and quantitatively determined on the basis of the crystallization enthalpies (Raemy et al. 1993), sometimes from the height of the glass transition. In Figure 9.3, the phenomena observed for these crystalline carbohydrate samples are melting followed by decomposition. Tables with melting and decomposition temperatures of carbohydrates as well as corresponding enthalpies are given in the literature (Raemy and Schweizer 1983). Gelatinization of starch-water systems is an endothermic nonreversible phenomenon easily observed by DSC. Retrogradation, which is a slow and low-energy recrystallization process, can be followed by isothermal microcalorimetry (Raemy et al. 1990; Silverio et al. 1996)

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Figure 9.3. Calorimetric curves of crystalline galactose, sucrose, and cellobiose, all three heated in sealed cells up to 260 °C. Calorimeter Setaram C80, 1 °C/min. From Raemy and Schweizer (1983), with permission.

but is more often characterized after a storage period by measuring the melting transition of the retrograded gel. Lipids Oils and fats reveal many thermally induced transitions as a result of heating or cooling. These transitions are fundamental and can be used to elucidate chemical and physical properties of oils and fats. Thermal analysis and calorimetric techniques (mainly DTA, DSC) have been methods of choice for studying thermal transitions in bulk lipids for more than 60 years. They have been proven to be the most efficient techniques for studying thermal effects that occur during melting, crystallization, and oxidation of lipids. More recently, DSC measurements have also been applied for investigating properties of lipids in dispersed systems; thus, numerous recent calorimetric studies concern membrane lipids (phospholipids and glycolipids). Interactions

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between lipids and other macronutrients are also a subject of recent calorimetric investigations. Bulk systems Melting profile DSC melting curves give valuable information on the melting profile of triacylglycerols (TAGs) and fats, for example, how they melt in the mouth. The complexity of thermal profiles of oils and fats is essentially due to their great variety of TAGs. Calorimetry has long been used to determine the melting profiles of TAGs and fats for controlling technological processes such as blending (Bartsch et al. 1990), chemical or enzymatic interesterification (Dian et al. 2006; Vu et al. 2007), fractionation (Herrera and Anon 1991; Bhaskar et al. 1998), and hydrogenation (Daniels et al. 2006). Changes in melting temperature and enthalpy also have been correlated to fat composition (Tan and Man 2000). Solid fat content (SFC), which represents the ratio of solid to liquid in a partially crystallized lipid at a given temperature, can be obtained from the calorimetric melting curve by sequential peak integration (Lambelet et al. 1986; Kaisersberger 1989; Bhaskar et al. 1998). SFC values are currently used in the fat industry for quality control. Accurate determinations of SFC values require, however, knowing the exact melting enthalpy of each phase or of the various fractions present in a sample, which is very difficult to assess for most fats. Polymorphism Polymorphism of TAGs and fats as well as phase transitions between the various polymorphic forms have been extensively studied by calorimetry (Wille and Lutton 1966; Huyghebaert and Hendrickx 1971; Dimick and Manning 1987; Garti and Sato 1988; Arishima et al. 1991; Loisel et al. 1998; Lovegren et al. 1976; Merken and Vaeck 1980; Minato et al. 1997; Rousset 1997; Rousset and Rappaz 1996; Sato 1996; Spigno et al. 2001). These studies have been conducted by measuring the melting enthalpy and temperature (pure components) or temperature range (complex mixtures such as fats) of the phases present in a lipid sample, as shown for cocoa butter in Figure 9.4. For binary or ternary mixtures of TAGs or fats, DSC has been used to determine real or “pseudo” phase diagrams, or iso-solid diagrams, by identifying the domains of the various phases formed (Knoester

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Figure 9.4. DSC heating curves of five polymorphs of cocoa butter. Mettler FP900, 5 °C/min. From Rousset (1997).

1972; Lambelet and Raemy 1983; Ali and Dimick 1994; Culot 1994; Elisabettini et al. 1998; Koyano et al. 1992; Rousset et al 1998; Timms 1994). Kinetics of crystallization Crystallization kinetics of bulk lipids and the formation and stability of their various polymorphs as a function of time and temperature are another domain in which DSC is very useful. For these measurements, the lipid sample has first to be heated to at least 20 °C above the melting temperature of its stable polymorph to erase all memory effects. Kinetic information has been obtained by measuring either isothermally after quenching at the desired temperature (Rousset and Rappaz 1997; Metin and Hartel 1998; Toro-Vazquez et al. 2005) or at constant cooling rate under various cooling conditions (Kawamura 1980; Cebula and Smith 1991). Complex thermal paths such as tempering stages also were studied by calorimetry to understand precisely the mechanisms that induce the appearance of stable crystalline forms (Rousset and Rappaz 1997). Precise kinetic parameters can be determined from isothermal experiments. The variation of SFC as a function of time can be obtained by sequential integration of the crystallization peak. This SFC function is then used to estimate crystallization parameters with the help of the Avrami or more complex models (Kloek et al. 2000; Foubert et al. 2002; Rousset 2002). Nucleation induction times, which are periods

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of time before nucleation appears, also can be determined from isothermal crystallization experiments (Rousset and Rappaz 2001). These kinetic parameters are useful indicators of the nucleation rate and may serve as a basis for predicting how to crystallize lipids in the desired form. Kinetic studies also help to understand the effects of compositional changes (TAGs or other minor components) on crystallization (Garti et al. 1988; Wahnelt et al. 1991; Tan et al. 2000; Vanhoutte et al. 2002a,b). However, as samples are not mixed, results from DSC crystallization studies are often difficult to interpret directly in terms of process operating conditions (Rousset and Rappaz 2001; Hartel 2001). DSC curves are often complex because various modes of crystallization and solid state transformations can contribute to a single peak. To define unambiguously the key signal characteristics such as minima and maxima, as well as end and start points, a method based on the determination of the first and the second derivative of the DSC raw signal has been proposed (Bouzidi et al. 2005). Quality control DSC crystallization and melting profiles of lipids have been used to assess the quality of oils, in particular of heated oils (Gloria and Aguilera 1998; Tan and Man 1999, 2000, 2002). Similarly, contamination (adulteration) of fats and fat-based products can be detected by calorimetry (Lambelet and Ganguli 1983; Bringer et al. 1991; Marikkar et al. 2002). Adulteration was demonstrated by the appearance or a modification (shift in the peak position and peak area) of a thermal transition occuring in the heating or cooling DSC curves of lipid mixtures. Oxidative stability of oils and fats Lipid oxidation is an exothermic phenomenon that can be observed by continuous monitoring of total thermal effect either under isothermal or nonisothermal conditions of measurements (Raemy et al. 1987; Kowalski 1989; Tan and Man 2002; Ulkowski et al. 2005). Measurements can be performed under a static air atmosphere or, preferably, under oxygen flow or oxygen pressure (Litwinienko and Kasprzycka-Guttman 1998). In isothermal experiments, oxidation induction times correspond to the time at which a rapid exothermic reaction between the lipid and oxygen occurred. Tables of oxidation induction times measured by isothermal heat flux calorimetry around 100 °C are reported in the

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literature (Raemy et al. 1987). For edible oils, induction times obtained by calorimetry were shown to correlate well with corresponding values determined by traditional methods (Tan et al. 2002). DSC can therefore be used to assess the oxidative stability of lipids (Raemy et al. 1987; Kowalski 1989; Tan and Man 2002). In the nonisothermal mode, Arrhenius kinetic parameters can be deduced from the shape of the oxidation curves, and these parameters can in turn be applied for calculation of the overall first-order rate constant of oxidation at various temperatures (Litwinienko and Kasprzycka-Guttman 1998; Ulkowski et al. 2005). Antioxidant efficacy Food antioxidant activity can be measured by calorimetry. The efficiency of an antioxidant to protect an oil is measured by the increase of induction time after incorporation of the test antioxidant into the oil. (Raemy et al. 1987; Irwandi et al. 2000; Tan et al. 2002; Giuffrida et al. 2006). A good correlation between DSC oxidative induction time and oxidative stability index determined by other analytical techniques was found (Tan et al. 2002; Gouveia et al. 2006; Giuffrida et al. 2006). Also, the radical scavenging activities of antioxidants can be investigated by DSC monitoring of the polymerization of substrates initiated by radical reactions (Fujisawa and Kadoma 2006). Dispersed systems Emulsifier-water systems Lipid-water systems that can be regarded as models of the lipid matrix of cell membranes include emulsifiers that may exhibit highly ordered self-assembly structures, which are liquid crystalline phases. DSC has been applied to the study of endothermic phase transitions occurring in lipid-water systems (Blume 1991;

Figure 9.5. (a) Presentation of the calorimetric curve of a saturated MAG with 20% water showing melting of different crystalline forms up to 70 °C and weak liquid crystal transitions at 85 °C and 110 °C. (b) Presentation of the following cooling curve, which shows that there is practically no hysteresis between the temperatures of the phenomena; however, the crystalline form melting at 45 °C has disappeared. (c) Presentation of the second heating curve, which confirms the reversible character of most transitions. (a–c) Setaram Micro-DSC III, 0.2 °C/min. From Raemy et al. (2005), with permission.

Heat flow/mW

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Brandenburg et al. 2006) when they transform from the gel to the liquid crystal phase (Chapman et al. 1974; Tölgyesi et al. 1985) and to determine thermotropic and lyotropic behavior of these systems (Briggs et al. 1996; Qiu and Caffrey 1999). Both gel to liquid crystalline β → α acyl chain melting transitions (Mellier 1988) and structural transitions between different three-dimensional structures (Willumeit et al. 2005) were associated with transitions observed in DSC curves. The most pronounced enthalpy changes were observed for acyl chain melting, whereas structural transitions, which were not accompanied by acyl chain melting, exhibited much lower enthalpy changes. Micro-DSC can be used at low heating and cooling rates to detect liquid crystalline transitions of exogenous emulsifier-water systems (Figure 9.5a, b, and c). Similarly, DSC has been applied to investigate the thermal behavior of several emulsifier-water systems modified by changing the pH value, the ionic composition of the environment, or by chemical agents (Tölgyesi et al. 1985; Forte et al. 1998; Fournier et al. 1998). Emulsions DSC is useful as it is sufficiently sensitive to measure transformations in dispersed phases, in particular when used simultaneously with synchrotron X-ray diffraction (XRD; Kalnin et al. 2002). Thermal behavior of lipids in a dispersion or emulsified form has been shown to be quite different from that of the same fat in bulk, for example, crystallization of milk fat (Lopez et al. 2002). Lipid polymorphism in dispersed systems can also be investigated by calorimetry. As shown in Figure 9.6, polymorphism of colloidal suspensions of TAG has been determined based on DSC melting transitions (Bunjes et al. 2007). Proteins The main phenomena observed by DSC, and especially micro-DSC, during heating of protein solutions are endothermic phenomena associated with protein denaturation. These phenomena were first described by Privalov (Privalov and Khechinashvili 1974). These phenomena were then also observed for products containing large amounts of proteins and enough water to allow protein mobility; for example, for proteins of dairy products where small exotherms associated to protein aggregation also can be detected (Unterhaslberger 2006), fish and meat

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Figure 9.6. DSC heating (10 °C/min) and cooling (5 °C/min) curves of tristearin nanoparticles stabilized with phospholipid/bile salt blends containing unmodified (top) or hydrogenated soybean lecithin (bottom) shortly after preparation. The dashed arrow in the bottom panel indicates the thermal range of the exothermic event prior to crystallization. Pyris 1 from Perkin Elmer. Endothermic is upward. From Bunjes et al. (2007), with permission.

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Heat Flux [mW/g]

0

91.9°C

54.9°C

0.5 Endo

–0.5 Denaturation Enthalpy: –1.82 J/g –1 –1.5 –2 –2.5 –3 10

Instrument: Micro DSC III Setaram Scan rate: 1°C/min Sample weight: 0.4152 g

20

30

40 50 60 Temperature [°C]

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Figure 9.7. DSC curve of the ovalbumin fraction of egg (first minus second run). Setaram Micro-DSC III, 1 °C/min. From Ferreira et al. (1997), with permission.

(Harwalkar and Ma 1990), egg (as shown in Figure 9.7; Ferreira et al. 1997; Grinberg et al. 2002), and cereals (Ellepola and Ma 2006). Denaturation can be quantified by the determination of the denaturation enthalpy. As the native protein sample is considered to be not denatured at all, its enthalpy in a first scan corresponds to 100% of denaturation. Thermally processed products can then be considered as partially or totally denatured, and a percentage of denaturation can thus be calculated from the determined enthalpies. The pH is important because it influences denaturation temperature and thus allows finding protective conditions for industrially treated proteins. Protein denaturation can also be partially or even totally reversible according to the results of second runs. For dry proteins, glass transition and oxidation also can be observed with thermal analysis techniques. Water Crystallization (undercooling) of water, melting of ice, and vaporization can be observed with thermal analysis and calorimetry. Since the enthalpies corresponding to these phenomena are quite high (333 J/g for ice melting and 2255 J/g for water vaporization), they can be

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observed easily with standard DSC instruments, even in samples with low water content.

Thermal Behavior of Raw and Reconstituted Food Most physicochemical effects observed with the main food constituents are also found in the calorimetric curves of raw and reconstituted foods; examples are coffee beans, chicory roots, cereals, milk powders, and infant formulas (Raemy 1981; Raemy and Lambelet 1982; Raemy and Löliger 1982; Raemy et al. 1983; Raemy and Schweizer 1983), where carbohydrate decomposition is observed systematically. For milk powders, lactose crystallization and lipid oxidation also are detected. The thermal phenomena observed with pure minor constituents will not be observed, however, once these constituents (e.g., caffeine) are dispersed in a food matrix. Many raw and reconstituted foods contain water. Therefore, measurements of such products in sealed cells above 100 °C must only be performed with great precautions because of pressure increase due to water vapor and gas release during decomposition. In addition to these phenomena, some reactions between food constituents, such as the Maillard reactions, which occur between proteins and reducing sugars, may be observed, for example, as an exothermic phenomenon in calorimetric curves of milk powders or infant formulas (Morgan et al. 2005) obtained with sealed cells.

Safety Aspects Carbohydrate decomposition, which sometimes immediately follows melting, lipid oxidation (especially if oil is present as a layer or at the surface of the product) as well as protein oxidation, and even Maillard reactions may present a hazard in industrial operations (e.g., roasting, high-temperature drying). The role of thermal analysis and calorimetry for determining safe conditions of industrial processes has already been explained elsewhere (Raemy and Löliger 1985; Raemy et al. 1985; Raemy and Gardiol 1987; Raemy 1988, 2001). The application of adiabatic calorimetry to the study of cellulose decomposition, cellulose being

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considered as a model for other foods, has been described in detail (Raemy and Ottaway 1991). Pressure increase due to water vapor pressure, gases evolved during roasting or decomposition, and air compression (the pressure increase due to dilatation of the pressure sensor has to be deduced) can be monitored by thermomanometry, for example, with the C80 calorimeter or with the ARC. In the case of safety studies, thermal analysis and calorimetric techniques must sometimes be applied unconventionally as measurements have to be carried out under conditions close to those of the process to be studied (Raemy 1992; Raemy et al. 2000).

Other Thermodynamic Parameters In addition, to observe the thermal behavior of food as a function of temperature, calorimetric techniques can also be used to determine thermal parameters such as heats of solutions, specific heats, and heats of combustion. Heat of Solution Solution calorimetry is a suitable technique for the study of liquidliquid and liquid-solid interactions (Hogan and Buckton 2000). It allows quantifying the thermodynamic effects that occur during the dissolution process and can potentially give information on the kinetics and the mechanism of dissolution. In a calorimetric experiment addressing the dissolution process, the output is a composite of wetting, liquid penetration, dissolution phenomena (disruption of the solid, removal of surface molecules, and incorporation of the solute molecules in cavities in the solvent), and any other interactions that might occur (Buckton 1995; Gao and Rytting 2006). This is a widely used technique, mostly in the pharmaceutical field. More specifically, solution calorimetry is used for quantifying the heat as a solid dissolves in a liquid. The theory behind the technique has been explained in detail previously (Gao and Rytting 2006). Some examples include the determination of amorphous content of lactose (Hogan and Buckton 2000; Harjunen et al. 2004; Dilworth et al. 2004; Katainen et al. 2005), sucrose and drugs (Gao and Rytting 2006), the enthalpy of solution of α-cyclodextrin (Bastos et al. 2004),

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various saccharides (Miller and de Pablo 2000), and caffeine (Pinto and Diogo 2006), to name a few. The swelling of seaweed and green tea has been studied by means of calorimetry (Miyagawa et al. 1995a,b), and the swelling and dissolution behavior of different polymers and polymer blends used as pharmaceutical excipients have also been reported (Conti et al. 2006). The significantly different dissolution behavior of crystalline and amorphous saccharides has also been studied (Miller et al. 1997; Miller and de Pablo 2000; Salvetti et al. 2007). All crystalline samples examined presented endothermic enthalpies of dissolution, ranging from about 17 to 90 J/g. Conversely, the same samples in the amorphous state showed an exothermic response, with enthalpies between −30 and −80 J/g. This difference is often attributed to the higher entropy and internal free energy of the metastable amorphous material, leading to enhanced dissolution rate and chemical reactivity relative to the thermodynamically more favorable and stable crystalline state (Hancock and Zografi 1997; Hancock and Parks 2000; Hancock 2002; Wong et al. 2006). In the case of lactose, it was proposed that the interactions within the crystalline material were stronger than the hydration process; thus, its dissolution resulted in an endothermic response (Harjunen et al. 2004). In the case of an amorphous material, the solid interactions might be weaker compared with the crystalline counterpart, resulting in a more spontaneous dissolution characterized by a release of energy during the process. In all cases, crystallization leads to a change of the physical structure, possibly leading to impaired rehydration properties (Roos and Karel 1991). The heats of solution (or dissolution) of many pharmaceutical substances also have been measured directly by solution calorimetry, generally with a different experimental setup. Quantitative analysis of polymorphs, solvates, and amorphous forms in pharmaceutical substances has been performed using solution calorimetry (Giron et al. 2004). Studies of polymorphism by solution calorimetry also have been reviewed (Giron 1995). The effect of moisture content on the thermodynamic response of dissolving powders was studied, and a significant change in the enthalpy of dissolution (less exothermic) for amorphous lactose samples stored at increasing humidities was reported (Hogan and Buckton 2000). For α-cyclodextrin, the enthalpy of solution reverted from exothermic for a dry sample to endothermic for a hydrated sample (Bastos et al. 2004).

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This effect was explained in terms of the exothermic nature of the wetting process, resulting in a less exothermic response for a sample that has already adsorbed moisture from the environment, compared with a completely dry sample. It is often argued that the sorption of water by a dry powder is the first stage of wetting and that the first few molecules adsorbed on the surface are responsible for the greatest part of the overall exothermic wetting response (Buckton 1995; Hancock and Shamblin 1998; Hancock and Dalton 1999; Hogan and Buckton 2000). This effect might be observed and can be quantified when a solid sample is exposed to water vapor. However, if the solid is undergoing dissolution, the distribution of moisture in the bulk of the material will govern the calorimetric response as consecutive layers of the solid are exposed to the liquid medium (Marabi et al. 2007b). Assuming that the moisture is uniformly distributed in the solid matrix, a less exothermic response is expected as the moisture content increases, and a transition from an exothermic to an endothermic process might be observed at a limiting moisture content (Bastos et al. 2004). In a dry glassy material, the measured enthalpy might result from the bonds created between the water molecules and the hydrogen-bonding sites (Miller and de Pablo 2000; Lechuga-Ballesteros et al. 2002). Haque and Roos (2006) recently speculated that freeze-dried materials might have a higher amount of hydrogen bonding sites available for sorption of water molecules than spray-dried materials. Indeed, even a small increase in moisture content of a drug above a critical value of 3% was shown to significantly decrease its dissolution rate (Li et al. 2004). Consequently, a less exothermic response due to either higher moisture content of the powder or a reduced number of available hydrogen bonding sites could have major implications in the wetting mechanism of food powders, which in turn might slow down their dissolution process (Marabi et al. 2007b). Faster dissolution kinetics were also reported to be correlated with more exothermic processes under different conditions (Hancock and Parks 2000; Terada et al. 2000; Marabi et al. 2007a). Isothermal solution calorimetry was also used to derive the wettability of finely divided solids (Lazghab et al. 2005) and the surface energy of solids such as silica, quartz, kaolinite, and illites (Zoungrana et al. 1994; Medout-Marere et al. 1998), fluorinated carbons (Spagnolo et al. 1996), talc and quartz (Malandrini et al. 1997a,b), and fumed silica (Yan et al. 2000). By using nondissolving liquids, it is possible to measure the enthalpy of immersion that can then be used to derive

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0.16 Fat content 0.7% 14.3% 29.3% 35.7% 45.0%

Normalized Heat Flow [W/g]

0.14 0.12 0.10 0.08 0.06 0.04

EXO

0.02 0.00

–0.02

0

500

1000

1500

2000

2500

Time [s]

Figure 9.8. Typical dissolution calorimetry curves of a model food powder with increasing amounts of fat. From Marabi et al. (2008), with permission.

the contact angle between the particulate solid and the liquid (Spagnolo et al. 1996; Adamson and Gast 1997). The application of immersion calorimetry could therefore circumvent the difficulties associated with assessing contact angles of food powders. Several reviews on the use of isothermal calorimetry in different (e.g., pharmaceutical, microbiological) applications are available (Buckton 1995; Wadsö 1997), indicating the wide applicability and useful information that can be obtained from this technique, which surprisingly, has not been exploited in the field of food science. The effects of fat content on the dissolution enthalpy and kinetics of a model food powder were reported previously (Marabi et al. 2008). Typical dissolution calorimetry curves for five freeze-dried samples with increasing fat contents are shown in Figure 9.8. Dissolution of all the powders resulted in an exothermic response. Increasing the amount of fat in the samples is clearly related to a decrease in the amount of heat released during the dissolution process. The decrease in the enthalpy of dissolution ranged from 61.9 to 32.4 J/g for samples with 0.7% and 45.0% of fat, respectively (Figure 9.9). This, in turn, is related to the dissolution kinetics of the powders, which also was shown to be significantly affected by increasing amounts of fat in the samples (Marabi et al. 2008). However, when the enthalpy of dissolution is normalized by the amount of fat material, it can be

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14.3

FAT (%) 29.3

35.7

45.0

0 –10 –20

ΔHdiss (J/g)

–30 –40 –50 –60 –70 Enthalpy of dissolution normalized by fat content

–80

Total Enthalpy of dissolution

–90

Figure 9.9. Enthalpy of dissolution of a model food powder with increasing amounts of fat expressed as J/g of sample and as J/g of nonfat material.

seen that very similar values are obtained for all the samples tested (Figure 9.9). This fact indicates that the fat has a minimal contribution to the overall enthalpy measured, which is in agreement with the slight endothermic response measured when pure fat is mixed with water (Marabi et al. 2008). The effects of different moisture contents and the physical state of maltodextrin (MD) and skim milk powder (SMP) also were studied (Marabi et al. 2007b). Namely, three different conditions were studied: after samples were freeze-dried (FD), after equilibration at 54.4% relative humidity, and after FD (water activity again less than 0.01) of the equilibrated sample. The calorimetric curves are shown in Figure 9.10a and b. The dissolution was found to be exothermic for all the tested samples, and the curves showed similar shapes. For both samples, a clear decrease in the response was observed when the FD and the equilibrated samples are compared. When the latter were FD again, the MD resulted in a curve almost identical to that obtained with the original FD samples. In contrast, the SMP sample showed only an intermediate response between those of the FD and the equilibrated samples. The enthalpy of dissolution decreased (became less exothermic) about 12- and 20-fold from the FD state compared with the equilibrated state for the MD and SMP samples, respectively.

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223

a MD DE21 - FD MD DE21 - aW 0.54 MD DE21 - aW 0.54 & FD

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Figure 9.10. Typical dissolution calorimetry curves of the maltodextrin DE21 (a) and skim milk powder (b) at different conditions. All the samples showed an exothermic response; however, increased moisture content or recrystallized lactose led to a less exothermic dissolution process. From Marabi et al. (2007b), used with permission.

After freeze-drying the equilibrated MD sample, more than 98% of the original enthalpy was recovered. These data indicate that the enthalpy of dissolution is a strong function of the water content and that it is reversible to some extent upon drying of the powder, provided a phase change does not occur in the solid. The part of enthalpy not recovered could be explained by the residual amount of water in the

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sample. In contrast to the MD powder, the equilibrated SMP sample that was subsequently FD recovered only approximately 50% of the value observed for the originally FD sample. This effect is related to the nonreversible process of lactose crystallization that occurred in the SMP sample. The enthalpy of dissolution was significantly reduced due to the presence of the crystalline material for which an endothermic effect is expected upon dissolution, with reported values between approximately 52 and 60 J/g (Miller and de Pablo 2000; Harjunen et al. 2004). An approximate reduction of about 8 J/g in the calorimetric response for each 1% of adsorbed water was calculated when both the MD and SMP FD samples were compared with the equilibrated ones. These values are comparable with those observed for amorphous lactose (Hogan and Buckton 2000), sucrose and trehalose (Miller and de Pablo 2000) having different moisture contents, for which an approximate reduction of 3–5 J/g in the net enthalpy for each 1% of water that is adsorbed was reported. Clearly, the physical state of the samples affected the enthalpy of dissolution, with amorphous samples showing a more exothermic response than partially crystalline samples. It was observed optically that the dissolution kinetics were hastened by more exothermic responses, which contributed to an overall spontaneous process, whereas less exothermic responses clearly resulted in a much slower dissolution rate. In conclusion, the current approach demonstrated that the thermodynamic aspect has a crucial importance in the dissolution of food powders and that isothermal calorimetry should be implemented if optimization of the dissolution process is required. Specific Heat Calorimeters are often used to determine specific heats of foods because process engineers request such information when installing new equipment. The methods and a synthesis of results have been presented in the literature (Mohsenin 1980). The values obtained vary between 1.25 J g−1 K−1 for very dry food products (without fat) and 4.18 J g−1 K−1 for water. The moisture content of a food has thus a strong influence on its specific heat value. The specific heat values of a solid increase with temperature; for water, the value is approximately constant between 0 °C and 100 °C.

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For high-precision measurements, synthetic sapphire is used as the standard (Raemy and Lambelet 1982). Sometimes specific heat measurements are combined with glass temperature measurements for food-water systems (Pyda 2002) as well as for polymers (Marti et al. 2006).

Heat of Combustion During burning of a food product, a large amount of energy is liberated. Values determined with calorimetric bombs are about 39 kJ g−1 for fat, 23 kJ g−1 for protein, and 17 kJ g−1 for carbohydrate.

Related Techniques Some related thermal analysis techniques, such as dynamical mechanical analysis (DMA) or dynamical mechanical thermal analysis (DMTA) give rheological rather than thermal information. Also, thermogravimetry, sometimes coupled with gas analysis instruments, can provide a better interpretation of calorimetric curves (e.g., when studying hydrated carbohydrates) by indicating weight losses associated with the observed thermal phenomena. Microscopy techniques, sometimes performed at different temperatures with a hot stage microscope, are often also very helpful in obtaining a clear interpretation of calorimetric curves. Concerning lipids, as assignments of DSC signals may be ambiguous due to the high number of thermal events, calorimetry often needs to be combined with XRD (Chung and Caffrey 1992; Keller et al. 1996) or even synchrotron XRD when transformations are rapid. Recent experiments combining DSC and synchrotron XRD have revolutionized the study of lipid crystallization (Ollivon et al. 2001; Kalnin et al. 2002; Lopez et al. 2002). DSC can also be used simultaneously with microscopy to identify morphologies associated with polymorphs (Rousset et al. 1998). Fat crystallization in oil-in-water emulsions has been followed by DSC in combination with either nuclear magnetic resonance spectroscopy (Özilgen et al. 1993) or XRD (Awad et al. 2001; Ollivon et al. 2001).

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Interest of Calorimetry for the Food Industry The main reason for studying exothermic phenomena (carbohydrate decomposition, fermentation, oxidation) of food with calorimetric techniques is to improve process safety. A precise knowledge of the temperature ranges of these phenomena and the corresponding enthalpies allows adequate safety measures to be taken and helps to diminish the number of fires, explosions, and bursting of autoclaves in foodprocessing plants (see Chapter 15). It leads thus to a favorable context for avoiding personnel injuries and for loss prevention. Exothermic reactions are desired in some processes, for example, roasting, and have to be avoided in others, such as high-temperature drying. Endothermic phenomena, such as melting, must be avoided or monitored to obtain the correct product. For example chocolate should melt in the mouth but not on the hand; thus, the cocoa butter in chocolate should mainly be in the crystalline form V (melting temperature range between 27 °C and 37 °C). Glass transition temperatures are, in combination with water activity and moisture content, of great interest for studying adequate storage conditions of milk powders, for example by avoiding browning and caking. But this concept also has allowed the development of coffee and milk products with desired amount of foam at the top when reconstituted in water or milk. These products, which are typical examples of the so-called glass transition technology, are presently very popular with consumers. Specific heats of foods are requested by process engineers for the design and installation of new thermal equipment (Kaletunç 2007). Heat of solution of food powders in water or milk is of major importance for the food industry because dissolution speed, which is directly related, is one of the main criteria of the consumer when selecting a food powder for preparing an instant drink. Conclusion In addition to this information, which can be obtained with the help of calorimetric techniques, some databases such as the European database EVITHERM can be used to find thermal parameters about food and nonfood products (www.evitherm.org). The Internet sites of the commercial instrument suppliers also give much valuable information.

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Even so, new products, new conditions, and new processes push performance of more calorimetric measurements and testing of new instrumentations, such as isothermal titration calorimetry (ITC), which is of interest for studying such interactions as that between proteins and active substances, or dielectric spectroscopy, which is of interest in the study of proteins or the amorphous state of carbohydrates. A new trend is to use coupled systems for performing evolved gas analysis in relation to the thermal signal; for example, thermogravimetry coupled with mass spectrometry is commonly used today. Thermal analysis and the study of foods have been mutually beneficial: thermal analysis and calorimetry in general have brought much information to food science and processing technology, whereas the study of foods, as the samples are easily available, has allowed the development and the promotion of many calorimetric techniques.

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Chapter 10 Shelf Life Prediction of Complex Food Systems by Quantitative Interpretation of Isothermal Calorimetric Data Simon Gaisford, Michael A.A. O’Neill, and Anthony E. Beezer

Introduction Qualitative Studies Quantitative Studies Empirical Model Fitting Modeling Based on Reaction Kinetics Reactions That Proceed to Completion Calculation of the Initial Calorimetric Signal θ0 Calculation for the Reaction Order Calculation for the Total Heat Released for Complete Reaction Calculation for Reaction Half-Life Calculation for Rate Constant Calculation for Reaction Enthalpy Reactions That Proceed to a Point of Equilibrium Test for Complete Reaction Determination of K Calculation of QT Summary References

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Introduction The analysis of foods and food components presents a considerable challenge, not least because they may contain many ingredients, be 237

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difficult to handle, and may derive from one or more natural sources (which inherently introduces batch-to-batch variability in composition). Indeed, in many respects foods may be considered as complex biomaterials. While qualitative analyses may suffice for some quality assurance protocols (e.g., texture, feel, density), it is often desirable to obtain quantitative data; this is particularly true where food authenticity (i.e., whether a food substance conforms to its label claim) is required. In this context, it is often difficult or complex to use classical assay techniques. It is not necessarily straightforward, for example, to quantify banned additives in foodstuffs using high-performance liquid chromatography or spectroscopy because of the need to isolate the analyte prior to analysis. A discussion of the merits of various analytical tools for determination of food authenticity can be found in Reid et al. (2006). We have long argued that calorimetry, in particular isothermal calorimetry, is ideally suited to the study of complex samples because it offers many unique advantages. First, the measured parameter is heat. This is advantageous because heat can be considered as a universal indicator of change (and note here that change in this context can mean both physical and chemical processes). Thus, it will unquestionably be the case that a sample can in principle be studied with calorimetry. Whether a meaningful interpretation can be made depends only upon the magnitude of the heat change and the number of events occurring (discussed further below). Second, the instrument requires no sample treatment or preparation. The entire sample is housed within an ampoule and monitored in situ; or, if the sample is too large for the ampoule, a fraction is enclosed that is representative of the whole. Thus, the need to isolate a particular analyte, as in a chromatographic assay, is obviated. Finally, the technique does not require optical clarity of a sample and is invariant to physical form, which means that any complex material can be studied in its entirety. There are two principal calorimetric techniques; isothermal calorimetry (IC) and differential scanning calorimetry (DSC). With the former, the sample is monitored at a constant temperature; and with the latter, the sample is subjected to a controlled temperature ramp (usually increasing). Unsurprisingly many of the calorimetric studies of foodstuffs have used DSC (see Raemy et al. 2004; Schiraldi 2004) or temperature-modulated DSC (De Meuter et al. 1999). This is, perhaps, not unexpected since such instruments are uniquely well suited to

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evaluating the consequences of cooking on foodstuffs. There have been much fewer such detailed studies devoted to IC investigations—the subject of this chapter—and yet the type of information obtainable from isothermal investigation can be vastly more enlightening because it is, of course, just such studies of storage over time that may define food stability and shelf life. It may, too, allow investigation of those reactions that occur isothermally at high temperature, such as the cooking temperature. In addition to these patent benefits, recent work has resulted in a new range of analysis methodologies with which to recover quantitative reaction parameters from IC data; these data can be used to inform sample design and improvement. A review of the application of isothermal calorimetry to food stability is thus the topic of this chapter.

Qualitative Studies Quantitative data interpretation usually requires some prior knowledge of the properties of the system under investigation (e.g., number of reacting systems, reaction pathway) as well as a model with some factual basis. While for simple (one- or two-component) systems quantitative interpretation may be possible, inevitably with more complex systems there will be instances in which qualitative outcomes can be indicative of change despite the absence of detailed interpretation of the experimental data. Much of the literature in this field thus reports qualitative data, and it is here that this discussion starts. Qualitative analyses are usually applied to complex systems because in such cases it is not technically possible to interpret multifaceted power-time data. Consequently, this discussion starts with applications of calorimetry to bioprocesses and bioprocessing in which microorganisms are used in the production of, or as an ingredient in, foodstuffs. It is not a simple matter to measure efficacy of bioprocesses in situ because of the heterogeneity of the system (which may change viscosity, density, and optical transparency). Conventional microbiological assay techniques, such as plating and counting, are time consuming, do not provide information on real-time growth in the actual process environment, do not compensate for the fraction of the microbial load that is dead or not viable, and exclude any contribution to efficacy caused by physical effects (such as thickening of the medium).

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With calorimetry, the power signal is quantitatively proportional to the number of viable organisms in the sample, which means the technique is immediately appropriate for counting cell numbers. Indeed, this is one of its primary advantages over plate-counting methods, because the calorimeter only reports heat changes from living organisms and is not subject to error from inclusion of nonviable cells. The technique addresses all of the concerns raised earlier because it is not dependent on the physical form of the sample and does not impose a requirement for optical clarity. An important consideration when using calorimetry to monitor growth of organisms is the repeatability of the growth curves. There can be enormous variation between bacteria cultured on different days; if this variability results in greater heat changes than the process under investigation, no conclusions can be drawn. In attempting to overcome this limitation, Beezer et al. (1976) and Cosgrove (1979) developed procedures to allow storage of frozen inocula of various organisms, including Saccharomyces cerevisiae and Pseudomonas aeruginosa. In this method, a batch of organisms is grown overnight in a bacterial culture medium. Late exponential growth phase cells are harvested, washed in phosphate-buffered saline (PBS), resuspended in 15% v/v glycerol to an organism density of 108 cfu/ml, and frozen in aliquots (1 ml) over liquid nitrogen. Organisms can be stored for more than 6 years in this frozen state and remain viable after thawing with less than 1% decrease in viability. The benefit of using frozen inocula is very tight reproducibility of the growth curves. An example (in this case for P. aeruginosa) is shown in Figure 10.1. Taking the total area under the growth curve (total heat output) as an indicator of organism numbers shows reproducibility to 6.3% (3.51 ± 0.22 J). For S. cerevisiae, the reproducibility is normally no greater than ±1.5% from the mean and never greater than ±2.5% from the mean (Perry et al. 1979). The growth curve represented in Figure 10.1 is complex and characteristic of organism growth in an undefined medium with restricted oxygen (the ampoule is sealed and the oxygen level is limited to that dissolved in the medium and present in the headspace). In brief, the initial exponential phase represents aerobic metabolism, which is then followed by a switch to anaerobic metabolism. Subsequent peaks and troughs represent sequential use of the major carbohydrate sources typically found in a complex growth medium.

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Figure 10.1. Power-time data showing the growth curves for six repeats of P. aeruginosa.

A good example of the very valuable data that can be produced from qualitative analysis of isothermal calorimetric data is provided by studies of the growth of yeasts on a variety of substrates (Perry et al. 1981, 1983). Yeasts play an important role in many food industries, including baking and brewing, and are used in other industries as well, for example, as a means of producing ethanol from renewable sources or for use in single-cell protein (SCP) production. The authors took three commercial strains of S. cerevisiae (D1, distilling; D2, pressed baking; and D3, active dried baking) and two NCYC strains (87, distilling and 239, brewing) and analyzed their growth curves with flow microcalorimetry (an isothermal calorimeter equipped with a cell that allows medium to be flowed through from an external reservoir). In a glucose medium the growth curves of the three baking strains showed little differentiation, although the growth curve of the brewing strain was notably different. In a maltose medium, good differentiation was observed between all strains. One immediate outcome from these data is the ability to use a simple medium (maltose) to characterize the properties of a new strain of S. cerevisiae and select it as appropriate for either baking or brewing. When complex media are considered the utility of calorimetric study increases further. Perry et al. (1983) showed that growth of yeast in

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glucose-maltose media did not show sequential use of the carbohydrates; rather, both sugars were exhausted after the initial growth phase. Discrimination of strains was achieved by adding maltotriose to the medium. Molasses, a waste product of the sugar industry, is a raw material used in both baking and brewing because it is highly suitable for yeast growth. The suitability of a particular batch of molasses is dependent upon the technical processes by which the sugar was manufactured and also by agronomic factors of the cane and beet production. As a result, the growth of yeast in a particular molasses batch can be highly variable, and there will be a direct impact on the production costs and quality of the baked or brewed product. A method that allows rapid assessment of the quality and suitability of a batch of molasses is thus highly desirable and difficult to achieve by classical physical and chemical means. It is known that only part of the carbohydrate reservoir in molasses is of nutritive value to the yeast. As a consequence, analytical data are not sufficient to characterize molasses batches from a bioavailability point of view. Perry et al. (1981) show how interpretation of calorimetric growth curves of S. cerevisiae in molasses samples could be used rapidly to identify optimal growth media, growth curves in molasses identified as “poor” being distinct from growth in molasses typed as “adequate-to-good.” A similar approach was used more recently by Alklint et al. (2005) to predict the shelf life of carrot juice. Here, growth of the mesophilic and psychrotrophic flora in the carrot juice was monitored after manufacture at 17 °C (the highest permitted temperature for elevated stability tests of chilled foodstuffs in Sweden) with an isothermal calorimeter. It was found that the heat outputs recorded correlated with plate counts, indicating that the calorimetric approach was valid. The initial cause of spoilage was found to be the same at 17 °C as at 8 °C (the maximum permitted storage temperature of chilled foodstuffs in Sweden), and the data were found to be suitable for predicting shelf lives. An area of growing interest is foods that offer some health benefits, so-called functional foods. Prime among these are products that aim to modulate the microflora of the gastrointestinal (GI) tract. Bacterial numbers vary along the human GI tract, increasing from about 103 g−1 of gastric content to about 106–107 g−1 of content at the terminal ileum (Gorbach et al. 1967). The colon, in particular, is a complex and diverse microbial ecosystem, bacterial numbers reaching 1011–1012 g−1

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of gut content (Cummings and Macfarlane 1991). This makes the colon the most metabolically active organ in the body. In addition, the number of bacterial species is large (several hundred), creating a diverse microflora. The numerically predominant bacteria (∼30%) are the bacteroides, although other genera, such as bifidobacteria, eubacteria, lactobacilli, streptococci, and clostridia, are also present (Fooks et al. 1999). The microflora has many roles and is important to general health and well-being. A primary function is in fermenting undigested foodstuffs that have not been absorbed in the upper GI tract. Typically, substrates are carbohydrates (including starches, dietary fiber, and oligosaccharides). The principal fermentation products are short-chain fatty acids (SCFA); these are subsequently absorbed by the body and metabolized, which contributes to the energy gain of the host (Cummings 1995) and means the relationship between host and microflora is symbiotic. Another important function is to inhibit the growth of pathogenic organisms. However, in the absence of sufficient carbohydrate, certain bacteria, such as clostridia, switch to protein fermentation, which produces harmful nitrogenous metabolites (including biogenic amines, indoles, and ammonia). To minimize this effect, the body excretes a number of mucins, which are high in carbohydrates and encourage the growth of certain microbial species. The UK food market is currently replete with products designed to modulate the gut microflora; usually, such products are supplemented with either prebiotics or probiotics. Probiotics are live bacteria of species deemed to be beneficial to health when ingested; usually, lactic acid bacteria (LAB) are indicated and they are commonly found in yogurts, where they convert lactose to lactic acid, giving the product its distinctive sour taste and acting as a preservative. Prebiotics are nondigestible food ingredients that stimulate the growth of one or more beneficial bacteria in the colon and were first defined by Gibson and Roberfroid (1995). Several potential nondigestible foodstuffs have been investigated for prebiotic efficacy, but to date the only substances for which credible data showing a favorable effect are available are the oligosaccharides (Delzenne and Roberfroid 1994). However, it is not a simple matter to show a beneficial prebiotic effect in vivo, primarily because of the sheer complexity of the gut microflora and its effect on physiological response. Typically, an increase in the number of bifidobacteria excreted in feces has

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historically been accepted as proof of efficacy, but it remains to be proven either that an increased level of bifidobacteria in the gut correlates with an increase in health or well-being or that the number of Bifidobacteria is a good biomarker for gut health (Ouwehand et al. 2005). In addition to the complexity of determining an in vivo effect, many probiotic foodstuffs contain multiple cultures, and it is difficult simply to demonstrate that these do not compete with each other. While classical microbiological techniques do not allow direct observation of such processes, calorimetry potentially does. For example, Schäffer et al. (2004) used isothermal calorimetry to monitor the growth of two cultures commonly found in probiotic dairy products. In addition to the added probiotic, Prebiolact (a probiotic organism developed by the Hungarian Dairy Research Institute), the product contains Hansen’s CHN-22 mesophilic butter culture to add aroma. It was found that the growth curves of the two organisms were distinct, the growth curves showing maxima at 4.5 h and 6.5 h for the Prebiolact and the butter culture, respectively. When studied in combination, the growth curve became more complex, but the maxima at 4.5 h and 6.5 h were still present, indicating the two organisms did not interfere with each other. Qin et al. (2006) demonstrated the effect of the water solubility of various chitosans on antimicrobial activity using isothermal calorimetry. The growth curves of Staphyloccus aureus, Escherichia coli, and Candida albicans were monitored in the presence and absence of various chitosans (different molecular weights and N-acetylated derivatives). It was found that water-soluble chitosans had no significant antimicrobial activity and in some cases increased the growth of C. albicans. Water-insoluble chitosans were found to have antimicrobial activity, however, when in an acidic medium. Chitosans with molecular weight of approximately 5 × 104 were found to be most active. Riva et al. (2001) used isothermal calorimetry to evaluate the shelf life of whole eggs, fresh milk, and carrots. The three foodstuffs were all subject to microbial spoilage, and microbial growth was monitored. Complex growth curves were recorded, which showed peaks associated with development of the microbial population. With an increase in temperature, the peaks appeared earlier and were sharper, indicating faster microbial growth. The authors selected the point at which the time derivative of the power signal was at a maximum as the stability

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time. This qualitative outcome was supplemented with data from plate counting and pH measurements, which confirmed the increasing power signal derived from an increase in the microbial population. Galindo et al. (2005) investigated the change in the metabolic response of foodstuffs pre- and postprocessing by isothermal calorimetry. Here, minimal processing operations were considered, such as peeling, grating or shredding. The effect of these operations can impact the quality of the product through several means; changes in respiration rate, increased biochemical reactions through wounding stress and microbiological storage. The rate of many of these processes is dependent upon the surface area of the product, and the authors found relationships between surface area and thermal power for several vegetables (carrots, rutabagas, and potatoes). It was also possible to monitor enzymatic browning of potatoes and the effect of browning inhibitors, such as citric acid or ascorbic acid.

Quantitative Studies Quantitative (which may mean simply determining the number and nature of reaction processes through to recovery of descriptive reaction parameters, such as rate constants, enthalpies, and activation energies) interpretation of complexity in isothermal calorimetric data is demanding. Primarily, this is because, as noted, heat is a universal accompaniment to chemical and physical change. Its ubiquitous nature means that the measured signal is a composite of the powers arising from each of the individual events occurring (which can involve physical, as well as chemical, change). Any meaningful analysis thus has the primary objective of determining the number of processes contributing to the overall data. This number alone is a useful basis for quantitative interpretation, because it could be indicative of a reaction pathway and hence could give some indication of mechanism. Once the number of steps is known, the data must be deconvoluted into their component parts. Once the individual processes are identified, analysis to recover quantitative reaction parameters is usually much more straightforward. Over the past 15 years, a number of methods have been proposed for quantitative analysis of calorimetric data, from simple model fitting to model-free chemometric analysis. Here, these approaches are reviewed and illustrated with examples of application to food products.

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Empirical Model Fitting In the complete absence of any knowledge of the processes occurring in the sample, or in the case where an equation based on a known mechanism is not available, the simplest approach to modeling calorimetric data is to fit the data to an empirical equation (i.e., an equation that conveniently fits the data but does not attempt to describe the reaction processes occurring). A simple example would be to use an exponential decay model, such as that shown in Equation 10.1: −x

y = y0 − A.e t

(10.1)

where x and y are the plotted variables, y0 is the initial value of y, and A and t are constants. For example, some calorimetric data are represented in Figure 10.2. The exact process that gave rise to these data is not important, but it shall be assumed that they represent the heat output of a partially completed reaction. The data can be fitted to Equation 10.1 by least-squares minimization to determine the equation parameters that describe them. Once these values have been determined, it is a simple matter to extend the data to the time at which the power signal falls to zero (shown by the dotted line in Figure 10.2). The area under the dotted line thus represents the total heat, Q, which

Figure 10.2. The fit of calorimetric data to an exponential model and the subsequent extrapolation to power = 0.

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would be generated by the reaction if it went to completion. From this information, it is easy to determine the percentage of reaction completed at any time, t, by taking fractional areas. Hence, it would be possible to determine quantitative shelf-life data, even though the reaction processes remain unclear. Similarly, taking the total heat output of a process (the area under the power-time data) allows quantification of the amount of material reacted if the reaction enthalpy is known. This approach can be used for quite sensitive analysis of components in foodstuffs when an enzyme is used to degrade a specific ingredient, because the enzyme is highly specific for a particular substrate and the calorimeter is able to quantify the reaction in a complex sample without the need for isolation or purification of the reacting components. A number of groups have reported enzymatic methods for quantification of ingredients in foodstuffs. For instance, Forte et al. (1996) demonstrated the utility of using lipolytic enzymes for quantification of fat content in food. Here, pancreatic lipase was used to catalyze the hydrolysis of triglycerides (lipases) in a number of food products (oils, milk, and milk derivatives). A calibration curve was prepared with tributyrin as a model substrate prior to work on the foodstuffs, and it was shown that the calorimetric response of the enzymatic turnover reaction was linear up to a substrate concentration of 15 mM. When analyzing oils, the authors had to dilute the samples 100-fold in buffer prior to analysis to ensure the experiment was conducted in this linear region. Two classes of oils were studied: olive oil (extra virgin, olive, and husk) and seed oil (peanut, soya, and mixed seed). It was found that it was possible to differentiate oils from different classes, but not oils within one class, as their heat outputs were the same within experimental error. It was found possible to differentiate milk samples (whole, semi-skimmed, and skimmed) and also to quantitate fat content to 0.1 g/l. In milk derivatives (yogurts), again a linear response was found for fat contents from zero to 3 g/l, although the slope of the line was observed to be lower than that of the milk samples. The authors ascribed this to the fact that yogurts have a microbial population also capable of producing lipase enzymes that would compete with the assay reaction. More recently, the same group demonstrated a similar approach for the quantification of L-malic acid (Antonelli et al. 2008). In this case, fumarase is used to catalyze the dehydration of L-malic acid. The

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calibration curve was found to be linear to 2.68 g/l; above this concentration, nonlinearity was found as a result of the inverse reaction also catalyzed by the fumarase. The authors compared the assay with a classical spectrophotometric approach and noted the calorimetric technique gave equivalent answers but with no requirement for sample isolation or cleanup. L-malic acid was successfully quantified in a range of beverages (red and white wines, soft drinks, and apple juice) and solid food products (apples, mandarins, and powder for making carbonated water). The use of ascorbate oxidase to quantify ascorbic acid (vitamin C) concentrations has attracted much attention. Antonelli et al. (2002) found a calibration curve of calorimetric response versus ascorbic acid to be linear between ascorbic acid concentrations of 3–270 mg/l. Similarly, O’Neill (2004) used this approach to investigate the quality of fresh orange juices and determined the kinetics of ascorbic acid degradation (discussed further below). A derivative technique that can offer some useful insight into the properties of food substances is solution calorimetry. In this experiment, a solid (usually) sample is introduced to a solvent, and the heat change upon dissolution is recorded (Royall and Gaisford 2005). The technique is useful because small changes in the physical form of a material, such as a change in polymorph or percentage of amorphous content, will result in a change of dissolution enthalpy. Marabi et al. (2007) have shown that solution calorimetry can be used to study the dissolution of two food powders, maltodextrin and skimmed milk. As the moisture content of the powder increased a concomitant decrease in the exothermic dissolution, heat was seen. The effect was reversible if the moisture content was reduced unless crystallization occurred in the sample. The authors used real-time video analysis to follow dissolution kinetics and related these to the calorimetric data. However, it is possible to interpret the calorimetric data quantitatively to recover dissolution parameters. Conti et al. (2006), in a study of the dissolution of various hydroxymethylcellulose (HPMC) polymers, demonstrated how it is possible to convert the calorimetric dissolution data into a plot of swelling ratio; this was achieved by assuming that the total heat output during the experiment, Q (obtained by integration of the power-time data), corresponded to complete swelling, while the heat output to any time t (qt) corresponded to the fraction of swelling that had occurred to that point. Hence, a plot of qt/Q versus time

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Figure 10.3. Power-time data for the dissolution of various grades of HPMC into water. Reprinted from Conti et al. (2006), with permission from Elsevier.

gave a set of data that represented the swelling response; typical plots of the dissolution profile of the polymer and the swelling ratios are shown in Figures 10.3 and 10.4. The swelling curves were then analyzed using a power-law model. It was shown that for all polymers dissolution occurred immediately following hydration of the polymer, although there was a rate dependence upon both polymer grade and solution pH. Modeling Based on Reaction Kinetics The next step in complexity from the use of empirical models is to fit data to models based on reaction kinetics. Clearly, for this approach to be valid, the mechanism of reaction must be known prior to analysis. This approach is also not appropriate for those processes that involve physical change (although if the chemical reaction data can be isolated from any physical change data this approach is legitimate). While the use of such models has been documented in full elsewhere (Gaisford and O’Neill 2006), it is appropriate to discuss the simplest case, a single step, A → P reaction process here; logical extension of the analysis can be made to more complex reaction schemes. The rate of disappearance of reactant A, or the buildup of product P, is given by

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Figure 10.4. The swelling profiles of various grades of HPMC calculated from the power-time data shown in Figure 10.1. Reprinted from Conti et al. (2006), with permission from Elsevier.

dA = kAn dt

(10.2)

A = A0 − x

(10.3)

dx n = k ⋅ ( A0 − x ) dt

(10.4)

− Since

Then

Where dx/dt is the rate of reaction, k is the rate constant, A0 is the initial quantity of reactant A that is available for reaction, x is the quantity of reactant A reacted at time t, and n is the order of reaction. It should be noted that from a mathematical perspective, n may have any value, integral or nonintegral, but to have meaning as a kinetic model only integral values are considered. Immediately, therefore, it is possible to check the validity of a model against real data, for the values of any reaction orders obtained should be integral. For any given reaction that has gone to completion, the total heat evolved

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during the course of the reaction, Q, must be equal to the product of the enthalpy of reaction, ΔH, and the number of moles of material reacted, A0: Q = A0 ⋅ ΔH

(10.5)

q = x ⋅ΔH

(10.6)

It follows that

where q is the heat evolved at time t. Substituting q/ΔH for x and Q/ΔH for A0 in Equation 10.4 and rearranging gives dq n = Φ = k ⋅ ΔH 1− n ⋅ (Q − q ) dt

(10.7)

where θ is the calorimetric power (in watts). Assuming n ≠ 1, integration of Equation 10.7 gives 1

(Q − q ) = [ k ⋅ t ⋅ ΔH 1− n ⋅ (n − 1) + Q1− n ]1− n

(10.8)

This expression may be substituted into Equation 10.7 to give n

Φ = k ⋅ ΔH 1− n ⋅[ k ⋅ t ⋅ ΔH 1− n ⋅ (n − 1) + Q1− n ]1− n

(10.9)

Equation 10.9 describes calorimetric data that derive from reactions that follow a single-step, solution phase process. Calorimetric data from such a reaction may be entered into a suitable mathematical package and, by least-squares minimization, the reaction parameters may be quantified. This process was first described by Bakri (1988) and was later extended by Willson et al. (1995). A further consideration of these equations results in methods to calculate directly the parameters of interest. Knowing these values reduces the burden on the fitting program and increases confidence in the values returned. Here, methods to calculate parameters for singlestep reactions that proceed to completion or to equilibrium are discussed. A discussion of the extension of these principles to more complex reaction schemes can be found in Gaisford and O’Neill (2006).

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Reactions That Proceed to Completion Calculation of the Initial Calorimetric Signal θ0 In most calorimetric experiments, data over the first few minutes are lost (because the sample is prepared externally to the instrument and there will be a heat of friction introduced by loading), which means it is not possible to measure the value of the power signal at t = 0 (θ0). The value must therefore be inferred in some way from the recorded data set. A convenient strategy is to apply a polynomial series (usually fourth order is sufficient) to the first 10 h of any experimental data set and to extrapolate to a value of θ0. Calculation for the Reaction Order Knowledge of the value of n is vital because it can give some mechanistic insight to a data set that contains no molecular information. By selecting two power values from the calorimetric data, θ1 and θ2, it has been shown that the ratio of the two associated times, t1 and t2, for θ1 and θ2 is dependent only on the order of reaction (Willson 1995). Rearrangement of Equation 10.9 for the two time points gives

t1 =

⎛ Φ1 ⎞ ⎜⎝ ⎟ k ⋅ ΔH 1− n ⎠

1− n n

− Q1− n

k ⋅ ΔH 1− n ⋅ ( n − 1)

(10.10)

and

t2 =

⎛ Φ2 ⎞ ⎜⎝ ⎟ k ⋅ ΔH 1− n ⎠

1− n n

− Q1− n

k ⋅ ΔH 1− n ⋅ ( n − 1)

(10.11)

and therefore, 1− n

t 2 (Φ 2 ) n = t1 (Φ )1−nn 1

(10.12)

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It follows that the order of reaction may be determined from knowledge of the value of t2/t1. This is most easily achieved through the use of a suitable mathematical worksheet. The values of θ1 and θ2 are converted into percentages of the initial calorimetric signal (θ0) and, by using the worksheet, a table of values of t2/t1 calculated from Equation 10.12, can be constructed as a function of the rate constant for a particular pair of (θ1/θ0) and (θ2/θ0) ratios. The experimental t2/t1 constant may then be compared with the table of t2/t1 values, and the order of reaction can be determined.

Calculation for the Total Heat Released for Complete Reaction In the unlikely case that the reaction progresses to completion within the experimental measurement period, Q is simply the area under the power-time curve. More commonly, this is not the case, and it becomes necessary to calculate a value for Q from the experimental data. Recall Equation 10.7: Φ = k ΔH 1− n (Q − q )

n

(10.7)

If two values of θ at different points along the calorimetric curve are taken, and their associated values of q are noted such that Φ1 = − ΔH 1− n k (Q − q1 ) Φ 2 = − H 1− n k (Q − q2 )

n

n

(10.13)

(10.14)

Then, n

Φ1 (Q − q1 ) = Φ 2 (Q − q2 )n

(10.15)

1

⎛ Φ1 ⎞ n = (Q − q1 ) ⎟ ⎜⎝ Φ2 ⎠ (Q − q2 ) Setting

(10.16)

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1n

=R

(10.17)

The value for Q can then be derived from Equation 10.18. Q=

(q1 − Rq2 )

(10.18)

1− R

At this point, it should be noted that the values of q1 and q2 must include the area under the curve for the missing data always encountered at the start of any calorimetric experiment. It is clear that this value cannot be measured directly, but it can be approximated (with reasonable accuracy) from the calculated value of θ0 and the accompanying polynomial equation used to derive it, described earlier.

Calculation for Reaction Half-Life The reaction half-life, t1 2, is defined as the time taken for half the reactable material to be consumed. Assuming n ≠ 1, it is easily calculated from t1 2 =

(2n−1 − 1)

(10.19)

[( n − 1) kA0n−1 ]

Hence, if k (see below), A0, and n are known, then t1 2 can be calculated. Calculation for Rate Constant Because the reaction order, n, is known, a kinetic equation that describes the reaction can easily be written. This equation can then be manipulated to reveal the rate constant. Taking a second-order reaction as an example, ⎛ A0 ⎞ Φ = − k ΔH ⎜ ⎝ 1 + kA0 t ⎟⎠

2

(10.20)

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If the ratio of two data points θ1 and θ2 at times t1 and t2 are taken, Equation 10.21 is obtained: Φ1 (1 + kA0 t2 ) = Φ 2 (1 + kA0 t1 )

(10.21)

This equation can be expanded and rearranged to yield a quadratic expression in terms of k: k 2 ( RA02 t12 − A02 t22 ) + k (2 RA0 t1 − 2 A0 t2 ) + ( R − 1) = 0

(10.22)

This quadratic function can then be solved in the normal way. Calculation for Reaction Enthalpy Earlier it was shown that the total heat output, Q, is given by Equation 10.5. The reaction enthalpy is then easily calculated. Reactions That Proceed to a Point of Equilibrium It is important to know whether the reaction under study reaches completion or equilibrium, because knowledge of the number of moles of material reacted is essential to calculate the correct value for the reaction enthalpy. Note: In the following treatment, Q represents the amount of material that will react and is distinct from QT which is the value of Q if all the sample (i.e., A0) content had reacted. Test for Complete Reaction A test for complete reaction is to study the reaction (for identical loads) over a range of temperatures. Noting that because the equilibrium constant (if one exists), K, will change as a function of temperature, Q must also vary as a function of temperature. If the reaction proceeds to completion, then the value of Q will remain constant across the temperature range and will be equal to QT for all temperatures. Determination of K For the reaction A ⇔ B, the equilibrium constant is given by K=

[ A] [ B]

(10.23)

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At equilibrium [B] is given by Equation 10.24 and [A] is described by Equation 10.25.

[ B] =

Q H

(10.24)

[ A] = AT − [ B ]

(10.25)

QT Q − H H

(10.26)

⎛ QT − Q ⎞ ⎝ ⎠ K= H H Q ⎛ ⎞ ⎝ H⎠

(10.27)

Q (QT − Q )

(10.28)

Hence,

[ A] = Thus,

And therefore, K=

Equation 10.28 can be written for studies at different temperatures, Tm (m = 1, 2, 3, etc.). Note that QT will remain constant for all values of T providing that the reaction mechanism does not change and that there is no dependence of change in heat capacity, ΔCp, over the chosen temperature range. Equation 10.28 then permits K to be calculated for any chosen temperature. However, a value for QT is required in order to effect this calculation. The value of QT is not directly available from the calorimetric data and must be calculated separately. Calculation of QT The calculation of QT is undertaken by consideration of the effect of change in temperature on the equilibrium constant. The van’t Hoff equation states

Shelf Life Prediction of Complex Food Systems ΔH δ ( ln K ) =− 1 R δ⎛ ⎞ ⎝T⎠

257 (10.29)

If it is assumed that ΔH is independent of temperature, then integration of Equation 10.29 gives K1 H 1 1 = − ⎛⎜ − ⎞⎟ K2 R ⎝ T1 T2 ⎠

(10.30)

K1 K will be equal to 2 , for temperatures T2 and T3, if the K2 K3 temperatures T1, T2, and T3 are such that Equation 10.31 is true.

The ratio

T1T2 T2T3 = (T2 − T1 ) (T3 − T2 )

(10.31)

(For instance, if T1 = 298 K and T2 = 303 K, then T3 will be equal to 308.5 K). If this condition is met, then Equation 10.32 can be written. Q2 Q1 ⎞ ⎛ ⎛ ⎞ ⎜ ⎟ ⎜ K1 ⎝ (QT − Q1 ) ⎠ K 2 ⎝ (QT − Q2 ) ⎟⎠ = = = Q3 Q2 K2 ⎛ ⎞ K3 ⎛ ⎞ ⎜⎝ ⎟⎠ ⎜⎝ (QT − Q2 ) (QT − Q3 ) ⎟⎠

(10.32)

This can be solved for QT, (for temperatures m = 1, 2, and 3): ⎛ Q 2Q + Q 2Q − 2Q3Q2Q1 ⎞ QT = ⎜ 2 1 22 3 ⎟⎠ ⎝ Q2 − Q3Q1

(10.33)

The enthalpy is now accessible since QT and AT are known: QT = ΔH AT Once ΔH is known, A can be calculated:

(10.34)

258

Calorimetry in Food Processing Qm =A ΔH

(10.35)

Once these parameters are known, it is a simple matter to calculate the rate constant, k, using the methods described earlier. Since QT and Q are accessible the equilibrium constant, K, can be derived from Equation 28 and hence the values for the Gibbs function and entropy are readily obtained from Equations 10.36 and 10.37 respectively. ΔG = − RT ln K

(ΔH − ΔG ) T

= ΔS

(10.36) (10.37)

The calculated values of K will allow an “internal” check on the validity of the procedure through the derived value of ΔH. A plot of ln K versus 1 will yield a straight line with slope − ΔH . If the procedure T R is invalid, the mechanism changes with T, or there is a significant temperature dependency of heat capacity (over the temperature range used), the van’t Hoff plot will not be linear, and/or the value of ΔH will not match that derived from Equation 10.34. Since Q and QT are known and if ΔH is accurately recovered, then it is possible quantitatively to determine the reactable material content in a heterogeneous sample. Although there are fewer examples in the literature, kinetic interpretation of calorimetric data for foodstuffs has been reported. Tortoe et al. (2007) report a kinetic analysis of the osmotic dehydration of apples, bananas, and potatoes. In osmotic drying, the foodstuff is placed in a concentrated sugar solution; water is then drawn out of the foodstuff as a result of osmosis. A side effect of osmotic drying is the ingress of sugar into the foodstuff, resulting in a dried, sweetened material. For all apple samples, a three-phase signal was observed. The initial, large, signal was assumed to be rapid transfer of water and sugar from the cut surfaces of the food. The second phase represented movement of water from intracellular spaces, and the final phase represented movement of water from extracellular spaces. For banana and potato samples, a two-phase signal was observed. In all cases, the processes

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were first order, which meant that a simple plot of ln (power) versus time allowed recovery of the rate constants from the gradients of the respective phases. The rate constants changed in magnitude in the order k1 > k2 > k3 and increased with temperature. Arrhenius plots of the data suggested a linear relationship for Golden Delicious apples and potatoes and nonlinear behavior for Cox apples and bananas. As noted, O’Neill (2004) studied the kinetics of ascorbic acid in freshly squeezed orange juice. This is more complex than simple studies of the oxidation of ascorbic acid because spoilage of fresh orange juice generally results both from oxidation of ascorbic acid and from degradation of pectin by pectin methyl esterase. Although it is still the subject of debate, it is thought that the degradation of pectin results in protein agglomerates that settle, resulting in clarification of the juice. Clarification is a major cause of spoilage in orange juice, and strenuous efforts are made to eliminate it. This can be done in a number of ways. Classically, the juice is pasteurized to denature the protein as well as destroy the microbial flora. However, heat treatment adversely affects the flavor and aroma of the juice and is generally unsatisfactory if the juice is to be marketed as fresh-squeezed. Alternatively, the juice is frozen during storage and transportation, reducing the activity of the enzyme and other degradative processes. This is costly and usually only viable for concentrated juice. An ideal solution would be some natural additive that does not adversely affect the desired quality parameters for orange juice but does inhibit pectin methyl esterase, in quantities that are not harmful, reducing cloud destabilization. Selection of such a material starts with the ability quantitatively to monitor the reaction processes directly within orange juice samples. Both the oxidation reaction and the enzyme reaction are pH dependent, with the oxidation reaction favoring alkaline conditions and the enzyme reaction favoring more neutral conditions. By buffering the orange juice at pH 7.0, O’Neill (2004) held the enzyme reaction at almost optimal conditions, while the oxidation reaction was more favored compared with the naturally more acidic pH of orange juice. The calorimetric data revealed two distinct first-order phases (Figure 10.5). The first reaction progressed with a rate constant of 3.9 (±1.0) × 10−5 s−1, and the second progressed with a rate constant of 2.7 (±0.3) × 10−5 s−1.

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Figure 10.5. Observed calorimetric output for buffered orange juice. Reprinted from O’Neill (2004), with permission.

Analysis of the pectin/pectin methyl esterase reaction revealed it to progress with a rate constant of 3.9 (±0.3) 10−5 s−1, identifying this as the first process in the orange juice sample. A similar analysis of the oxidation of ascorbic acid in buffer gave a rate constant of 3.0 (±0.3) 10−5 s−1, highlighting this as the second process. A further benefit of the kinetic analysis was the recovery of the enthalpy of oxidation of ascorbic acid, −155 ± 25 kJ/mol. This information allows the quantification of ascorbic acid content in the orange juice sample. From Figure 10.5, it can be seen that the calorimetric output from approximately 100,000 s onwards can be solely attributed to the oxidation of ascorbic acid. This being so, then the calorimetric output at t = 0 (θ0) for the oxidation reaction can be obtained from the y intercept of the ln θ versus t plot. This value is quantitatively proportional to the amount of ascorbic acid of the sample: A=

Φ0 kH

where A is the quantity of ascorbic acid.

(10.38)

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Summary The study of foods and food ingredients is complicated, largely because physical form impacts the selection and use of any analytical assay technique. Because it measures a universal property and is invariant to physical form, calorimetry offers an exciting opportunity for the investigation of foodstuffs. The full capabilities of calorimetry have not yet been exploited in this area, although the number of reported applications is increasing. Here, it has been shown that calorimetry can be used to study a huge variety of samples, from simple ingredients (such as ascorbic acid) to complex biological processes. Data interpretation ranges from qualitative to quantitative, the analysis methods being dependent upon the complexity of the sample. However, a combination of clever experimental design, sample preparation, and data analysis mean that quantitative outcomes are increasingly available from calorimetric data matrices, and the application of the technique to foods and food ingredients can only continue to increase.

References Alklint, C., Wadsö, L., and Sjöholm, I. 2005. Accelerated storage and isothermal microcalorimetry as methods of predicting carrot juice shelf-life. J Sci Food Agri, 85:281–285. Antonelli, M.L., Spadaro, C., and Tornelli, R.F. 2008. A microcalorimetric sensor for food and cosmetic analyses: L-malic acid determination. Talanta, 74: 1450–1454. Antonelli, M.L. D’Ascenzo, G. Laganà, A., and Pusceddu, P. 2002. Food analyses: A new calorimetric method for ascorbic avid (vitamin C) determination. Talanta, 58:961–967. Bakri, A., Janssen, L.H.M., and Wilting, J. 1988. Determination of reaction rate parameters using heat conduction microcalorimetry. J Thermal Anal, 33: 185–190. Beezer, A.E., Newell, R.D., and Tyrrell, H.J.V. 1976. Application of flow microcalorimetry to analytical problems—preparation, storage and assay of frozen inocula of saccharomyces cerevisiae. J Appl Bacteriol, 41:197–207. Conti, S., Gaisford, S., Buckton, G., and Conte, U. 2006. Solution calorimetry to monitor swelling and dissolution of polymers and polymer blends. Thermochim Acta, 450:56–60. Cosgrove, R.F. 1979. Long-term storage of microorganisms used in antimicrobial effectiveness tests. J Assoc Off Anal Chem, 62:1188–1190.

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Cummings, J.H. 1995. Short chain fatty acids. In: Human Colonic Bacteria: Role in Nutrition, Physiology and Pathology, G.R. Gibson and G.T. Macfarlane, editors. CRC Press: Boca Raton, FL. Cummings, J.H. and Macfarlane, G.T. 1991. The control and consequences of bacterial fermentation in the human colon. J Appl Bacteriol, 70:443–459. Delzenne, N. and Roberfroid, M.B. 1994. Physiological effects of non-digestible oligosaccharides. Lebens-Wiss Technol, 27:1–6. De Meuter, P., Rahier, H., and Van Mele, B. 1999. The use of modulated temperature differential scanning calorimetry for the characterisation of food systems. Int J Pharm, 192:77–84. Fooks, L.J., Fuller, R., and Gibson, G.R. 1999. Prebiotics, probiotics, and human gut microbiology. Int Dairy J, 9:53–61. Forte, L., Vinci, G., and Antonelli, M.L. 1996. Isothermal microcalorimetry as a useful tool for fat determination in food. Anal Let, 29:2347–2362. Gaisford, S. and O’Neill, M.A.A. 2006. Pharmaceutical isothermal calorimetry. Informa Healthcare, New York. Gibson, G.R. and Roberfroid, M.B. 1995. Dietary modulation of the human colonic microbiota: Introducing the concept of prebiotics. J Nut, 125:1401–1412. Gómez-Galindo, F., Rocculi, P., Wadsö, L., and Sjöholm, I. 2005. The potential of isothermal calorimetry in monitoring and predicting quality changes during processing and storage of minimally processed fruits and vegetables. Trends Sci Technol, 16:325–331. Gorbach, S.L., Nahas, L., and Lerner, P.I. 1967. Studies of intestinal microflora. I. Effects of diet, age, and periodic sampling on numbers of faecal microorganisms in man. Gasteroenterology, 53:845–855. Marabi, A., Mayor, G., Raemy, A., Bauwens, I., Claude, J., Burbidge, A.S., Wallach, R., and Saguy, I.S. 2007. Solution calorimetry: A novel perspective into the dissolution process of food powders. Food Res Int, 40:1286–1298. O’Neill, M.A.A. 2004. PhD dissertation. University of Greenwich: London. Ouwehand, A.C., Derrien, M., de Vos, W., Tilhonen, K., and Rautonen, N. 2005. Prebiotics and other microbial substrates for gut functionality. Cur Opi Biotechnol, 16:212–217. Perry, B.F., Beezer, A.E., and Miles, R.J. 1979. Flow microcalorimetric studies of yeast growth: Fundamental aspects. J Appl Bacteriol, 47:527–537. Perry, B.F., Beezer, A.E., and Miles, R.J. 1981. Microcalorimetry as a tool for evaluation of complex media: Molasses. Microbios, 32:163–172. Perry, B.F., Beezer, A.E., and Miles, R.J. 1983. Characterization of commercial yeast strains by flow microcalorimetry. J Appl Bacteriol, 54:183–189. Qin, C., Li, H., Xiao, Q., Liu, Y., Zhu, J., and Du, Y. 2006. Water-solubility of chitosan and its antimicrobial activity. Carbohydrate Polymers, 63:367–374. Raemy, A., Lambelet, P., and Rousset, P. 2004. Calorimetric information about food and food constituents. In: The Nature of Biological Systems as Revealed by Thermal Methods, Dénes Lörinczy, editor, pp 69–98. Klewer Academic Publishers: London.

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Reid, L.M., O’Donnell, C.P., and Downey, G. 2006. Recent technological advances for the determination of food authenticity. Trend Food Sci Technol, 17:344–353. Riva, M., Fessas, D., and Schiraldi, A. 2001. Isothermal calorimetry approach to evaluate the shelf life of foods. Thermochim Acta, 370:73–81. Royall, P.G. and Gaisford, S. 2005. Application of solution calorimetry in pharmaceutical and biopharmaceutical research. Curr Pharm Biotechnol, 6:215–222. Schäffer, B., Szakály, S., and Lörinczy, D. 2004. Examination of the growth of probiotic culture combinations by the isoperibolic batch calorimetry. Thermochim Acta, 415:123–126. Schiraldi, A. 2004. Thermal analyses and combined techniques in food physical chemistry. In: The Nature of Biological Systems as Revealed by Thermal Methods, Dénes Lörinczy, editor, pp 69–98. Klewer Academic Publishers: London. Tortoe, C., Orchard, J., Beezer, A.E., and O’Neill, M.A.A. 2007. Potential of calorimetry to study osmotic dehydration of food materials. J Food Eng, 78:933–940. Willson, R.J. 1995. Ph.D. dissertation. University of Kent: Canterbury. Willson, R.J., Beezer, A.E., Mitchell, J.C., and Loh, W. 1995. Determination of thermodynamic and kinetic parameters from isothermal heat conduction microcalorimetry: Applications to long-term reaction studies. J Phys Chem 99:7108–7113.

Chapter 11 Use of Thermal Analysis to Design and Monitor Cereal Processing Alberto Schiraldi, Dimitrios Fessas, and Marco Signorelli

Introduction Starch Proteins Nonstarch Carbohydrates Process Applications Conclusions References

265 268 272 276 278 285 285

Introduction The term thermal analysis (TA) means the record of any physical property during a given thermal treatment under strict temperature control. The main physical property that is monitored in this way is enthalpy (and the related property, heat capacity); the relevant thermal analysis is named calorimetry and is performed in a few well-defined conditions, each with a specific name: isothermal calorimetry (IC), differential scanning calorimetry (DSC), temperature-modulated DSC (TMDSC), and modulated adiabatic scanning calorimetry (MASC). Another physical property that is traditionally included in the TA realm is mass; the relevant analysis is named thermogravimetry (TGA) and is currently used to monitor the mass loss upon heating the sample. Its trace is often transformed into that of its time derivative (DTG). The mechanical properties of a given sample, like Young’s modulus, E, elastic and storage moduli, G′ and G″, respectively, are those 265

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recorded with the dynamic mechanical analysis (DMA, or DTMA) and thermomechanical analysis (TMA). Applications related to the dielectric character of the sample go under the name of dielectric analysis (DEA). Every type of TA has been used in the study of food stuffs and food processes. Calorimetry was used to determine heat capacities (Schwartzberg 1976; Noel and Ring 1992), describe the behavior of frozen ice-forming food systems (Goff, Montoya, and Sahagian 2002), and study many phase transitions (Roos 1995), including those that imply a heat capacity drop with no transition enthalpy, like the socalled glass transition (Slade and Levine 1991). Since most of these changes imply a substantial modification of the mechanical and rheological properties of the sample, DMA and DEA also were used to describe these phenomena (Laaksonen and Roos 2000; Vodovotoz, Hallberg, and Chinachoti 1996). A rather recent improvement of thermogravimetry is the Knudsen TGA that allows determination of the water activity along the whole dehydration pattern of a given sample in isothermal conditions (Schiraldi and Fessas 2003). Because of the intrinsic heterogeneity of many food materials, a suitable sampling is crucial for the reliability and the reproducibility of the recorded TA traces, which usually is much poorer than for purified chemicals. Too-small samples may not represent the food investigated; smashed or powdered products may miss some important feature of the starting material, for example, the surface area/volume ratio that affects the rate of diffusion-limited processes and the effects related to the overall texture of sample that can be missed when the material is ground or finely sliced (Riva, Schiraldi, and Piazza 1994). The simultaneous occurrence of different changes within a given sample is another reason for the humping and bumping trend of the TA traces from food samples. In DSC, baseline shifts due to glass transitions are often partially overlapped to peak signals of first-order transitions and chemical, biochemical, and microbe-sustained reactions. This explains why DSC investigations were often devised to allow just qualitative tests or comparison between samples and why tentative evaluations of the enthalpy changes, as in the case of starch gelatinization and retrogradation, are often a major cause of discrepancy between different authors who made different choices for the baseline trends. As a consequence, DSC has been considered a substantially qualitative analytical tool by many food scientists and technologists.

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Fortunately, this is not true, provided that the operators are well trained and adequately educated in physical chemistry. Suitably performed TA indeed provides a real insight into the processes that occur within a food system during a thermal treatment, as the relevant change of the involved physical property is directly related to the laws of thermodynamics and kinetics. For example, any thermal effect, ΔH, when coupled with a temperature value, T, is a measure of the stability of the system, thanks to the relationship, ⎡ ∂ ⎛ G ⎞ ⎤ = − ΔH T2 ⎣⎢ ∂T ⎝ T ⎠ ⎦⎥ p

(11.1)

where G is the Gibbs function. Moreover, the rate of heat release or adsorption, Q = dQ/dt (where Q and t stand for heat and time, respectively), is directly related to the rate of the underlying process, dα Q = ΔH ⎛ ⎞ , ⎝ dt ⎠

(11.2)

similar expressions holding for the change rate of practically every physical property recorded during a temperature scan. These laws are of great help in recognizing the “true” trend of a baseline (Roduit 2000) and the “true” transition points, because they provide means to make reliable predictions. The problem that cannot be easily overcome concerns the number of partially overlapped phenomena that take place during a given heating or cooling run, each relevant to a single component of the system, as in the case of cerealbased food products. A deconvolution of the relevant trace therefore becomes a necessary step of the data treatment (Schiraldi 2003). This chapter reports a short critical review of the applications of TA to the study of cereal-based food and related processing. The main concern of these products deals with the transitions of starch in the presence of other components of the system, such as proteins and nonstarch carbohydrates, which compete for the available moisture and/or affect its partition between the different regions or phases of a given food system, and lipids, which are responsible for specific interactions that produce peculiar DSC signals. Since water is ubiquitous in most food systems, it is expedient to look at the changes that take place within a given sample by focusing on water and directly detecting changes in the state of water that are concomitant with

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changes relevant to the other components of the system. Particular emphasis therefore is given in this chapter to approaches based on the choice of water as a “probe compound.”

Starch Starch is a supramolecular substance that nature assembles within the seeds of cereals and pseudocereals, legumes, and tubers. Starch may not, therefore, be referred to as a chemical compound: it has neither a definite molecular mass nor a fusion point, and it does not react with other substances until its granular structure is rotten and its glucose polymers, amylose and amylopectin, are exposed to the surrounding environment. Starch chemistry starts with surface processes that take place at the pores and defects of the granule structure, which remains practically unaffected at temperatures below 45 °C, even with excess water. An aqueous suspension of starch granules is easy to prepare and investigate with TA techniques. A superficial wetting takes place when starch granules are dispersed in excess water. The process is exothermic and can be detected with isothermal calorimetry (IC) and suitable mixing cells (Riva, Piazza, and Schiraldi 1991). DSC (and related variations) equipment (Liu and Shi 2006) is instead enough to monitor changes that take place when the starch suspension is heated (Figure 11.1). Across a 30 °C range, starting from an onset temperature that depends on the vegetal origin of the starch investigated (e.g., 45 °, 50 °, and 65 °C, for potato, wheat and rice, respectively), water enters the granule and disaggregates the internal crystal regions that are mainly formed by the side branches of amylopectin molecules. The whole starch granule is transformed in a swollen jelly ghost of the original hard and birefringent body. A gentle stirring turns the starting suspension into a dispersion of amylopectin gel and amorphous insoluble amylose. The two glucose polymers are mutually incompatible (Kalichevsky and Ring 1987), which means that they may not stay in the same phase, being competitors for the available moisture. Because of this, the system is rather heterogeneous and unstable. On further heating, the amylopectin gel turns into a sol, while around 90 °C, nucleation of amylose crystals can take place. The whole process, currently dubbed “starch gelatinization,” is therefore a multistep, fully

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Figure 11.1. DSC trace of a suspension of starch (Merck product) granules in excess (72.5%, w/w) water, recorded on heating (upper curve) and cooling (lower curve) at 5 °C min−1 scanning rate (authors’ unpublished data).

irreversible (Figure 11.1) transformation of the starting suspension of starch granules. The corresponding DSC trace presents a wide endothermic signal, with maximum at a temperature that depends on the moisture content of the system: the lower the moisture, the higher the temperature of the maximum (Figure 11.2). Moreover, mainly because of the different sizes of the starch granules, samples with low moisture content (in any case, never below 70%) show a high T shoulder, which has been attributed to the delayed degradation of larger granules (Biliaderis, Page, Maurice, and Juliano 1986), although the reliability of this interpretation has yet to be convincingly demonstrated. A further endothermic signal (in the 90 °– 115 °C range) can often be observed (Figures 11.1 and 11.2) in the DSC of aqueous suspensions of starches extracted from some flours that contain lipids: the signal corresponds to the fusion of amyloselipid complexes formed in the course of the starch gelatinization (Eliasson 1994; Bulpin, Welsh, and Morris 1982; Le Bail 1999). Because amylopectin molecules can carry phosphate groups (the number of which once again depends on the vegetal origin of the starch), the properties of the gel obtained are affected by the ionic strength and the pH of the surrounding medium. In particular, when

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Figure 11.2. DSC traces of suspensions of starch (Merck product) granules at various water contents (72.5%, 61.1%, and 42.5%, w/w) recorded on heating at 5 °C min−1 scanning rate (authors’ unpublished data).

the system is cooled to room temperature (and below), the amylopectin gel becomes the matrix of growing crystals that entrap water and cause the system to harden. Although the structure of the original granules is by no means restored, the term starch retrogradation is currently used to indicate this process. It can be easily monitored with DSC investigations (Riva, Fessas, and Schiraldi 2000). It must be understood that the crystal phases formed are sufficiently extended to allow X-ray detection (Zobel 1988; Yuryev et al. 2004), but they should be envisioned as islets of ordered arrays of chains with disordered moieties dangling out of their boundaries, rather than as well-shaped microcrystals. Crystal islets of different extension are present throughout a given amylopectin gel, and various types of crystal structures may be formed, named according to the respective X-ray diffraction patterns A, B, C, etc. (Zobel 1988; Yuryev et al. 2004). For these reasons, the progress of the crystal growth can imply the coexistence of different polymorphs that have different thermal stability, and when the “retrograded” system is warmed, the “fusion” process therefore encompasses a temperature range between 30 ° and 90 °C (Figure 11.3) (Riva, Fessas, and Schiraldi 2000). If the system undergoes a suitable annealing treatment, formation of amylose crystals becomes possible (Wasserman et al. 2007). Their fusion takes place in the 125 °–140 °C range and can be easily detected with a DSC investigation (Wasserman et al. 2007). Figure 11.4 shows

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Figure 11.3. Endothermic effect related to the fusion of amylopectin crystals formed within the starch gel phases of a bread dough (data are given as excess heat capacity, namely heat flux, divided by the scanning rate × sample mass product). Dashed curve describes the trend of the of the relevant enthalpy versus the storage time. The records were obtained at 2 °C min−1 heating rate. Modified from Riva et al. (Riva, Fessas, and Schiraldi 2000).

two DSC traces obtained from the same aqueous starch suspension: the dashed profile refers to the first heating (2 °C min−1) run, during which starch gelatinization and fusion of amylose-lipid complexes occurred, whereas the full line profile refers to the heating run performed after a suitable annealing treatment (Wasserman et al. 2007) that favors the formation of amylose crystals. The latter shows the endothermic effect related to the fusion of amylopectin crystals, while a second endothermic peak with onset at a much higher temperature is related to the fusion of amylose crystals. The growth of these crystal phases prevents the formation of amylose-lipid complexes (no signal appears in the expected temperature range). These amylose crystals are resistant to the amylase assisted hydrolysis: the hosting system has therefore received the misleading name of “resistant starch,” which meets industrial and commercial needs rather than the chemical truth. A starch gel heated in an open crucible releases its moisture with a diffusion-limited mechanism (Fessas and Schiraldi 2005). This means that solvation water can be easily exchanged with water engaged in the structure.

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Figure 11.4. DSC traces obtained from an aqueous starch suspension (heating rate 2 °C min−1; data are transformed in apparent heat capacity, dividing the heat flux by sample mass × heating rate). The dashed line refers to the first heating. The heavy line corresponds to the record of the reheating run performed after a suitable annealing treatment (Wasserman et al. 2007). Notice that the fusion of the amylopectin crystals (formed during the annealing) occurs at lower temperature, while no signal relevant to amylase-lipid complexes appears and that a new endotherm occurs at much higher T corresponding to the fusion of amylose crystals (formed during the annealing).

In short, all these starch transformations take place in a cereal flour dough in the presence of many other substances that compete for the available moisture or may directly interact with starch carbohydrates. The relevant DSC traces should therefore be interpreted by taking into account the possible interactions between different dough components. Blends of flours from cereals and pseudocereals allow dough preparations in which many interactions are expected (Fessas et al. 2008). These “complications” can be addressed by considering the potential role of the other nonstarch main components of a dough, namely, proteins, nonstarch carbohydrates, and lipids.

Proteins In most cereals, both globular and networking proteins are present. The former, dubbed albumins and globulins after Osborne (Osborne 1924), can be either enzymes or carriers, are soluble in aqueous media, and

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are therefore easily extractable. The latter tend to form wide threedimensional meshes that entrap aqueous phases and separated bodies, like starch granules. Gluten is the most important representative of this family: it is not soluble in water and therefore can be separated by washing a dough loaf with hot water to wash away starch carbohydrates and globular proteins. Minor amounts of nonsoluble compounds remain entrapped in the gluten meshes and represent the unavoidable “contaminants” of any gluten preparation. Details about the chemistry of the cereal proteins are beyond the scopes of this chapter; suffice it to say that when a dough is prepared from a cereal flour, globular proteins play mainly a surfactant role that is crucial in stabilizing the air bubbles formed in a leavening loaf, whereas gluten is responsible for the overall rheological behavior of the system. Both globular proteins and gluten fix water molecules, although in rather different ways. The former are normally solvated at the surface polar groups and modify their own solvation shell when unfolding and denaturation take place. Gluten instead uses water molecules as bridges between the next neighboring chains (Belton 1999) and develops an extended network, due to a large number of hydrogen bonds (Figure 11.5). Because of this, gluten can entrap large amounts of interstitial water within its meshes. Some disulphide bonds provide more robust interand intrachain links and affect the overall extensibility of the network. Any elongation strain squeezes the interstitial water out of the meshes so that the next neighboring chains become closer to one another; when the strain is allowed to relax, water can reoccupy the interstitial regions and make the meshes swell back to the starting size. This view has been recently perfected (Belton 2005; Kontogiorgos and Goff 2006), and it is still adequate to suggest general guidelines for our understanding of the competition of various flour components for the available water that undergoes displacement between the coexisting aqueous phases of a cereal flour dough from its early mixing, to proofing and baking, or freezing and storing. This a fundamental issue to be considered. Because of the thermodynamic incompatibility (Tolstoguzov 2003) between proteins and carbohydrates, as well as between different carbohydrates (Liu and Shi 2006), a flour dough is indeed a dispersed system in which several aqueous phases coexist that can exchange the solvent between one another.

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Figure 11.5. Naive picture of the water bridges and direct hydrogen bonds between hydro-compatible biopolymer chains (which can be referred to as carbohydrates or gluten proteins).

This partition of water can be detected with thermogravimetry investigations (Fessas and Schiraldi 2005): the related DTG trace shows a broad signal that can be deconvoluted in two or more components, each relevant to a given water fraction (Figure 11.6). The water fraction that sustains the evaporation at mild temperatures mainly belongs to the imbibing moisture (in the earliest steps) and by the separated (because of the thermodynamic incompatibility of their solutes, such as carbohydrates and globular proteins) aqueous phases: the solvent that evaporates from one aqueous phase is quickly replaced by the water migrating from any neighboring aqueous phase. As a result, the dehydration of the samples looks like a single process governed by the core-to-surface diffusion of moisture (Fessas and Schiraldi 2005). The rest of the water (about 15% of the starting overall dough moisture) tends to remain close to the network forming polymers (mainly gluten) and can be stripped away only at temperatures above 100 °C. Starch gelatinization is mainly sustained by the former water fraction when the dough is being baked. Because the starch gelatinization

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Figure 11.6. Deconvolution of the DTG trace obtained from a wheat dough sample. The high-temperature peak can be related to the water fraction tightly trapped within the gluten meshes, while the lowest broad peak is related to the imbibing water that is released through a Fickian diffusion mechanism. The other minor peaks should be referred to as contributions from moderately bound water fractions.

rate is strongly dependent on the starch/water mass ratio, the loss of water reduces the degree of gelatinization attained (Fessas and Schiraldi 2000) (Figure 11.7). The effect is obviously different in the various regions of a dough loaf, being more severe at the surface where dehydration is faster and the crust is being formed. Two main situations can be envisaged: namely, before and after the onset of the starch gelatinization. Wetted starch granules fix a relatively small amount of water, while most of the solvent is engaged by salts, globular proteins, water-soluble nonstarch carbohydrates, and gluten. The hydration shell water is the poorly available fraction of the overall dough moisture, whereas the rest of the solvent, including water molecules trapped in the gluten meshes and in the nonstarch carbohydrate-entangled coils, can be driven toward the starch granules once the gelatinization onset is trespassed. This onset in turn depends on the available moisture: addition of small molecular mass solutes that can fix water produces a delay of the starch gelatinization, while addition of hydratable polymers, such as water-soluble arabinoxylans, has no significant effect. The reason for this difference is due to the different effect on the relative humidity (RH) of the system: for a given dry matter content, an aqueous solution of simple sugars or salts shows a

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Figure 11.7. Deconvolution of the DTG trace of a manually mixed dough with 42% moisture content (30 mg sample, 2 °C/min heating rate) and starch gelatinization of a dough undergoing heating at the same heating rate in a open DSC pan. Modified from Fessas and Sachiraldi (Fessas and Schiraldi 2000).

much lower RH than an aqueous solution of arabinoxylans (Fessas and Schiraldi 1998). Combinations of TA with other techniques, such as NMR relaxometry (Vittadini et al. 2003; Lopes-Da-Silva et al. 2007), MRI (Hills 1998), and NIR (Huang et al. 2003), provide the experimental evidence of these changes, even in a very complex system such as staling bread (Morgan, Fourneaux, and Stanley 1992; Schiraldi, Piazza, and Riva 1996). They can, however, be better understood once the principle of thermodynamic incompatibility is put at work.

Nonstarch Carbohydrates Cereal and pseudo-cereal flours contain some nonstarch carbohydrates coming from various regions of the seed. A fraction of them is not water extractable and therefore is segregated from the aqueous phases of the dough: the role of this fraction has little relevance to the physical behavior of the other dough components (safe for some sensorial properties of the final products).

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Figure 11.8. DTG traces of dough prepared with buckwheat (a), wheat (b), and 50% (w/w) buckwheat/wheat flour (c) (authors’ unpublished data).

The water-extractable fraction conversely plays a crucial role on the overall viscosity of the dough and to the partition and displacement of water (Fessas and Schiraldi 2001a; Courtin and Delcour 1998; Fessas and Schiraldi 2001b). Although only one water molecule per single sugar monomer can be engaged to solvate these polymers, much larger amounts of moisture are trapped within their entanglements and can be easily displaced under the effect of chemical potential gradients. Because a flour dough is a quite viscous environment, only shortrange displacements are indeed allowed. This explains why when the dough is being dehydrated, as in the course of a TGA run, the water displacements through the samples are rapidly hindered by the concurrent increase of the viscosity. The flour of some gluten-free cereals and pseudo-cereals, such as buckwheat, soy, and amaranth, can trap water because of different proteins but cannot form a stable dough because the polymer chains do not arrange themselves in a web. This water fraction is therefore much more mobile than the moisture trapped within gluten meshes. The relevant DTG trace therefore shows a single “diffusional” (see above) peak (Figure 11.8). When one of these flours is mixed with wheat flour to form a blend, then the dough can be formed upon kneading. The relevant DTG trace (Figure 11.8) shows two peaks: the high T signal is once again attributed to the moisture fixed by the gluten

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meshes, although the temperature gap between the two maxima, which is related to the looseness of the gluten network, is smaller than in the trace of a wheat flour dough. This effect is mainly related to nonstarch and nongluten proteins, which form separate phases (droplets) that do not allow gluten to attain an extended and tight reticulation (Fessas and Schiraldi 1998).

Process Applications The information collectable with TA investigations can be directly used to improve food formulations and process conditions. Of great interest are the use of the so-called state diagrams that highlight the role of the glass transition temperature as a border between high- and low-molecular-mobility regions, which have been collected in Roos’s book (Roos 1995). Few other examples are helpful to the reader. Any given thermal treatment, such as cooking, baking, frying, etc., corresponds to a thermal history experienced by the system. A direct reproduction of such history can be difficult to plan when using TA equipment. Nonetheless, one can inscribe any thermal history in a TTT (time temperature transformation) diagram that can be defined on the experimental basis of TA evidence of a given transformation responsible for a specific signal in the TA record. Starch gelatinization monitored with DSC is a good example. The relevant signal is an endothermic shouldered peak, the area of which, once divided by heatingrate × starch-mass product, corresponds to the specific enthalpy change, ΔH (joules per gram of starch units), accompanying the gelatinization. The peak area swept at any given T within the (Tonset, Tend) range is related to the progress degree, α, achieved at that T: α (T ) =

partial peak area . total peak area

(11.3)

To account for the effect of the moisture content, the reference to be chosen is the thermal effect recorded in excess water, Δ∞, which is larger than those observed at low water contents since only a fraction of the overall starch undergoes gelatinization (or experience a complete transformation) when moisture is less than 40% (w/w). The total peak area must accordingly be scaled with respect to the area of the

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peak recorded in excess moisture. To clarify with an example, if the recorded ΔH is 0.75 ΔH∞, then α (T ) =

partial peak area × 0.75 total peak area

(11.4)

Since starch gelatinization is an irreversible transformation, the relevant DSC signal reflects the process kinetics and is affected by the heating rate, β = dT/dt. The higher the heating rate, the higher the end temperature of the signal (there is also a shift of the apparent onset temperature that is of minor concern for the present discussion, however). One therefore has to define the temperature range where starch gelatinization takes place for each β and the progress degree within that range: dα dα dT dα Q = ΔH ⎛ ⎞ = ΔH ⎛ ⎞ ⎛ ⎞ = β ΔH ⎛ ⎞ ⎝ dt ⎠ ⎝ dT ⎠ ⎝ dt ⎠ ⎝ dT ⎠

(11.5)

where Q is the recorded heat flux per unit mass of starch and ΔH is the related enthalpy change. Once the DSC runs at the selected heating rates are performed, the increase of α on sweeping the related peaks can be determined: more precisely, one has to detect the temperatures at which α attains some given levels (say α = 0.1, 0.2, 0.3, etc.) for each considered β (Figure 11.9). The TTT diagram is a T-versus-t plot, where the straight lines corresponding to the various heating rates considered in the experimental design of the DSC investigations have to be drawn first. The selected iso-α temperatures (see above) have to be marked along the corresponding straight line in the TTT diagram. Eventually, a map of iso-α points is obtained that can be used to draw iso-α curves (Figure 11.10). Remember that the maximum attainable α does not depend on β, being mainly related to the available moisture. This is a third fundamental parameter to be accounted for. The experimental design must therefore include DSC runs performed with samples of a different moisture content, and a third axis can be added to the TTT diagram to account for it. In the three-dimensional TTT diagram, the iso-α loci are surfaces. For a more practical approach, samples can be partially dehydrated by

Figure 11.9. Extent of starch gelatinization, α, on increasing temperature, according to the DSC data recorded at various heating rates, β. Iso-α temperatures have to be reported in the TTT diagram.

2

5

1

0.5

140 120

0.5

T / ˚C

100

0.8 0.6

80

0.4

60

0.1

0.2

40 20 0 0

50

100

150

200

250

t / min

Figure 11.10. Iso-moisture (excess water) TTT plane relevant to starch gelatinization in bread dough samples. The dotted straight lines correspond to different heating temperatures (0.5 °, 1 °, 2 °, 5 °C/min), while the data points are the drawn from the α-versus-T plots to evidence the attainment of a given α level (values reported at the right-hand side). The tie lines connecting these points are the iso-α curves. Modified from Fessas and Schiraldi (Fessas and Schiraldi 2000).

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Figure 11.11. Bread dough samples: starch gelatinization extent achieved in DSC open pans. Modified from Fessas and Schiraldi (Fessas and Schiraldi 2000).

heating them in open DSC pans within the furnace of the instrument at a given heating rate. The run has to be stopped at a temperature, Ti, that can represent a rough average of the real thermal history experienced by the product. The DSC sample pan is then quickly cooled to room temperature, sealed, and again heated at the same rate to evaluate the residual starch gelatinization. The relevant moisture content of the sample at Ti (and in the second DSC run with sealed pans) can be eventually determined as the mass loss after an overnight rest at 105 °C after piercing the cover of the sample pan with a needle (Figure 11.11). This experiment allows determination of the fraction of starch gelatinization that occurred during the first (open pans at variable moisture content) and the second phase (sealed pans at constant moisture content) (Fessas and Schiraldi 2000). An ideal experiment should in principle be performed with a TGA-DSC combined instrument. Unfortunately, this cannot be a realistic choice, since the vaporization enthalpy of water (2.3 kJg−1) is 2 orders of magnitude larger than the enthalpy of starch gelatinization. A superposition of separate investigations can nonetheless be performed (see Figure 11.7). The corresponding TTT diagram (Figure 11.12) is indeed a curved section of the three-dimensional plot. On the same diagram, any thermal history actually experienced by the system, as well as the

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T / °C

100

0.7 0.6 0.5 0.4 80 0.3 0.2 0.1 60 a

40

20

thermal histroy

0

5

10

15 t / min

20

25

30

Figure 11.12. TTT diagram corresponding to the data reported in Figure 11.11. The figure is a projection of a surface of the three-dimensional TTT (moisture-vs.temperature-vs.-time) diagram.

moisture loss that occurred in the real process, can be represented with a process path. The corresponding curve crosses the iso-α surfaces (Figure 11.13). The highest attained α may not regress and can indeed be referred to as the maximum progress of starch gelatinization achievable at the end of the thermal history considered. Obviously, one must take into account that real systems have a much larger mass than a DSC sample and therefore experience temperature and moisture gradients on cooking or baking. For a more detailed simulation of the process, one therefore must first record the thermal history and the moisture changes in each region of the real system and then draw the relevant information about the extent of the starch gelatinization achieved. Another practical use of the information drawn from TA investigations concerns the effects of mechanical treatment, such as kneading, and layering, that affect the tightness of the gluten network of wheatbased products. Mechanical stresses squeeze some water out of the gluten phase, thus allowing protein moieties to come closer to one another and, possibly, to form direct links (mainly hydrogen bonds [Belton 1999; Belton 2005]). If the product is baked just after the experience of such mechanical stresses, more water evaporates on baking, and the final product is harder and brittle. This occurs in bis-

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Figure 11.13. Sketched representation of the real thermal history experienced by the product (sigmoid curve) that crosses the iso-α surfaces in the TTT diagram. The figure is a projection of a surface of the three-dimensional TTT (moisture-vs.-temperaturevs.-time) diagram. Modified from Fessas and Schiraldi (Fessas and Schiraldi 2000).

cuits (Piazza and Schiraldi 1997). The experimental evidence related to this effect is provided by the DTG trace of the dough. The high T “gluten peak” of a stressed (overmixed) dough is smaller and closer to the large low T peak (Fessas and Schiraldi 2005). This indeed means that some water migrates from the gluten phase toward the neighboring starch-rich regions (these are short-range displacements), where it can evaporate more easily. A couple of hours rest is, however, enough to restore the previous water partition (Fessas and Schiraldi 2005). The “relaxed” dough leads to a softer baked product (Piazza and Schiraldi 1997). The DTG trace can be of help also in predicting the effect of extra ingredients on the final properties of a given product. The effect of the nonstarch carbohydrates acting as hydrocolloid sinks of moisture appears in the DTG traces with a downward shift of the high T peak. The main difference from the effects related to mechanical stresses is that this peak corresponds to more than 20% of the overall dough moisture. This means that the water fractions bound to hydrocolloids and gluten, respectively, have similar fugacities, both being less free

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Figure 11.14. Pictures of the alveolar crumb structure of bread from wheat (A) and a wheat-buckwheat blend added with soluble polysaccharides (B).

than the other water fractions of the dough. Some visual evidence of this picture is provided by light microscopy investigations (Autio and Salmenkallio-Marttila 2001; Autio and Laurikainen 1997) that show islets of nonstarch hydrocolloids dispersed within the gluten meshes, apparently hindering the contacts between them and preventing the formation of a tight network. If this situation is not perturbed by mechanical stresses, the water loss during baking is smaller than for a dough with no extra hydrocolloids. The result is a product with broader crumb alveoli (Fessas and Schiraldi 1998). The nonstarch hydrocolloid “water sinks” keep the final product softer for a longer period, thus acting as antistaling ingredients (Fessas and Schiraldi 2001a). One may define an “optimal” shift of the DTG peak that corresponds to the desired tightness of the final network by adjusting the amounts of extra hydrocolloids added to the recipe. The case of bread prepared from a blend of wheat and buckwheat flours can be a suitable example (Fessas et al. 2008), where the nonstarch carbohydrates were those water-extracted from the buckwheat hull in a separate process and added as an aqueous solution to the dough. The bread prepared from a blend of wheat and dehulled buckwheat had a crumb with alveolar distribution quite similar to that of the crumb of the bread obtained from wheat flour (Figure 11.14), although with a 45% greater density. In this modified dough, the effects of soluble nonstarch polysaccharides has been tuned through the counterbalancing action of

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globular proteins that play the role of surfactants that stabilize the dough matrix/air interface (Fessas and Schiraldi 1998).

Conclusions Thermal analysis is very suitable for monitoring the transformations that take place in cereal-based food products. It requires, however, specific training for operators because the records obtained from a food sample usually have to be mathematically treated to unveil the single contributions coming from largely overlapped events relevant to different components or phases of the system. Combination with other experimental techniques is nonetheless of great help, especially when water partition and displacements are involved in the changes induced by the process. Specific applications to simulate thermal treatments and adjust the recipe of a given product can be drawn from either DSC or TGA data.

References Autio, K. and Laurikainen, T. 1997. Relationships between flour/dough microstructure and dough handling and baking properties. Trends Food Sci Technol, 8:181–185. Autio, K. and Salmenkallio-Marttila, M. 2001. Light microscopic investigations of cereal grains, doughs, and breads. Lebensm-Wiss U Technol, 34:18–22. Belton, P.S. 1999. On the elasticity of wheat gluten. J Cereal Sci, 29:103–107. Belton, P.S. 2005. New approaches to study the molecular basis of the mechanical properties of gluten. J Cereal Sci, 41:203–211. Biliaderis, C.G., Page, C.M., Maurice, T.J., and Juliano, B.O. 1986. Thermal characterization of rice starches: A polymeric approach to phase transitions of granular starch. J Agric Food Chem, 34:6–14. Bulpin, P.V., Welsh, E.J., and Morris, E.R. 1982. Physical characterization of amylose-fatty acid complexes in starch granules and in solution. Starch/Staerke, 34:335–339. Courtin, C.M. and Delcour, J.A. 1998. Wheat-Derived Arabinoxylans. J Agric Food Chem, 46:4066–4073. Eliasson, A.C. 1994. Interactions between starch and lipids studied by DSC. Thermochim Acta, 246:343–356. Fessas, D. and Schiraldi, A. 1998. Texture and staling of wheat bread crumb: Effects of water extractable proteins and “pentosans.” Thermochim Acta, 323:17–26.

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Fessas, D. and Schiraldi, A. 2000. Starch gelatinization kinetics in bread dough: DSC investigations on “simulated” baking processes. J Therm Anal Calorim, 61:411–423. Fessas, D. and Schiraldi, A. 2001a. Water properties in wheat flour dough I: Classical thermogravimetry approach. Food Chem, 72:237–244. Fessas, D. and Schiraldi, A. 2001b. Phase diagrams of arabinoxylan-water binaries. Thermochim Acta, 6518:1–7. Fessas, D. and Schiraldi, A. 2005. Water properties in wheat flour dough II: Classical and Knudsen thermogravimetry approach. Food Chem, 90:61–68. Fessas, D., Signorelli, M., Pagani, A., Mariotti, M., Iametti, S., and Schiraldi, A. 2008. Guidelines for buckwheat enriched bread: Thermal analysis approach. J Therm Anal Calorim, 91:9–16. Goff, H.D., Montoya, K., and Sahagian, M.E. 2002. The effect of microstructure on the complex glass transition occurring in frozen sucrose model systems and foods. In: Amorphous Food and Pharmaceutical Systems. H. Levine, editor, pp. 145–157. The Royal Society of Chemistry: University of Durham, UK. Hills, B. 1998. Magnetic Resonance Imaging in Food Science. J.Wiley & Sons: New York. Huang, Y., Tang, J., Swanson, B.G., Cavinato, A.G., Lin, M., and Rasco, B.A. 2003. Near infrared spectroscopy: A new tool for studying physical and chemical properties of polysaccharide gels. Carbohyd Polymers, 53:281–288. Kalichevsky, M.T. and Ring, S.G. 1987. Carbohydr Res, 162:323–328. Kontogiorgos, V. and Goff, H.D. 2006. Calorimetric and microstructural investigation of frozen hydrated gluten. Food Biophys, 1:202–215. Laaksonen, T.J. and Roos, Y.H. 2000. Thermal, dynamic-mechanical, and dielectric analysis of phase and state transitions of frozen wheat doughs. J Cereal Sci, 32:281–292. Le Bail, P., et al. 1999. Monitoring the crystallization of amylose-lipid complexes during maize starch melting by synchrotron X-ray diffraction. Biopolymers, 50:99–110. Liu, Y. and Shi, Y.C. 2006. Phase and state transitions in granular starches studied by dynamic differential scanning calorimetry. Starch/Staerke, 58:433–442. Lopes-Da-Silva, J.A., Santos, D.M.J., Freitascarla-Brites, A., and Gil, A.M. 2007. Rheological and nuclear magnetic resonance (NMR) study of the hydration and heating of undeveloped wheat doughs. J Agric Food Chem, 55:5636–5644. Morgan, K.R., Fourneaux, R.H., and Stanley, R.A., 1992. Observation by solid-state 3C CP MAS NMR spectroscopy of the transformations of wheat starch associated with the making and staling of bread. Carbohydr Res, 235:15–22. Noel, T.R. and Ring, S.G. 1992. A study of the heat capacity of starch/water mixtures. Carbohydr Res, 227:203–213. Osborne, T.B. 1924. The Vegetable Proteins. Longmans Greens: London. Piazza, L. and Schiraldi, A. 1997. Correlation between fracture of semi-sweet hard biscuits and dough viscoelastic properties. J Text Stud, 28:523–541. Riva, M., Fessas, D., and Schiraldi, A. 2000. Starch retrogradation in cooked pasta and rice. Cereal Chem, 77:433–438.

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Riva, M., Piazza, L., and Schiraldi, A., 1991. Starch gelatinization in pasta cooking: Differential flux calorimetry investigations. Cereal Chem, 68:622–627. Riva, M., Schiraldi, A., and Piazza, L. 1994. Characterization of rice cooking: Isothermal and differential scanning calorimetry investigations. Thermochim Acta, 246:317–328. Roduit, B. 2000. Computation aspects of kinetic analysis. Thermochim Acta, 355:171–180. Roos, Y.H. 1995. Phase Transitions in Foods. Academic Press: New York. Schiraldi, A. 2003. Phenomenological kinetics: An alternative approach. J Therm Anal Calorim, 72:885–900. Schiraldi, A. and Fessas, D. 2003. Classical and Knudsen thermogravimetry to check states and displacements of water in food systems. J Therm Anal Calorim, 71:221–231. Schiraldi, A., Piazza, L., and Riva, M., 1996. Bread staling: A calorimetric approach. Cereal Chem, 73:32–39. Schwartzberg, H.G. 1976. Effective heat capacities for the freezing and thawing of food. J Food Sci, 41:152–156. Slade, L. and Levine, H. 1991. Beyond water activity: Recent advances based on an alternative approach to the assessment of food quality and safety. CRC Crit Rev Food Sci Nutr, 30:115–359. Tolstoguzov, V.B. 2003. Some thermodynamic considerations in food formulation. Food Hydrocolloids, 17:1–23. Vittadini, E., Dickinson, L.C., Lavoie, J.P., Pham, X., and Chinachoti, P. 2003. Water mobility in multicomponent model media as studied by 2H and 17O NMR. J Agric Food Chem, 51:1647–1652. Vodovotz, Y., Hallberg, L., and Chinachoti, P. 1996. Effect of aging and drying on thermomechanical properties of white bread as characterized by Dymanic Mechanical Analysis (DMA) and Differential Scanning Calorimetry (DSC). Cereal Chem, 73:264–270. Wasserman, L.A., Signorelli, M., Schiraldi, A., Yuryev, V., Boggini, G., Bertiniand, S., and Fessas, D. 2007. Preparation of wheat resistant starch: Treatment of gels and DSC characterization. J Therm Anal Calorim, 87:153–157. Yuryev, V.P., Krivandin, A.V., Kiseleva, V.I., Wasserman, L.A., Genkina, N.K., Fornal, J., Blaszczakb, W., and Schiraldi, A. 2004. Structural parameters of amylopectin clusters and semi-crystalline growth rings in wheat starches with different amylose content. Carbohydrate Res, 339:2683–2691. Zobel, H.F. 1988. Starch crystal transformations and their industrial importance. Starch/Staerke, 40:44–50.

Chapter 12 Importance of Calorimetry in Understanding Food Dehydration and Stability Yrjö H. Roos

Introduction Phase and State Transitions of Food Components Calorimetric Glass Transition Measurement Dielectric and Mechanical Relaxations Thermal Analysis in Characterization of Food Systems The Frozen State of Foods Systems State Diagrams and Dehydration Spray-Drying Freeze-Drying Glass Transition and Stability of Dehydrated Materials Conclusions References

289 292 293 297 298 301 303 305 306 307 308 309

Introduction Dehydration involves removal of solvent water from dissolved and hydrated food components. The process requires heat for evaporation or sublimation of water and concentration of food solids at high levels. This results in water removal to an almost anhydrous state of food components. Traditional dehydration processes are based on empirical knowledge of food material properties and processing needs to achieve desired product characteristics. Advanced dehydration processes, such 289

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as spray-drying and freeze-drying require more a fundamental understanding of water or ice properties and their removal as well as knowledge of physicochemical properties of the dehydrated solids (Roos 1995, 2002a, 2004). Thermal analytical and calorimetric measurements can often be used to characterize phase transitions of food solids and water (2002b). These measurements provide data that can be used to adjust dehydration conditions and temperatures to improve dehydration processes and product characteristics, such as flavor retention, storage stability, and flowability of powders (Roos 1995, 2004). Various phase and state transitions occur in food dehydration and storage of dehydrated foods. Phase transitions typically include evaporation of water and crystallization of food components (precrystallization before dehydration, crystallization during dehydration, and crystallization during storage). Most dehydrated materials, however, contain noncrystalline, amorphous solids. These can exist as solid glasses or viscous, supercooled fluids (White and Cakebread 1966; Slade and Levine 1995; Roos 1995, 2004). The glassy state of materials refers to the nonequilibrium, solid state, which is universal of all glass-forming materials, such as inorganic glasses, and synthetic noncrystalline polymers, sugars, and proteins as the main amorphous food components. Typical characteristics of the glassy state include transparency, solid appearance, and brittleness (White and Cakebread 1966; Sperling 1992). In noncrystalline, amorphous systems, molecules have no ordered structure, and the volume of the system is larger than that of the equilibrium crystalline systems with the same composition. Amorphous, noncrystalline systems can exist as glassy solids or supercooled liquids (rubber, leather, syrup) (Slade and Levine 1991; Roos 1995, 2004; Slade and Levine 1995), depending on their physical state, that is, apparent solid or liquid-like properties. The transition in which a solid, glassy material undergoes a change to a supercooled liquid is a change in state of the material rather than a change in phase (Sperling 1992; Roos 1995). This state transition is universally referred to as glass transition. Glass transition involves a change in heat capacity, which can be measured by calorimetric methods. As the glass transition is a change in the state of the system, it also results in a dramatic change in mechanical properties (Roos 1995). Changes in state and flow properties in food systems greatly

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affect their behavior in dehydration equipment and storage stability at low water contents. Glass transitions of amorphous components in foods are often monitored by differential scanning calorimetry and recorded from a step change in heat capacity (Roos 2002b). Understanding the physical state of food materials requires that properties of individual food components and their interactions with each other are well characterized. The first studies referring to glass formation by food components were those of dairy powders and glucose. It was recognized that sugars formed solid, noncrystalline structures (glasses) and that the properties of noncrystalline lactose in dairy powders and ice cream were often responsible for dramatic changes in product quality (White and Cakebread 1966). Slade and Levine (1991, 1995), Karel et al. (1994), and Roos (1995, 2004) emphasized that solid, dehydrated food systems, as well as frozen food systems, contain noncrystalline (amorphous) components and that the physical state of the components controls food properties and stability. For example, water can be removed from milk by dehydration or freezing. These processes remove solvent water, and the solute molecules often remain in a disordered, dissolved or dispersed “amorphous” state. Macroscopic observations of such systems have suggested that dehydration may result in glass formation. Calorimetric and other systematic studies are then required for full characterization of the glass-forming components and their properties in the dehydrated food systems. Carbohydrates and some proteins are the most typical hydrophilic components of food solids. These components may form amorphous, noncrystalline structures at low water contents (White and Cakebread 1966; Slade et al. 1991; Roos 1995, 2004). The most typical food processes resulting in glass formation by amorphous or partially amorphous food components include baking, extrusion, dehydration, and freezing (Roos 1995). Noncrystalline food solids are extremely sensitive to water and may show various time-dependent changes that result in a dramatic decrease in food quality. The most important quality-controlling parameter of amorphous food solids is their glass transition. Structural relaxations associated with increased molecular mobility in the vicinity of the glass transition are observed from rapid changes in food properties above the glass transition. The glass transition describes a temperature range over which a change of a solid glass to a softened material takes place, with the concomitant appearance

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of vibrational and translational mobility of component molecules (Sperling 1992). There are several glass transition-related changes in foods that affect their properties and stability. These include stickiness and caking of powders and sugar-containing products; collapse in freeze-drying and collapse of dehydrated structures; crispness of snack foods and breakfast cereals; crystallization of amorphous sugars; recrystallization of gelatinized starch; ice formation and recrystallization in frozen foods; and to some extent, nonenzymatic browning and enzymatic reactions (Roos 1995; Slade and Levine 1995; Roudaut et al. 2004). The objective of the present review is to highlight important properties of food components associated with their thermal behavior and the use of calorimetry and other thermal analytical techniques in the characterization of food systems, particularly with regard to their dehydration properties and stability control of dehydrated food systems.

Phase and State Transitions of Food Components Phase transitions indicate changes in the equilibrium state of materials, and they can be classified according to changes in thermodynamic properties (Roos 1995). The main requirement for any phase to coexist with another phase is that the Gibbs free energy of two or more phases at the transition pressure and temperature is the same. The equilibrium state is always that with the lowest Gibbs free energy. According to the thermodynamic classification of phase transitions, first-order phase transitions are those at which the first derivatives of the thermodynamic functions exhibit discontinuity; that is, at a first-order transition temperature there is a discontinuity in heat capacity and thermal expansion coefficient (Roos 1995). Such discontinuity occurs in melting/crystallization and boiling/condensation temperatures. A second-order phase transition shows a step change in heat capacity and thermal expansion coefficient. Glass transitions occur in thermodynamically nonequilibrium systems, and therefore they do not involve a thermodynamic change in phase. A glass transition may be considered as a “state transition” of an amorphous material with some of the thermodynamic characteristics of a second-order phase transition. The amorphous state, however, is a nonequilibrium state, and its properties are time-dependent. For

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Equilibrium Liquid

(Pressure)

s

C oo lin g R ap id

Cooling

Crystallization

ow Sl

H

tin g

g in ol Co

GLASS

ea

Heating

g in at He

Equilibrium Liquid

Cooling

RUBBER

Nonequilibrium Solid

Heating

CRYSTAL

a Pl

Calorimetric Measurements

eh yd ra tio n

a iz tic

S

n tio

D

a t ur at io n

Equilibrium Solid

So lu bi liz at io n

SOLUTION

MELT

Figure 12.1. Equilibrium and nonequilibrium states of materials. Materials suffer state and phase transitions in various food-processing and storage conditions, which include equilibrium phase transitions such as crystallization and melting and nonequilibrium state transitions as a result of changes in temperature, water content, or both.

example, molecules in an amorphous material can have an infinite number of intermolecular arrangements; that is, amorphous materials have similar molecular disorder to that of liquids and gases, and any observed characteristics may be specific to the material only at the time of observation. Changes in amorphous materials may be followed as a function of time, and the rates of changes are likely to depend on rates of molecular relaxations and diffusion within the amorphous state. In food dehydration processes, depending on the rate of solvent (water) removal or cooling of the amorphous food solids into the solid glassy structures, different characteristics of the glassy state of the same material can be obtained. Various states of materials, their phase and state transitions, and glass formation in dehydration processes are described in Figure 12.1.

Calorimetric Glass Transition Measurement The glass transition is a change in state associated with a considerable change in molecular mobility. Molecular mobility is time-dependent and no exact glass transition temperatures can be measured or defined.

Calorimetry in Food Processing Exothermal Heat Flow

294

Exotherm Onset Midpoint

?

Time-dependent changes

ΔC p Endset

Endotherm T

Figure 12.2. Schematic representation of typical DSC curves obtained for amorphous materials in heating over their glass transition temperature range. The glass transition temperature, Tg, is often taken from the onset temperature of the glass transition or as the midpoint value corresponding to 50% change in heat capacity occurring over the glass transition. Glass transition may involve an exotherm or an endotherm corresponding to differences in glass formation (heating/cooling rates; solvent removal/sorption rates).

Observed changes in heat capacity and characteristic changes around the glass transition occur over a temperature range. The glass transition temperature is often the onset temperature of the glass transition temperature range (onset Tg) or the temperature corresponding to a 50% heat capacity change over the transition (midpoint Tg) as measured by differential scanning calorimetry (DSC; Figure 12.2). Glass transitions have been reported for a wide range of food components, including sugars and other carbohydrates as well as proteins (Slade and Levine 1995; Roos 1995). Pure food components often show a single, clear glass transition in DSC thermograms about 100 ° to 150 °C below their equilibrium melting temperature, Tm. Calorimetric techniques measure the change in heat capacity associated with glass transition. This can be observed in heating or cooling of the material, as the glass transition is reversible. An increase in heat capacity occurs when a supercooled liquid is heated over its glass transition. The temperature range of the glass transitions is highly dependent on food composition and molecular weight of components. Low-molecularweight components, for example, water and simple sugars, show glass

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transition over a relatively narrow temperature range followed by rapidly increasing flow above the glass transition (Roos 1993). Highmolecular-weight food components, such as proteins and starch, as well as heterogeneous food systems often show glass transition over a wide temperature range (Hoseney et al. 1986; Roos 1995; Slade and Levine 1995; Ronda and Roos 2008). The glass transition of systems with two or more components is dependent on component properties and their miscibility. Solvents, such as water in foods, often have a low molecular weight, and they are fully miscible with their glass-forming solutes. The glass transition temperature of a solute is highly dependent on the presence and concentration of solvents. Even very small amounts of solvent may substantially decrease the observed glass transition temperature. It has been found that an increasing amount of water decreases both the glass transition temperature and its temperature range, but it also increases the change in heat capacity of the transition (Roos and Karel 1991a, 1991b). Other mixtures of miscible components, for example, sugars, also show composition-dependent glass transition temperatures, and they are often substantially affected by the lower-molecular-weight components (Roos 1995). This can also be observed in edible films plasticized by other plasticizers, such as glycerol and sorbitol (Talja et al. 2007). In food systems, several components (e.g., starch, proteins) can exist in partially amorphous states, and many of them exhibit only partial miscibility or remain immiscible, forming single or several phases within food microstructure (Kalichevsky and Blanshard 1993; Vega et al. 2005). In food processing and storage, the glass transition occurs in both cooling and heating over the glass transition temperature range, and glass transition of food solids often takes place during removal of water in freezing and dehydration processes (Figure 12.1). The materials show the reversible characteristics of the glass transition and also a tendency to transform toward the equilibrium state. As described by Figure 12.1, the glassy state is a nonequilibrium amorphous state and the glass transition is a time-dependent property. It may occur at varying temperatures at different experimental time scales (e.g., frequency), as shown in Figures 12.3 and 12.4. Furthermore, depending on the rate of glass formation and possible changes occurring with time in the glassy state (aging), various relaxations may be observed over the glass transition (Figures 12.2, 12.3, and 12.4).

V H S

Anomalous Changes In Thermodynamic Properties Depending on Glass Properties Non-equilibrium State

Equilibrium State Non-equilibrium State

Liquid

Supercooled liquid

ΔHm

Crystal

Glass

≈100-150°C

Tg

Tm

T

MECHANICAL OR DIELECTRIC PROPERTY

Figure 12.3. Thermodynamic states of materials. The equilibrium liquid state occurs at and above the equilibrium melting temperature, Tm. At lower temperatures, the crystalline solid state may exist at equilibrium. Noncrystalline systems can be supercooled liquids or they may become solidlike supercooled materials (glasses) below the glass transition temperature, Tg. However, the glassy state is a nonequilibrium and time-dependent state. It may have various molecular arrangements with different enthalpy, H, entropy, S, and volume, V, states. Melting of crystals involves heat of melting, ΔHm, but the glass transition involves no latent heat of the transition.

Storage modulus or dielectric constant

Increasing frequency Mechanical and dielectric relaxations

Loss modulus or dielectric loss

γ and β relaxations

Tg

α relaxation

TEMPERATURE

Figure 12.4. Mechanical and dielectric relaxations of materials in the glassy state and around the glass transition.

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Dielectric and Mechanical Relaxations Dielectric analysis (DEA/DETA) and dynamic mechanical analysis (DMA/DMTA) are thermal analytical methods that allow complementary characterization of amorphous food systems (Kalichevsky et al. 1992; Talja and Roos 2001; Roudaut et al. 2004). These techniques detect relaxations in dielectric and mechanical properties of the materials. In general, amorphous structures are fairly stable in the solid, glassy state (Sperling 1992; Slade and Levine 1995), and the relaxation times extend to several years or decades. At temperatures around and above the transition, the solid state is rapidly transformed to a supercooled liquid state (viscous fluid) with more rapid time-dependent flow (White and Cakebread 1966; Roos 1995; Roudaut et al. 2004). The change in mechanical properties is observed by DMA and detected as a change in the complex moduli of the material. These changes are referred to as α relaxations, which also appear as analogous changes in dielectric properties in a DEA analysis (Figure 12.4). For example, dehydrated, glassy foods have a solid and brittle behavior, whereas the materials may flow as syrups or become soggy above the glass transition (e.g., freeze-dried foods). This change is associated with a decreasing modulus appearing in a DMA analysis as well as a decrease of the dielectric constant. Mechanical and dielectric properties detect glass transition in foods by their sensitivity to relaxations and changes in modulus and dielectric properties (Kalichevsky et al. 1992; Sperling 1992; Talja and Roos 2001). Glass transition as such cannot be measured by DEA or DMA, but these techniques detect relaxations associated with the change in heat capacity (Figure 12.4). Several relaxations may appear at lower temperatures (β and γ relaxations), and they appear as changes in storage modulus, E′ or G′; loss modulus, E″ or G″; dielectric constant, ε′; dielectric loss constant, ε; and mechanical and dielectric loss, tan Δ, below the glass transition (Figure 12.4). The α relaxation is the main relaxation associated with the glass transition. The observed relaxation temperatures are highly dependent on the frequency, f, of the applied stress or dielectric disturbance, which clearly indicate the time-dependent characteristics of noncrystalline materials under disturbance. Many researchers have used frequencies around 1 Hz in DMA and DEA measurements. However, it seems that frequencies indicating relaxations at the calorimetric glass transition onset

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temperature occur at much lower frequencies (Talja and Roos 2001). These frequencies may be in the range of 0.01–0.1 Hz, and they indicate the onset of molecular mobility or viscous flow within the glassy material; that is, the solid characteristics are changing to liquid-like material properties.

Thermal Analysis in Characterization of Food Systems DSC is the most universal thermal analytical technique used to detect phase and state transitions of food systems. DSC requires minimal sample preparation, and the materials studied can be hermetically sealed in sample pans for the analysis at known water contents. Amorphous or partially amorphous structures in foods are formed in food processing, particularly as the result of water removal and dehydration. Loss of water causes the concentration of solids to increase, for example in baking, dehydration, freezing, and extrusion, as described by Figure 12.1. Depending on the rate of solvent removal or cooling into the solid state, glasses with different properties can be obtained (Figure 12.2 and 12.3). These food processes form concentrated, supercooled, amorphous, nonequilibrium materials that exhibit time-dependent changes. The materials exhibit a thermodynamic driving force toward an equilibrium state, for example, the crystalline state. This is typically observed in a DSC scan of pure food components, such as lactose and sucrose, which show a glass transition followed by a crystallization exotherm. Crystallization is time-dependent, but pure substances often show instant crystallization at a scanning rate-dependent temperature (Figure 12.5). The glass transition occurs over a temperature range, although it is often referred to with a single temperature value (Figure 12.3). Glass transition may be present in either low-moisture and dehydrated foods or frozen foods in which a concentrated solute phase is formed because water is separated as a crystalline ice phase within the material. Several real food systems and components may exist as only partially amorphous materials, and many food components are only partially miscible or immiscible forming single or several phases within food microstructure; for example, carbohydrate, protein or lipid-rich regions or phases. Glass transition occurs in both cooling and heating and also in removal or sorption of a plasticizer or a solvent or both. The main

Importance of Calorimetry in Understanding Food

299

Glass transition ENDOTHERMAL HEAT FLOW

Tg

Melting

T cr

Tm

Crystallization

TEMPERATURE

Figure 12.5. Schematic representation of DSC curve typical of amorphous sugars with glass transition, Tg, and crystallization, Tcr, of the amorphous phase and melting of the crystals at Tm.

plasticizer of amorphous food solids is water. Water is a plasticizer to most carbohydrates and proteins. Lipids exist in separate, hydrophobic phases and show little interactions with hydrophilic components and functional groups of food polymers (Roos 1995). Water softens food solids by decreasing their glass transition temperature toward that of water, at around −135 °C. The presence and interaction of water molecules with food solids results in changes in the amorphous structure. The glass transition of dehydrated food solids decreases as a result of water sorption (water uptake from surroundings), and their properties may change from those of the glassy solid to viscous liquids or syrup (sugar systems) or leathery material (protein systems) in an isothermal water sorption process. The glass transition of amorphous sugars occurs over a temperature range of 10 °–20 °C, whereas the glass transition of food polymers may extend over a temperature range of more than 50 °C (Hoseney et al. 1986; Roos 1995; Roudaut 2004). The change in heat capacity (ΔCp) of sugar glasses around their glass transition is around 0.5–1.0 J/g °C, and the transition occurs over a temperature range of about 10 °–20 °C (Roos 1993). The ΔCp of proteins and starch is often quite small, and the glass transition may occur over a broad temperature range. The magnitude of the glass transition often increases with increasing water content, and the transition occurs over a more narrow temperature

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range. The glass transition of proteins and polysaccharides may only be measurable by DSC with a relatively large level of water plasticization (Roos 1995). Food systems also may exhibit numerous glass transitions depending on composition and extent of phase separation (miscibility). Some anhydrous food components, for example several monosaccharides and polyols, have their anhydrous glass transitions below room temperature, and they cannot be dehydrated to solid materials (Roos 1993, 1995). Many carbohydrates and proteins as well as other polymeric food components have glass transitions in dry states above 200 °C, which approaches their decomposition temperature. A common problem in observing glass transitions in food systems at intermediate water content is that liquid-crystalline transitions of lipids often overlap the glass transition of hydrophilic food solids. The effect of water on the glass transition can be predicted, for example, using the Gordon-Taylor equation (Gordon and Taylor 1952). We have combined the water sorption data and glass transition data to establish diagrams showing critical values for water content and water activity that result in glass transition at the storage temperature (Figure 12.6). Such diagrams can be established using, for example, the Gordon-Taylor equation to model water plasticization and the

120

60

0.6

Glass Transition Region (Temperature-dependent critical storage parameters)

40 20 0

Tg

-20 -40

Critical Water Activity (25°C)

Critical Water Content (25°C)

-60 0

10

20

30

0.4

0.2

WATER ACTIVITY

TEMPERATURE (°C)

80

-80

0.8

Extrapolated GAB Sorption Isotherm Time-dependent crystallization

100

0 40

WATER CONTENT (g/100g dry solids)

Figure 12.6. Glass transition and water sorption behavior of lactose. Water sorption results in lactose crystallization as the glass transition at the observation temperature is exceeded, and the water activity and water content become higher than the critical values.

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Guggenheim-Anderson-De Boer (GAB) equation to model water sorption (Roos 1995).

The Frozen State of Foods Systems Freezing of water in food systems occurs at temperatures below the equilibrium melting temperature of water, Tm. The equilibrium melting temperature refers to the temperature at which the last ice crystals melt during heating of a frozen material. The equilibrium melting temperature is dependent on dissolved food components and their concentration. Below Tm, freezing of water may continue until an equilibrium amount of ice appears at the freezing temperature or a kinetically limited maximum amount of ice has formed at a lower temperature (Roos and Karel 1991c, Roos 1995). A maximally freeze-concentrated system shows an initial solute concentration-independent glass transition temperature, Tg′, of solutes plasticized by the unfrozen water. The unfrozen water is a continuous phase, with dispersed ice crystals that exhibit an onset of ice melting during heating at an initial concentration-independent temperature, Tm′. This behavior has been well established for many common sugars and carbohydrates (Goff 1995; Roos 1995; Slade and Levine 1995; Singh and Roos 2005). Typically, the solute concentration of a glassy unfrozen solute phase dispersing the maximum amount of ice formed in a frozen system is around 80% (w/w) (Slade and Levine 1991; Roos 1993; Talja and Roos 2001; Singh and Roos 2005). These transitions can be described by DSC curves of nonannealed and annealed systems (Figure 12.7). The kinetic limitations for ice formation and time-dependent characteristics of maximum freeze concentration can be related to the limited diffusion, high viscosity, and longer relaxation times as the glassy state of the unfrozen solids-unfrozen water phase is approached (Figure 12.8). Phase and state transitions of maximally freeze-concentrated materials are complex and show the nonequilibrium nature of ice formation in calorimetric and thermal analytical studies. However, the same thermal analytical techniques, as well as electron spin resonance (ESR) and nuclear magnetic resonance (NMR) techniques, provide information about transitions of freeze-concentrated solids and ice melting (Slade and Levine 1995; Roos 1995; Laaksonen et al. 2002; Roudaut et al. 2004). Low-molecular-weight components, such as sugars, often

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Initial Tg

Unfrozen state

Annealing time t0

Partial freeze-concentration

Tm

t1

t2

T'g Maximal freeze-concentration

T'm

t3

Annealing temperature

TEMPERATURE

Figure 12.7. A schematic representation of time-dependent ice formation in freezeconcentrated food systems. A rapidly cooled system shows a glass transition corresponding to the solute-water ratio of the solution when no ice is formed. Ice formation may be achieved by annealing (isothermal holding) at a temperature below Tm′. This can be detected from an increasing glass transition temperature, Tg, for the freezeconcentrated unfrozen solute phase and an increasing size of the ice-melting endotherm. At maximum ice formation, more ice cannot be formed, and the glass transition occurs at initial water content-independent temperature, Tg′, and is followed by onset of ice melting at Tm′.

exhibit clearly observable and separate but time-dependent transitions (Roos and Karel 1991b,c). In DSC measurements, the glass transition temperature of the maximally freeze-concentrated solute, Tg′, must be taken from the onset temperature of the transition temperature range. The midpoint or endpoint of the transition cannot be defined because the glass transition is often not complete prior to the ice-melting transition (Roos 1995). Hence, the ΔCp of the glass transition may remain unknown or a value that is too low may be obtained because the glass transition may not be complete when the first ice crystals in the frozen system melt. Melting of ice gives a relatively sharp endothermic peak, and its onset temperature can be taken as the onset of ice melting, Tm′, for ice within the maximally freeze-concentrated system (Roos and Karel 1991c). As described in Figure 12.7, freezing to this maximally freeze-concentrated state may require an isothermal treatment (anneal-

Importance of Calorimetry in Understanding Food T–T g Relative Relaxation Time (°C) 40 2.4x10 –8

TEMPERATURE

Tm

20

1.3x10 –5

0

1.0x10

303

Supercooled liquid

0

Tm’

low sf u co olid Vis ys

Tg’

ation ion α-relax ansit r t s Glas 0

ss Gl a

Thermal plasticization

Maximum Ice Formation

Water plasticization

WEIGHT FRACTION OF SOLIDS

1.0

Figure 12.8. Schematic state diagram showing the decrease in glass transition with increasing water content and decreasing relaxation times at increasing levels of thermal or water plasticization. Maximum ice formation takes place time dependently over the temperature range from the glass transition temperature of the maximally freeze-concentrated unfrozen phase, Tg′, and onset of ice melting in the maximally freeze-concentrated unfrozen phase, Tm′. Equilibrium ice melting occurs according to the equilibrium ice-melting temperature, Tm, curve.

ing) at a temperature favoring maximum ice formation (Roos and Karel 1991c; Singh and Roos 2005).

State Diagrams and Dehydration Calorimetric transition temperatures can be shown in state diagrams (Figure 12.8 and 12.9). State diagrams are often used to show phase and state transition data to describe material states at various temperatures and levels of water plasticization (Roos and Karel 1991a; Roos 1995; Slade and Levine 1991, 1995). A typical state diagram shows the glass transition temperature against water content with Tg′, Tm′, and Tm data as shown for lactose in Figure 12.9. The effect of water on the glass transition can be predicted, for example, using the Gordon-Taylor equation (Roos 1995), which uses the solids, water glass transition

Calorimetry in Food Processing

50

0

-50

-100

Glass transition Solubility range (equilibrium mixture Time-dependent of α - and crystallization β -lactose) Supercooled Calorimetric transition Tg liquid temperatures

T'm

Equilibrium freezing zone

T'g

Temperature range for maximum ice formation

s

Temperature (°C)

100

Tg Glass

-150 0.0

Gla s

304

0.2 0.4 0.6 0.8 Weight Fraction of Lactose

C'g 1.0

Figure 12.9. State diagram of lactose with the glass transition temperature, Tg, curve, and transition temperatures for maximally freeze-concentrated lactose solutions (Tg′ is the glass transition temperature of a maximally freeze-concentrated solution, and Tm′ is onset temperature of ice melting in a maximally freeze-concentrated solution).

temperatures, and their weight fractions to predict the glass transition temperatures at various water contents. We also have used combined water sorption and glass transition data to establish diagrams showing critical values for water content and water activity. The critical water activity and water content have been defined as those corresponding to the glass transition occurring at a processing or storage temperature for which water sorption isotherm is shown (Figure 12.6). Such diagrams can be obtained, for example, by using the Gordon-Taylor equation to model water plasticization and the GAB relationship to model water sorption (Roos 1995). The critical water content and water activity diagrams, together with state diagrams, are important tools in explaining changes in time-dependent mechanical and flow properties that are related to glass transition and water plasticization (Slade et al. 1991; Kokini et al. 1994; Roos 1995; Roos et al. 1996; Rahman 2006). Isoviscous states or relaxation time curves can be shown in state diagrams to describe rapid changes of time-dependent characteristics of food systems above the glass transition.

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Spray-Drying Spray-drying is an efficient dehydration method for a large variety of liquid materials and slurries, which can be converted to small liquid droplets using rotating disks or pressure nozzle atomizers. The tiny droplets can be dehydrated in hot air within seconds, which allows continuous production of free-flowing powders. Although the principle of the process is relatively simple, phase and state transitions of food solids have a significant impact on whether the materials can be spraydried successfully or whether the powders have free-flowing properties in handling, packaging stages, storage, and use. It has been suggested that formation of the glassy state from solids in spray-drying, particularly the glass-forming properties of carbohydrates, have a correlation with spray-drying behavior of fruit juices and materials rich in sugars (Bhandari and Howes 1999). These materials are often extremely difficult to dehydrate because the solids tend to stick on drier surfaces and cake inside dehydration- and powderhandling equipment. Stickiness is probably the most important property in establishing criteria for the suitability of food materials to spray-drying. Studies of glass transitions of dehydrated sugars and high-sugar products have confirmed that stickiness is related to the glass transition of amorphous powders (Roos and Karel 1990). Based on the knowledge of phase and state transitions in dehydration of liquids with dissolved substances, it may be assumed that the rapid removal of water causes vitrification of the liquid droplets (i.e., solids in the droplets dehydrate and form glasslike structures) within a short time and formation of a solid particle surface (Roos 2004). Glass transition of the solids at the surface layer of a drying droplet is a key parameter in defining stickiness behavior of the particles and formation of liquid bridges occurring in subsequent caking as a result of rapid decrease in surface viscosity above the glass transition. Therefore, materials with an anhydrous glass transition below room temperature cannot be spray-dried, as they cannot be converted to a solid state at room temperature. The viscosity changes resulting from the glass transition also can be used to control agglomeration of fine particles and in the manufacturing of instant powders (Roos 1995). In such processes, it is essential to allow controlled stickiness on particle surfaces and adhesion of particles to form clusters. The formation of clusters is followed by

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Freeze-concentrated unfrozen solute phase

Freeze-dried glass solute membranes Ice

Pores

Freeze-drying below T m’

id Sol Flo w Freeze-drying above T m’

Collapsed liquid

Figure 12.10. The role of onset temperature of ice melting, Tm′, in successful freezedrying and liquid flow resulting in collapse as ice temperature exceeds Tm′ in a freezedrying process.

removal of water and cooling to solidify the surfaces into the glassy state. The final product will have larger particles and remain free flowing and stable at appropriate storage conditions. Freeze-Drying The Tg′ and Tm′ temperatures of biological materials are extremely important determinants of appropriate operation parameters in freezedrying (Roos 2004). Freeze-drying requires that dissolved substances are freeze-concentrated to an almost solid state and that the highly viscous state is retained throughout the dehydration process; that is, the material should consist of solid ice and a freeze-concentrated, solid, glassy, unfrozen phase (Figure 12.10). Ice melting above Tm′ has a dramatic plasticization effect in a freeze-concentrated system and results in liquid flow. The effect of state transitions and ice melting above Tm′ are described for freeze-drying in Figure 12.10. Accordingly, the highest allowable pressure (or ice temperature) in freeze-drying is defined by the initial melting temperature of ice in the system. At conditions allowing melting, flow may occur, and some dehydration occurs from the liquid state; the process no longer can be referred to as freeze-drying (Figure 12.10).

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Loss of structure known as collapse in freeze-drying occurs above a critical temperature that allows the viscous flow of freeze-concentrated amorphous solutes (Bellows and King 1973) as they are plasticized by unfrozen water (Roos 2004). The onset temperature of ice melting, Tm′, determines a temperature at which a maximally freezeconcentrated system becomes plasticized by dissolving ice crystals; therefore, Tm′ can be used as a critical reference temperature for production of properly freeze-dried materials (the ice vapor pressure in freeze-drying must be kept below the ice vapor pressure at Tm′ by the control of drying pressure and heat supply for sublimation of ice). Collapse can be avoided by the use of ice temperatures below Tm′ in freeze-drying, and the Tm′ values agree well with collapse temperatures reported for freeze-drying of carbohydrate systems (Bellows and King 1973; To and Flink 1978; Roos 1995).

Glass Transition and Stability of Dehydrated Materials Stickiness and caking are common problems in handling of powders containing amorphous carbohydrates. Stickiness and caking appear as the viscosity of the amorphous components decreases and powder particles adhere as they gain liquid-like flow properties at conditions resulting in glass transition. Glass transition of lactose may occur as a result of water plasticization in dairy powders. Such plasticization is often the cause of time-dependent lactose crystallization. An instant crystallization may be observed at a high level of rapid thermal and water plasticization (Jouppila et al. 1997; Haque and Roos 2005). A schematic representation of glass transition-related flow and its effect on food material behavior, including development of stickiness, caking, and crystallization, is shown in Figure 12.11. The crystallization of lactose has been found to be highly time-dependent following the typical crystallization rate behavior of amorphous solids (Roos and Karel 1991a, Jouppila et al. 1997). The time-dependent lactose crystallization in dairy powders is often observed in water sorption studies (Haque and Roos 2005). These have shown that above a critical storage relative humidity, there is a loss of sorbed water (Figure 12.6). The loss of sorbed water in dairy powders corresponds to the difference in water sorption by amorphous and crystalline lactose. However, it should be noted that the loss of sorbed water is time-dependent, and

Calorimetry in Food Processing

Stability Zone ‘Solid’

Glass Transition Fermi’s Model (M. Peleg) CRITICAL ZONE VISCOUS FLOW Increasing Diffusion

Structural Transformations

SOLID

Crispness

RELAXATION TIME

Months Days Hours Minutes Seconds

Glassy State

Flow

LIQUID

Critical Zone

Mobility Zone

‘Highly time-dependent’

‘Instant changes’

EXTENT OF CHANGE IN PROPERTY

Years

Hardening, Cracking

308

TEMPERATURE, WATER ACTIVITY OR WATER CONTENT

Figure 12.11 Changes in relaxation times as a result of thermal or water plasticization in food systems. Around and above the glass transition rapidly appearing liquidlike properties of the materials result in dramatic changes in mechanical properties and diffusion. The changes in mechanical properties around glass transition may be modeled using the Fermi relationship (Peleg 1993).

the crystalline form of lactose produced is dependent on the crystallization conditions (Jouppila et al. 1997; Haque and Roos 2005). Most crystals formed are anhydrous, but at the higher storage humilities increasing amounts of α-lactose monohydrate is formed. Crystallization of amorphous lactose in sealed packages and in bulk storage also results in an increase in water activity and acceleration of most deteriorative changes, such as browning reactions and oxidation.

Conclusions Thermal and calorimetric properties of food and biological materials at various water contents are extremely important determinants of their dehydration and stability characteristics. Calorimetric measurements provide data for selection of appropriate dehydration parameters and manipulation of solids composition to enhance dehydration and improve storage stability. Several dehydrated materials, particularly spray-dried and freeze-dried, exist as amorphous, glassy solids. Formation of a solid structure contributes to the success of dehydration processes and the quality characteristics of dehydrated materials. Knowledge of glass transitions and ice-melting properties of sensitive

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materials is the basis of successful freeze-drying. Freeze-drying may only take place below the onset temperature of ice melting in a frozen system, as higher temperatures allow flow of freeze-concentrated matrices as well as collapse and loss of quality. Relationships between flavor retention and encapsulation of volatiles and dispersed components, and formation of a glassy, continuous hydrophilic phase in dehydration processes, are important in stabilization of such components. Solids crystallization, lipid oxidation, nonenzymatic browning, and enzymatic changes are often interrelated and controlled by the glass transition and water. References Bellows R.J. and King C.J. 1973. Product collapse during freeze drying of liquid foods. AIChE Symp Ser, 69(132):33–41. Bhandari B.R. and Howes T. 1999. Implication of glass transition for the drying and stability of dried foods. J Food Eng, 40:71–79. Goff H.D. 1995. The use of thermal analysis in the development of a better understanding of frozen food stability. Pure Appl Chem, 67:1801–1808. Gordon M. and Taylor J.S. 1952. Ideal copolymers and the second-order transitions of synthetic rubbers. I. Non-crystalline copolymers. J Appl Chem, 2:493–500. Haque M.K. and Roos Y.H. 2005. Crystallization and x-ray diffraction of spray-dried and freeze-dried amorphous lactose. Carbohydr Res, 340:293–301. Hoseney R.C., Zeleznak K., and Lai C.S. 1986. Wheat gluten: A glassy polymer. Cereal Chem, 63:285–286. Jouppila K., Kansikas J., and Roos Y.H. 1997. Glass transition, water plasticization, and lactose crystallization in skim milk powder. J Dairy Sci, 80:3152–3160. Kalichevsky M.T. and Blanshard J.M.V. 1993. The effect of fructose and water on the glass transition of amylopectin. Carbohydr Polym, 20:107–113. Kalichevsky M.T., Jaroszkiewicz E.M., Ablett S., Blanshard J.M.V., and Lillford P.J. 1992. The glass transition of amylopectin measured by DSC, DMTA, and NMR. Carbohydr Polym, 18:77–88. Karel, M., Anglea, S., Buera, P., Karmas, R., Levi, G., and Roos, Y. 1994. Stabilityrelated transitions of amorphous foods. Thermochim Acta, 246:249–269. Kokini J.L., Cocero A.M., Madeka H., and de Graaf E. 1994. The development of state diagrams for cereal proteins. Trends Food Sci Technol, 5:281–288. Laaksonen T.J., Kuuva T., Jouppila K., and Roos Y.H. 2002. Effects of arabinoxylans on thermal behavior of frozen wheat doughs as measured by DSC, DMA, and DEA. J Food Sci, 67:223–230. Peleg, M. 1993. Mapping the stiffness-temperature-moisture relationship of solid biomaterials at and around their glass transition. Rheol Acta, 32:575–580. Rahman M.S. 2006. State diagram of foods: Its potential use in food processing and product stability. Trends Food Sci Technol, 17:129–141.

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Ronda F. and Roos Y.H. 2008. Gelatinization and freeze-concentration effects on recrystallization in corn and potato starch gels. Carbohydr Res, 343:903–911. Roos Y. 1993. Melting and glass transitions of low molecular weight carbohydrates. Carbohydr Res, 238:39–48. Roos Y.H. 1995. Phase Transitions in Foods. Academic Press: San Diego. Roos Y.H. 2002a. Importance of glass transition and water activity to spray drying and stability of dairy powders. Le Lait, 82:475–484. Roos Y.H. 2002b. Thermal analysis, state transitions, and food quality. J Therm Anal Calorim, 71:197–203. Roos Y.H. 2004. Phase and state transitions in dehydration of biomaterials and foods. In: Dehydration of Products of Biological Origin, A.S. Mujumdar, editor, pp. 3–22. Science Publishers: Enfield. Roos Y. and Karel M. 1990. Differential scanning calorimetry study of phase transitions affecting the quality of dehydrated materials. Biotechnol Progr, 6:159–163. Roos Y. and Karel M. 1991a. Applying state diagrams to food processing and development. Food Technol, 45, 66, 68–71, 107. Roos Y. and Karel M. 1991b. Nonequilibrium ice formation in carbohydrate solutions. Cryo-Letters, 12:367–376. Roos Y. and Karel M. 1991c. Amorphous state and delayed ice formation in sucrose solutions. Int J Food Sci Technol, 26:553–566. Roos Y.H., Karel M., and Kokini J.L. 1996. Glass transitions in low moisture and frozen foods: Effects on shelf life and quality. Food Technol, 50(11):95–108. Roudaut G., Simatos D., Champion D., Contreras-Lopez E., and Le Meste M. 2004. Molecular mobility around the glass transition temperature: A mini review. Innov Food Sci Emerg Technol, 5:127–134. Singh K.J. and Roos Y.H. 2005. Frozen state transitions of sucrose-protein-cornstarch mixtures. J Food Sci, 70(3):E198–E204. Slade L. and Levine H. 1991. Beyond water activity: Recent advances based on an alternative approach to the assessment of food quality and safety. Crit Rev Food Sci Nutr, 30:115–360. Slade L. and Levine H. 1995. Glass transitions and water-food structure interactions. Adv Food Nutr Res, 38:103–269. Sperling L.H. 1992. Introduction to Physical Polymer Science, 2nd edition. John Wiley & Sons: New York. Talja R.A. and Roos Y.H. 2001. Phase and state transition effects on dielectric, mechanical, and thermal properties of polyols. Thermochim Acta, 380:109–121. Talja R.A., Helén H., Roos Y.H., and Jouppila, K. 2007. Effect of various polyols and polyol contents on physical and mechanical properties of potato starch-based films. Carbohydr Polym, 67(3):288–295. To, E.T. and Flink, J.M. 1978. “Collapse,” a structural transition in freeze dried carbohydrates. II. Effect of solute composition. J Food Technol, 13:567–581. Vega C., Kim E.H.J., Chen X.D., and Roos Y.H. 2005. Solid-state characterization of spray-dried ice cream mixes. Coll Surf B: Biointerfaces, 45:66–75. White G.W. and Cakebread S.H. 1966. The glassy state in certain sugar-containing food products. J Food Technol, 1:73–82.

Chapter 13 High-Pressure Calorimetry and Transitiometry* Stanislaw L. Randzio and Alain Le Bail

Introduction High-Pressure Calorimetry Scanning Transitiometry Applications Water in Pork Muscle Frozen Water Ratio in Gelatine Gels Pressure Shift Freezing Gelatinization of Starch Phase Stability of Systems Containing Lipids Conclusions References

311 313 317 324 324 326 329 330 336 337 338

Introduction The discovery by Bridgman in 1914 (Bridgman 1914a) of a pressureinduced coagulation of egg white enhanced research on the influence of pressure on biological and food systems. From the results obtained over the years, it became evident that the use of pressure can lead to developments of food products with new properties, such as highpressure-processed jams prepared by nonheating food processing, which entered the market in Japan in 1992 (Hayashi 2002). Other important technological applications are concerned with high-pressure processing of food, especially freezing-thawing and crystallization *M. Malecki is acknowledged for technical assistance in the preparation of this chapter.

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processes (Knorr 1999) and the use of extruders. An understanding of the effects of processing on the physical properties of food materials should allow prediction of the formulation of raw materials and processing conditions so as to achieve desired end product properties. To achieve this goal, systematic studies must be done to develop a database on the physical properties of food materials as a function of variables relevant to processing (Kaletunç and Breslauer 1996). Calorimetry is a powerful tool for determination of thermophysical properties of matter and processes over wide ranges of external conditions (Randzio 1998; Randzio 2002), and thus it is a suitable tool for creating such databases. However, the calorimetric techniques, mainly differential scanning calorimetry (DSC), to date have been mostly used only to evaluate the effects of high-pressure processing, the calorimetric measurements being performed after processing under atmospheric pressure. For example, Stute et al. (Stute et al. 1996) compressed aqueous suspensions of wheat starch at 293 K at various pressures up to 500 MPa, and then after compression up to a selected pressure, the sample was decompressed, removed from the autoclave, and analyzed with a classical DSC to verify the degree of pressure-induced gelatinization at 293 K. Douzals et al. (Douzals et al. 2001) have placed the autoclave in a thermostat and were able to perform similar pressure measurements over the temperature interval from 253 K to 373 K. However, the degree of gelatinization caused by the processing at various pressures and temperatures also was determined after processing by using classical DSC under atmospheric pressure. However, one must note that such a use of DSC can give indicate the effect of pressure only for irreversible phenomena. To be able to understand the real role of pressure in food processing one must know the phase diagrams of its constituents and the mechanisms of transitions between the phases or states. Phase diagrams of biomacromolecules and biopolymers can be extremely complicated (Smeller 2002), and an interplay between temperature and pressure is sometimes difficult to interpret. For this reason, it is important to make calorimetric measurements under typical processing conditions of high hydrostatic pressure and temperature under well-determined pressure and temperature conditions and to determine the thermodynamic and thermomechanic parameters of the transitions. This chapter describes such direct high-pressure calorimetric techniques and reviews their applications in investigation of selected systems important for food science and technology.

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High-Pressure Calorimetry Figure 13.1 presents a schematic diagram of a high-pressure calorimetric system developed by LeBail et al. (Le Bail et al. 2001; Zhu et al. 2004). The high-pressure calorimetric system is composed of a differential calorimetric detector made from 220 thermocouples interconnected between the two calorimetric vessels, high-pressure pump, hydraulic fluid reservoir, pressure detector (Asco Instruments, France), and a circulating liquid thermostat. The pressure in the system was controlled by proportional-integral-derivative computer software through a stepping motor and a gear box of the high-pressure pump (Nova Swiss, Switzerland). The processing of the calorimetric signal was performed with computer software. The investigated substance was always placed in a small flexible plastic pouch. A detailed view of the calorimetric system elements can be seen in Figure 13.2. The high-pressure vessels connected to the high-pressure hydraulic system by flexible stainless steel capillary tubing can be easily introduced into the cavities of the differential calorimetric detector, placed in the calorimetric block, which in turn is surrounded with a copper coil in which

Figure 13.1. Schematic diagram of a differential high-pressure calorimeter: (1) differential calorimetric detector, (2, 3) calorimetric vessels, (4) high-pressure pump, (5) hydraulic fluid reservoir, (6) pressure detector, (7) circulating liquid thermostat, (8), pressure control, (9) calorimetric signal processing, (10) investigated substance in a flexible plastic pouch.

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Figure 13.2. Experimental setup of a high-pressure calorimeter: (1) high-pressure vessels, (2) flexible stainless steel capillary tubing, (3) cavities of the differential calorimetric detector, (4) calorimetric block, (5) copper cooling coil.

a temperature-controlled liquid circulates, ensuring a good temperature stability. The whole system is placed in a stainless steel flask filled with heat-insulating material. Figure 13.3 presents a detailed view of a high-pressure calorimetric vessel made from stainless steel. The investigated substance was sealed under vacuum in a polyethylene pouch. The high-pressure closing of the vessels was done from one side with a nitrile O-ring placed on a plug, with a threaded plug holding it in place. From the other side, the calorimetric vessels were connected to the hydraulic high-pressure system through stainless tubing (3.2 mm outside diameter) and standard Harwood connections. The internal volume of the calorimetric vessels was 4.6 cm3. The pressure sensor was calibrated against a Bourdon reference pressure gauge. The calorimeter temperature was calibrated against a K-type thermocouple (Omega, USA) placed in the calorimetric vessel at selected temperatures between 253 K and 293 K. The calibration of the calorimetric detector was carried out by joule effect using a 100ohm resistance settled in the high-pressure vessel. Processes investigated in this calorimeter can be induced either by pressure variations at constant temperature or by temperature variations at constant pressure. Figure 13.4 presents an example of melting of ice at 265.9 K induced by pressure variations at a rate of 16.7 kPa/s (1 MPa/min). Integration of the calorimetric trace gave the value of the

Figure 13.3. High-pressure calorimetric experimental vessel: (1) body of the vessel made from stainless steel, (2) polyethylene pouch containing the investigated substance, (3) nitrile O-ring placed on a plug, (4) threaded plug holding the closing in place.

Figure 13.4. Calorimetric trace of melting of ice at 265.9 K induced by pressure variations at a rate of 16.7 kPa/s (1 MPa/min).

315

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latent heat of fusion equal to the value derived from Bridgman’s highpressure volumetric data (Bridgman 1912) within a 3% agreement, which is a good validation of the instrument correctness. It is worth noting that the Calvet-type calorimeters are also suitable instruments for measurements performed under typical processing conditions of high hydrostatic pressure and temperature of various processes under well-determined pressure and temperature conditions that are important for food science and technology. Such instruments have been used either as a single calorimetric detector (Chourot, LeBail, and Chevalier 2000) or as a differential calorimetric device (Randzio, Grolier, and Quint 1994). In differential mounting, a Setaram C80 calorimeter was used in an upside-down position. This permitted using differential calorimetric vessels fixed on a laboratory table and connected to the high-pressure pump and pressure detectors with rigid stainless steel tubing, allowing performance of direct calorimetric measurements up to 400 MPa. This was the first pressure-controlled scanning calorimeter with linear pressure variations at rates from 0.5 kPa/s (30 kPa/min) to 0.2 MPa/s (12 MPa/min). When performing high-pressure calorimetric measurements, one should realize that the significance of the calorimetric signal depends on the use of the pressure-transmitting fluid. If the pressure is transmitted to the calorimetric vessel through the substance under investigation (liquid, liquid suspension, liquid emulsion, or even a paste), the heat developed as a result of pressure variation is proportional to the coefficient of thermal expansion of the substance under investigation (Randzio 1985). This is because the mass of the substance contained in the calorimetric vessel varies, m = VE/V, where VE is the internal volume of the calorimetric vessel and V is the molar or specific volume of the substance under investigation, the latter one being pressuredependent. This is a kind of open mass vessel (quasi-constant volume), in which the substance under investigation entirely fills the calorimetric vessel and at least a part of the external tubing connecting to the pressure generator. In investigating solid samples, the pressure must be transmitted by a fluid. Thus, the thermal effect developed due to a pressure variation is composed of two contributions. The first is proportional to (dV/dT )p of the solid sample under investigation and the second to the thermal expansion coefficient of the pressure-transmitting fluid. However, an exact separation of the two contributions requires careful treatment of the data (Rodier-Renaud et al. 1996).

High-Pressure Calorimetry and Transitiometry

317

Scanning Transitiometry Figure 13.5 presents schematically a relatively new technique called scanning transitiometry (Randzio 1996). The function of scanning transitiometry consists of scanning one of the three variables ( p, V, or T ) when the second is kept strictly constant. During the scanning, the variations of the dependent variables and the associated calorimetric signal are simultaneously recorded. From these two quantities and the scanned variable, two thermodynamic derivatives, thermal and mechanical, are simultaneously determined for the system under study. Figure 13.6 presents four thermodynamic situations covered by scanning transitiometry, where from the state variables and the heat effect one can determine four pairs of thermodynamic derivatives (Randzio 1997). Each of the situations has specific applications, which prove its particular utilities. In studying food systems, the most useful is the use of temperature as a scanned variable at constant pressure and the use of pressure as a scanned variable at constant temperature. In the former case, the output variables are heat capacity at constant pressure and thermal expansion. After proper integration, one obtains enthalpy and volume variations caused by the applied temperature change. In the latter case, the output variables are pressure derivative of entropy and compressibility. After proper integration, one obtains heat of transition and associated volume change. Sometimes, especially for transitions

volume 1–5 cm3 5 +10–6 –5 +10–3 cm3/s

temperature

pressure

heat flux

223–673K 0.1–5mK/s

COMPUTER

0.1–400MPa 0.001–0.05MPa/s

10–7–10–1W

Figure 13.5. Scheme of basic principles of scanning transitiometry.

318

Calorimetry in Food Processing INPUTS

OUTPUTS

T = const P = f(t)

PVT

Thermal Mechanical

T = const V = f(t)

PVT

Thermal Mechanical

P = const T = f(t)

PVT

Thermal Mechanical

V = const T = f(t)

PVT

Thermal Mechanical

(∂S/∂P)T = –(∂V/∂T)P (∂V/∂P)T (∂S/∂P)T = (∂P/∂T)V (∂V/∂P)T (∂H/∂T)P (∂V/∂T)P (∂U/∂T)V (∂P/∂T)V

Figure 13.6. Thermodynamic scheme of scanning transitiometry.

with a negative slope of the equilibrium line, the use of temperature as scanned variable at constant volume is advantageous. As it is shown in Figure 13.5 during the transitiometric experiment, the experimenter can see all thermodynamic variables of the process under investigation. The screen in Figure 13.5 exhibits an isobaric investigation of thermal gelatinization of a 50% water suspension of wheat starch by scanning temperature at a rate of 2.5 mKs−1 under 90 MPa of pressure. The fundamental advantage of the scanning transitiometry with respect to calorimetry is that the former technique gives simultaneously two contributions to a thermodynamic potential change, thermal and mechanical, thus permitting description of a transition in a single experiment; with calorimetry, such a description requires at least a few measurements performed at various pressures, and it is not as precise. Figure 13.7 presents a schematic diagram of a scanning transitiometer, which was used in investigation of pressure influence on the phase transformation occurring during thermal gelatinization of aqueous wheat starch suspensions (Randzio and Orlowska 2005). It consists of a calorimeter equipped with high-pressure vessels, a pressure-volumetemperature system, and a LabView-based virtual instrument (VI) software. Two calorimetric detectors made from 622 thermocouples each are mounted differentially and connected to a nanovolt amplifier. The calorimetric detectors are placed in a calorimetric metallic block, the temperature of which is directly controlled with an entirely digital feedback loop of 22-bit resolution (∼10−4 K), which is part of the transitiometer software. The calorimetric block is surrounded by a heatingcooling shield. The temperature difference between the block and the

High-Pressure Calorimetry and Transitiometry

319

calorimetric detector upper entries heat.-cool. shield

air

cone plug

TDIFF

T

calorimetric signal

TSECURITY

80W

300W

ampoule temp. control

calorimetric detector investigated substance

computer

spring

data acquisition & process control

high pressure tubing heat insulation calorimetric block dry air flow cooling fluid

pump Pt 100

measuring vessel

step motor control

pressure detector

reference vessel

Figure 13.7. Schematic diagram of a scanning transitiometer.

heating-cooling shield is set as constant (5, 10, 20, or 30 K) and using an additional controller. The temperature measurements, both absolute and differential, are performed with calibrated Pt 100 sensors. The heaters are homogeneously embedded on the outer surfaces of both the calorimetric block and the heating-cooling shield. The whole assembly is placed in a thermal insulation embedded in a stainless steel body and placed on a stand that permits moving the calorimeter up and down over the calorimetric vessels. When performing measurements near 273 K or below, dry air is pumped through the apparatus. The calorimetric vessels are made from 0.8-cm internal diameter 316 SS tubing and are fixed on a mounting table attached to the mobile stand. A flexible ampoule containing the sample is placed in the measuring vessel on the top of a spring, ensuring placement of the sample in the center of the calorimetric detector. Another technique is to use mercury as the hydraulic fluid and place a sample of prepared material directly on the mercury. Mercury offers a great advantage because its compressibility is very low, which is extremely advantageous for measurements of both quantities of volume variations and heat flux. Only the measuring vessel is connected to the PV line. The reference vessel acts only as a thermal reference; a stainless steel bar of appropriate dimensions is placed in it to balance the baseline of the differential

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calorimetric signal. The tubing of both measuring and reference vessels are connected to reducers placed inside the calorimeter when it is in the lowered (measuring) position. The connections from the reducers to the manifold are made with thin stainless steel capillaries to reduce heat losses to the environment. The vessels are closed, with a cone plug fixed in place by an internally threaded cover, which also acts as a heat exchanger between the calorimetric vessel tubing and the calorimetric detector. Two sleeves also are fixed on the calorimetric vessel tubing below the cover to help control the heat exchange between the calorimetric vessel tubing and both the calorimeter block and the shield. The piston pump (9 cm3 of total displaced volume) is driven by a stepping motor controlled by the transitiometer software (manual control is possible during preparatory operations). The pressure detector is a Viatran 245 transducer, 100 MPa full range with a precision of 0.15% full scale deflection (fsd). The pressure detector, the output of the calorimetric amplifier, and the stepping motor are connected to a NI PCI-MIO-16XE-50 multifunction board through a NI SCB-68 shielded connector block. The temperature measurements and digital control of the calorimetric block are performed through a serial port. The software, elaborated with the use of LabView language, performs as a virtual instrument (VI). It consists of 90 subVI, each responsible for a particular function: pressure measurement, temperature measurement, counting the motor steps for recording the volume variations, measuring the calorimetric signal, etc., and each performs independently. However, all the subVIs form a hierarchical structure with a top window, where the experimenter can see simultaneously all four variables (pressure, P, volume variations, V, temperature, T, and heat flux, q) associated with the process under investigation and the current status of the temperature and pressure control loops. The experiments are performed by starting thermal and mechanical stabilization for at least 5000 s, then the temperature scanning starts, which is accompanied by automatic volume compensation to keep the pressure constant. At the end of the scan, the temperature is kept constant for at least 5000 s. Any static baseline shift of the calorimetric signal between the low and high temperature stabilizations is corrected. No corrections are made for the calorimetric signal recorded during the temperature scan.

High-Pressure Calorimetry and Transitiometry

321

Figure 13.8. Transitiometric vessels: (a) standard high-pressure vessels; (b) the vessels kept in a holder to facilitate reproducible closing and opening of the vessel by a dynamometric wrench; (c) a sample of a gelatinized sample of 50% aqueous suspension of wheat starch pushed out after a transitiometric isobaric experiment under 90 MPa.

The method presented here is rather simple and safe in practice. The total volume of the liquid phase under pressure is only about 20 ml; the energy accumulated in it is rather small and not dangerous. The mercury used as a hydraulic fluid is always contained in a closed space. In case of a leak, the mercury is collected on a special protecting plate. The calorimetric vessels are conveniently and reproducibly closed and opened with a torque wrench with the vessels placed in a specially designed holder. Figure 13.8a–c presents a view of transitiometric vessels: standard high-pressure vessels, the vessels kept in a holder to facilitate reproducible closing and opening of the vessel by a dynamometric wrench, and a sample of a gelatinized sample of a 50% aqueous suspension of wheat starch pushed out after a transitiometric experiment. Because of the high sensitivity of the instrument, some precautions must be taken to ensure valid measurements. In the case of the calorimetric signal, the main precaution is to carefully compensate the thermal balance of the differential calorimetric vessels. It is also important to keep the initial mercury level always in the same position, just above the entry to the calorimetric detector zone. Adjustment of the mercury level is easily done with the motorized pump. With respect to the volumetric component, it is very important that displacement

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of the mercury during calibration experiments be sufficiently slow to avoid overpressure in the flow lines, and differentiation of the piston displacement must be carefully done to avoid excessive noise on the one hand and excessive damping on the other. The temperature and energy scales of the differential calorimetric detector were calibrated under atmospheric pressure with the fusion of gallium, Tm = 302.91 K, ΔHm = 5.59 kJ mol−1; p-bromochloro-benzene, Tm = 337.73 K, ΔHm = 18.760 kJ mol−1; p-di-bromobenzene, Tm = 360.45 K, ΔHm = 20.530 kJ mol−1; benzoic acid, Tm = 395.55 K, ΔHm = 18.062 kJ mol−1; and indium, Tm = 429.75 K, ΔHm = 3.28 kJ mol−1. The calibration experiments were done by enclosing a calibration substance in a 75-mm-long thin glass tube placed in the center of the calorimetric vessel. In order for the internal heat exchange to resemble that occurring in the real experiment, but to avoid thermal effects of gelatinization, the remaining inner space of the calorimetric vessel was filled with dried starch. The precision of the temperature scale is ±0.2 K. The energetic calibration constant kc of the calorimetric detector depends on temperature and is described by Equation 13.1: kc (WV −1 ) = 3.423 × 10 −3 + 9.993 × 10 −6 T K

(13.1)

The mean deviation between Equation 13.1 and the calibration data is 1.4%. The reproducible resolution of the calorimetric detector varies from 1.3 × 10−7 W at 303 K to 1.6 × 10−7 W at 430 K. As reported previously, for properly designed experimental vessels, the energetic calibration constant of the calorimetric detector does not depend on pressure (Randzio, Grolier, and Quint 1994). The volumetric calibration of the high pressure pump was performed by weighing 11 mercury samples displaced by known numbers of motor steps. Each motor step corresponded to a displacement of (5.22 ± 0.03) × 10−6 cm3. The volumetric calibration of the high pressure pump and both energetic and temperature calibrations of the calorimetric detector were verified by test measurements using isobaric fusion of benzene, for which both enthalpy and volume of the transition are exactly known (Bridgman 1914b). Figure 13.9 presents an example of such measurements performed at a scanning rate of 2.5 mK s−1 at 100 MPa. The mean results from eight independent measurements gave the following: ΔfusV(100 MPa) = 0.1038 ± 0.0028 cm3g−1 [the respective literature

High-Pressure Calorimetry and Transitiometry

323

120 –10 Endo

a

100 80

–50

60

–70

40

–90 –110 –130 299

20

b 301

303 305 Temperature (K)

dV/dT (mm3 K–1 g–1)

Heat flux (mW·g–1)

–30

307

0 309

Figure 13.9. Example of simultaneous transitiometric traces (heat flux and volume variations) of isobaric melting at 100 MPa of 0.3858 g of benzene used as a verification test for thermal and volumetric calibrations of the transitiometer used in the present study: (a) heat flux, (b) dV/dT.

value is ΔfusV(100 MPa) = 0.1026 cm3g−1]; ΔfusH(100 MPa) = 131.1 ± 2.1 J g−1 [the respective literature value is ΔfusH(100 MPa) = 126.3 Jg−1]; Tfus,onset(100 MPa) = 304.2 ± 0.3 K and Tfus,peak(100 MPa) = 305.7 ± 0.5 K [the literature value is Tfus(100 MPa) = 305.6 K given without any specification]. The agreement with volumetric data is very good. Small differences with the thermal data probably are caused by internal heat exchange conditions in the calorimetric vessel. In test measurements, only 0.4 g of benzene was floating on the mercury, whereas in the calibration experiments, the calibration substances were placed in the center of the calorimetric vessel and were surrounded by a dry starch powder. Figure 13.10 presents results of another test performed on the temperature and pressure dependence of the thermal expansion of the mercury used as hydraulic liquid. The hydraulic liquid was displaced to the top of the empty calorimetric vessel and temperature-scanning measurements performed at a rate of 2.5 mK s−1 at various pressures. The thermal expansion of the hydraulic fluid is almost constant at a given pressure and only very slightly decreases with temperature. The mean values are as follows: 0.949 ± 0.033 mm3 K−1 at 10 MPa, 0.895 ± 0.038 mm3 K−1 at 60 MPa, and 0.888 ± 0.050 mm3 K−1 at 100 MPa. This test was important for analysis of transitions observed

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Figure 13.10. Volume variations during isobaric temperature scans at various pressures with only hydraulic liquid (mercury) present in the system. The data at 10 MPa and 100 MPa are shifted by +0.3 mm3K−1 and −0.3 mm3K−1, respectively, to avoid overlapping.

in food systems as a function of temperature under isobaric conditions, which are discussed later. Applications Water in Pork Muscle High pressure has a dramatic effect on the phase transition behavior of water, encompassing several forms of ice crystals, depending on the pressure and temperature (Bridgman 1912). This phase transition phenomenon offers several potential applications in food processing, such as pressure-shift freezing, high-pressure freezing, high-pressure thawing, etc. (Cheftel, Thiebaud, and Dumay 2002; Kalichevsky, Knorr, and Lillford 1995). Water is a major component of most foods, especially fresh products (e.g., meat, fish, vegetables, fruits), and properties of such foods are strongly related to the properties and content of water. Such foods containing water are, with respect to phase transition phenomena, somewhat similar to water-ice phase transitions when subjected to high-pressure and low-temperature processing (Le Bail et al. 2003). Water in foods can exist either in a free and thus freezable state or in a bound and thus nonfreezable state. The content of freezable water

High-Pressure Calorimetry and Transitiometry

325

traditionally has been considered to depend on freezing temperature (Pham 1987). The presence of solutes in the aqueous phase shifts the phase diagram of water, lowering the freezing point or ice-melting temperature at atmospheric pressure. This effect is enhanced with increasing solute concentrations (Fennema 1973; Cheftel, Levy, and Dumay 2000). Therefore, phase transition processing of water in foods during high-pressure treatment is more complex than that of pure water. In this respect, an example of high-pressure calorimetric applications in the investigation of real foods is an evaluation of the phasetransition behavior of water in pork muscle (Zhu, Ramaswamy, and Le Bail 2004), described below. Small samples of fresh pork muscle specimen (0.62–0.72 g) were prepared and vacuum-packaged in polyethylene bags (80-μm-thick multiplayer film). While awaiting calorimetric experiments, the packaged samples were stored at 277 K. To make measurements, the investigated sample was placed in the calorimetric vessel and either isothermal pressure scanning or isobaric temperature scanning was performed. After calorimetric experiments, moisture content in each investigated sample was determined by drying in an oven at 376 K for 24 h. For isothermal pressure-scanning measurements, the calorimetric temperature was set at a selected value (268, 263, 258, and 253 K). Once the calorimetric signal showed a stable baseline, the pressure was increased linearly at a rate of 5 kPa/s (0.3 MPa/min), and the heat flow was recorded every 5 s. When the pressure reached the corresponding phase-change temperature, the frozen sample started to melt, resulting in a peak of heat flow. Figure 13.11 presents a comparison of calorimetric heat flow signals of thawing of pure ice and of ice in a frozen pork muscle at 263 K induced by a linear pressure scans at a rate of 5 kPa/s (0.3 MPa/min). Figure 13.12 presents similar isothermal heat flow signals of pressure-induced thawing of ice in frozen pork muscle recorded at various temperatures. It is worth noting that this is a heat flow calorimetric detector; the temperature increase is extremely small, thus it ensures quasi-isothermal conditions for the measurements performed. The temperature-scanning measurements were carried out at various constant pressures by increasing (for thawing) or decreasing (for freezing) the temperature at a prescribed rate. Figure 13.13 presents results of a typical temperature-scanning measurement of freezing of ice in a pork muscle performed at a rate of 2.5 mKs−1 (0.15 K/min) under pres-

326

Calorimetry in Food Processing 0

Heat flux (mW/g)

–20 –40 –60 –80

Pork

–100 0.1

Water

50

100 Pressure (MPa)

150

200

Figure 13.11. Comparison of thawing heat flux of pure ice and frozen pork muscle induced by pressure scans at a rate of 5 kPa/s (0.3 MPa/min) at 263.1 K.

Heat flux (mW/g)

0

–10

–20

–30 –268.0K –40 0.1

25

50

–263.1K –258.0K –253.1K 75

100 125 150 Pressure (MPa)

175

200

250

Figure 13.12. Thawing heat flux of frozen pork muscles induced by a pressure scan at a rate of 5 kPa/s (0.3 MPa/min) at various temperatures.

sure of 111.7 MPa. At the transition, the temperature scan was perturbed because of a rapid release of heat during fast crystallization of ice. Frozen Water Ratio in Gelatine Gels The same experimental procedure as that described above for pure water and water in pork muscle has also been used with gelatine gels containing 2% and 10% of dry gelatine (Chevalier-Lucia et al. 2003).

High-Pressure Calorimetry and Transitiometry 2.5

327

259

258

1.5 Temperature

1.0 0.5

257

Temperature (K)

Heat flux (mV)

2.0

Heat flux 0.0 –0.5

256 0

1200

3600 Time (s)

6000

Figure 13.13. Heat flux of crystallization of water in pork muscle induced by cooling under pressure of 111.7 MPa.

Dried powdered gelatine (Merck, Darmstadt, Germany) was dissolved in distilled water and placed in an hermetic flask. The solution maintained at 293 K was mixed for 1 h at 100 rpm with a magnetic stirrer. The mixture was then heated for 30 min at 323 K at the same stirring rate. The formed gelatine gel was then stored for 12 h at 277 K to allow maturation. Three samples of 5 g of gel were dried at 375 K for 24 h to check the final water content in the gels before the calorimetric measurements. The pressure-controlled scanning calorimetric measurements were then performed with gels containing 2% and 10% of dry matter at three temperatures: 268, 263, and 258 K. For each temperature, the onset pressure, the peak pressure, and the latent heat were measured, always for three different samples. It was observed that whatever the concentration in dry matter, the latent heat of gelatine gels decreased with the melting temperature as observed previously for pure water. As illustrated in Figure 13.14, whatever the concentration in dry matter, the latent heat of the gelatine gel decreased with the melting temperature as observed for water. The latent heat versus temperature evolution was fitted by a second-order polynomial expression given by Equations 13.2 and 13.3 for 2% and 10% gelatine gels, respectively. L2% = 0.0921⋅T 2 + 6.668 ⋅T + 318.8 R 2 = 0.991

(13.2)

L10% = 0.0884 ⋅T 2 + 8.922 ⋅T + 296.9 R 2 = 0.999

(13.3)

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Calorimetry in Food Processing

Latent heat (J/g)

400

300

200

100 253

258

263

268

273

Temperature (K)

Figure 13.14. Evolution of the latent heat of pure water (square, experimental data; dash, Bridgman’s data) and of gelatine gels (circle, 2%; diamond, 10% in dry matter) according to the melting temperatures.

Figure 13.15. Evolution of the ratio of latent heat of melting gelatine gels and latent heat of melting water as a function of melting temperature (or respective pressure): circle, 2% gelatine gels; diamond, 10% gelatine gels.

The dry matter content appeared to have an influence on the latent heat under pressure. The higher the dry matter, the lower was the latent heat. This phenomenon observed under atmospheric pressure was also valid under high pressures. Figure 13.15 presents the ratio of the latent heat observed for gelatine gels and that of pure water. Under atmospheric pressure (273 K), this ratio for the 2% and 10% gelatine gels was 0.98 and 0.9, respectively. The difference between these results and the water content of the gels is probably due to the bound water fraction. The bound water fraction, which does not freeze, corresponds to some water molecules fixed on polar groups of components such as proteins. It can be seen in Figure 13.15 that when the melting temperature decreases (the phase-change pressure increases), this ratio

High-Pressure Calorimetry and Transitiometry

329

decreases. Thus, it can be concluded that the amount of the bound water in gelatine gels increases with pressure. Pressure Shift Freezing Pressure shift freezing (PSF) is increasingly receiving attention in recent years because of its potential benefits for improving the quality of frozen food. Generally, the PSF process consists of three successive steps: (1) cooling the product under pressure to a low temperature (e.g., 253 K at 200 MPa) without involving phase change; (2) a quick depressurization (adiabatic expansion) to create supercooling for instantaneous, uniform, and partial initiation of the freezing process (resulting largely in ice nucleation); and (3) completion of the freezing process (ice crystal growth) under atmospheric pressure. It has been demonstrated that the PSF process produces fine and uniform ice crystals throughout the food samples (Chevalier, Le Bail, and Ghoul 2002), thus reducing ice crystal-related textural damage to frozen products (Chevalier et al. 2001). In the PSF process, after the pressure release, a portion of the liquid water is frozen, and the resulting crystals are usually very small in size (like ice nuclei in conventional freezing) and will then grow when the freezing is completed under atmospheric pressure. Evaluation of the amount of ice nuclei formed instantaneously by depressurization is important for a better understanding of PSF process. The high-pressure calorimetry can be very helpful in this respect (Zhu, Ramaswamy, and Le Bail 2005). Figures 13.16 and 13.17 Pressurization

A

Nucleation

Supercooling

Water

Cooling

E

F Cooling

Temperature (K)

273

B

Ice-I C

G D

Depressurization

252 0.1

Pnuc

210 Pressure (MPa)

Figure 13.16. Basic procedure of pressure-shift freezing based on phase transition between water and ice I under pressure; Pnuc is the pressure under which ice nucleation starts.

330

Calorimetry in Food Processing Temperature

300

253.1

C

200 Heat flux 100 0

252.1

A

B

0 Pressure

–100 0

Temperature (K)

Heat flux (mW/g) or Pressure (MPa)

400

1200

2400 Time (s)

3600

251.1 4800

Figure 13.17. Typical profiles of pressure, heat flux, and temperature during pressure-shift freezing of pork muscle under pressure of 199 MPa at 253.1 K.

present procedures of a PSF experiment performed in a high-pressure calorimeter using pork muscle under pressure of 199 MPa at 253.1 K. After placing the sample into the experimental vessel, the calorimeter was rapidly pressurized to target level (AB in Figure 13.16). Then the sample was cooled to the preset temperature under constant pressure (BC in Figure 13.16). When the baseline of the calorimetric signal became stable (AB in Figure 13.17), pressure was released to initiate the nucleation process (CDEF in Figure 13.15 and BC in Figure 13.17). Because the depressurization was carried out rapidly (within a matter of 1 or 2 s), water was instantaneously supercooled in the liquid state (a metastable one), even after the complete release of pressure (i.e., Pnuc = 0.1 MPa and EF in Figure 13.16), and then ice nucleation occurred. After ice nucleation, sample temperature increased to the freezing point (EF in Figure 13.16) due to the latent heat of crystallization. Finally, the sample was allowed to complete freezing under atmospheric pressure (FG in Figure 13.16 and CD in Figure 13.16). Figure 13.18 shows the ratio of ice crystal to the whole mass of the sample formed during depressurization of the pork, determined from the highpressure calorimetric results described above. Gelatinization of Starch Starch is one of the most important natural macromolecules. Its importance stems from the fact that the starch granule is an almost universal

High-Pressure Calorimetry and Transitiometry 293

Temperature (K)

40 Ice ratio and regression curve 273

30 20

253

10

50

100 150 Pressure (MPa)

200

0 250

Ratio of ice to sample mass (%)

50 Phase-change curve

0.1

331

Figure 13.18. Ratios of ice crystal to sample mass instantaneously formed after depressurization during pressure-shift freezing of pork muscle (74.2% moisture content) at various initial pressures, with temperatures slightly higher than the corresponding phase-change points of water.

form for packaging and storing carbohydrate in green plants. It is also one of the main components of food materials, especially those submitted to elevated pressure extruder processing. The process of preparing a homogeneous sol phase from a mixture of native starch and water is called gelatinization. Starch gelatinization is a combined process consisting of hydration of amorphous regions and subsequent melting of crystalline arrays. It was demonstrated recently (Hayert et al. 2003) that all the transformations occurring during starch gelatinization can be observed with a high-sensitivity DSC done at a low rate of temperature scanning under atmospheric pressure. Typical results are shown in Figure 13.19 for pastes or emulsions of wheat starch with various total water contents. The main endothermic transition occurring from 319 K to 333 K independently of the water content is likely associated with melting of the crystalline part of the starch granules followed by a helix-coil transformation in amylopectin, the main component of starch. This endothermic transition is followed by a water-dependent, slow, exothermic transformation, which is probably related to reassociation of the unwound helices of amylopectin with parts of amylopectin molecules other than their original helix-duplex partner, forming physical junctions and creating more general hydrogen-bonded associations. The high-temperature endothermic transition occurring at water contents around 50 wt % and

332

Calorimetry in Food Processing –26

Endo

46.8 Heat flux difference (mW/g)

–28 –30

56.0

–32

60.3

–34

64.8

A

M

N

–36 –38 –40 300

310

320

330

340

350

360

370

380

Temperature (K)

Figure 13.19. DSC traces obtained at atmospheric pressure at a rate of 16.67 mK s−1 for aqueous native wheat starch emulsions at selected concentrations of water (total water contents in wt %). M, main endothermic transition, occurring from 319 K to 333 K independently of the water content; A, water-dependent, slow, exothermic transformation; N, high-temperature endothermic transition occurring at water contents around 50 wt % and higher.

higher is associated with destruction of amylose-lipid complexes or with a nematic-isotropic transition, which ends the formation of the isotropic colloidal SOL phase. The pressure influence on those transitions has been investigated under the process conditions of pressure and temperature, using a scanning transtitiometer described above. A mixture of native wheat starch with 50 wt % of added water (56.0 wt % total water content) has been selected for such transitiometric highpressure studies (Randzio and Orlowska 2005). Figure 13.20 presents results obtained in isobaric experiments by scanning temperature at a low rate of 2.5 mK s−1 (0.15 K min−1) under pressures of 10, 60, and 100 MPa. Results at each pressure present two output signals recorded simultaneously as a function of temperature, the heat flux and dV/dT (thermal expansion), both quantities expressed per gram of dry starch. The most important observation is that all the transitions recorded previously under atmospheric pressure at a temperature-scanning rate of 16.7 mK s−1 (1 K min−1) with a highsensitivity DSC (see the respective trace at 56.0 wt % of total water

Figure 13.20. Transitiometric traces (heat flux and dV/dT per gram of dry starch) obtained simultaneously and under the process conditions of pressure and temperature by scanning temperature at a rate of 2.5 mK s−1 at various pressures for a starch-water emulsion (56.0 wt % total water content). A, exothermic transformation; N, endothermic transition.

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content in Figure 13.19) also are observed in the transitiometric traces in Figure 13.20 performed under elevated pressures at a much lower temperature-scanning rate of 2.5 mK s−1 (0.15 K min−1). The transitiometric method also measures simultaneously the volume changes at those transitions. In Figure 13.19, the right ordinate presents the dV/ dT of the sample; the dV/dT from the hydraulic liquid (see Figure 13.10) have been subtracted from the experimental data. The dV/dT of the sample at the main endothermic transition (M) decreases over the pressure range under investigation (10–100 MPa). Also note that the changes of dV/dT at the particular transitions are rather small, while the general tendency is for dV/dT to rise considerably with temperature over the whole temperature range. The last phenomenon is associated with swelling of starch granules during gelatinization, even under elevated pressures. This allows a more detailed analysis of the main transition (M). For several degrees prior to and after the transition, dV/dT increases linearly with temperature with the same or very similar slope. Assuming this, dV/dT during the transition can be divided into two contributions, one due to the assumed linear swelling and one due to the phase transition itself. Figure 13.21 presents an example of such a division of the results obtained at 10 MPa. Once the division is made, the two contributions can be integrated separately to give volume changes occurring in the transition, a positive change for the swelling and a negative change for the transition itself.

Figure 13.21. Division of dV/dT for the main endothermic transition into a positive dV/dT due to swelling and a negative dV/dT due to the transition itself.

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Integrated volumetric data, together with thermal data, all obtained for each pressure from at least four independent experiments and performed each time on a freshly prepared sample, are given in Table 13.1. The errors are the standard deviations from the mean values of all experiments performed at each pressure. Table 13.1 also contains data at 0.1 MPa obtained previously (Randzio, Flis-Kabulska, and Grolier 2002) with a DSC. From linear approximations of pressure dependence of the parameters presented in Table 13.1, the following slopes could be obtained: dHtrans/dp = −(9.85 ± 2.25) mJ MPa−1 g−1, dVtrans/dp = 2.27 ± 0.37 10−3 mm3 g−1 MPa−1, dVswelling/dp = −(9.28 ± 2.05) 10−3 mm3 g−1 MPa−1 and dTtrans/dp = −(24.6 ± 6.9) mK MPa−1. Assuming the main transition (M) is an equilibrium first-order transition, the last slope also can be obtained from the Clapeyron equation and data from Table 13.1, from 10 MPa to 100 MPa (dTtrans/dp)Clapeyron = −(78.3 ± 2.5) mK MPa−1. Although the slopes are both negative, the agreement is poor, implying that the mechanism of the transition is more complicated than the first-order transition assumed by the Clapeyron equation. Also, the pressure dependence may not be linear, especially at low pressure. Future studies will focus on that problem. Despite a large number of pressure studies on starch gelatinization, only the results of Rubens and Heremans (Rubens and Heremans 2000) were obtained from under the process conditions of pressure and temperature studies and are in agreement with the present results. In Figure Table 13.1. Thermodynamic data for the main transition (M) in an aqueous emulsion of wheat starch (56 wt % total water) expressed per gram of completely dry starch. Pressure (MPa) Quantity ΔtransH (Jg−1) ΔtransV (mm3g−1) ΔswellingV (mm3g−1) Ttrans,onset (K)

0.1

10

60

100

3.52 ± 0.07

3.12 ± 0.12

2.65 ± 0.07

2.45 ± 0. 7

—

−0.788 ± 0.038

−0.647 ± 0.040

−0.586 ± 0.035

—

1.53 ± 0.10

1.22 ± 0.05

0.683 ± 0.036

320.5 ± 0.5

319.5 ± 0.5

318.1 ± 1.0

317.9 ± 0.6

Source: Data at 0.1 MPa are from Randzio et al. (Randzio, Flis-Kabulska, and Grolier 2002).

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4 of their study, performed using infrared spectroscopy and a diamond anvil cell, Rubens and Heremans (Rubens and Heremans 2000) show that the temperature of the transition is lowered by pressure increase. In opposition to the pressure effects on the main endothermic transition (M), the pressure influence on both the exothermic transformation (A) and the high-temperature endothermic transition (N) is positive. At 10 MPa, the exothermic transformation starts at 348.6 ± 0.6 K and is shifted by pressure to higher values at a mean rate of 38.9 ± 9.9 mK MPa−1. Also at 10 MPa, the high-temperature endothermic transition starts at 382.7 ± 0.2 K and is shifted by pressure to higher values at a mean rate of 96.1 ± 3.4 mK MPa−1. These observations are in agreement with a general thermodynamic approach to these transitions. In Figure 13.20, in transition (A), the exothermic effect is always associated with a decrease of thermal expansion, which is most probably caused by a negative volume change at that transition, similar to the observation made on the main transition (M). In transition (N), the endothermic effect is associated with a small increase of thermal expansion, which is most probably caused by a positive volume change at that transformation. Thus, the Clapeyron equation in both cases also would give positive dT/dP slopes. The uncertainty limits given for the above results contain both a purely instrumental contribution, 1–5%, and a contribution from the preparation of the emulsion, which can be several percent. Phase Stability of Systems Containing Lipids Lipids are components of various food products, especially oils. The content and the nature of lipids influence the phase stability of such products. Figure 13.22 presents a pressure-temperature phase diagram (a) and pressure dependence of latent heat of fusion (b) for cocoa butter, palm oil, copra oil, and for comparison, water. All the data were obtained from high-pressure calorimetric measurements (Hayert et al. 2003). It can be seen that the cocoa butter and copra oil, which do have almost no high unsaturated fatty acids, solidify at rather low pressures below 60 MPa. In contrast, the palm oil, which contains more high unsaturated fatty acids, solidifies under much higher pressure of 122 MPa. All the transitions in the investigated systems containing lipids have positive slopes and their latent heats do not depend on pressure, which can suggest that the volume variations of those transi-

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Figure 13.22. (a) PT phase diagram of lipids (cocoa butter, palm oil, copra oil) and water determined from high-pressure calorimetric measurements. (b) Pressure dependence of latent heat of fusion of lipids (cocoa butter, palm oil, copra oil) and water determined from high-pressure calorimetric measurements.

tions are positive. These features are in opposition to the respective properties of water, which should be taken into consideration in highpressure processing of food products containing those components. Conclusions Presented in this chapter are a review and a description of highpressure calorimetric and transitiometric techniques, which allow

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investigation under typical processing conditions of high hydrostatic pressure and temperature of various transitions and processes occurring in model and real food systems under well-determined pressure and temperature conditions. The thermodynamic and thermomechanic parameters of the transitions under investigation determined with the described techniques permit understanding of the effects of high-pressure processing on the physical properties of food materials and thus should allow prediction of the formulation of raw materials and processing conditions so as to achieve desired end-product properties. A detailed description of selected applications of the presented techniques in the analysis of high-pressure processing of selected systems important for food science and technology should also stimulate further development of such applications in other food systems.

References Bridgman, P.W. 1912. Water in the liquid and five solid forms under pressure. Proc Am Acad Arts Sci, 47:439. Bridgman, P.W. 1914a. The coagulation of albumin by pressure. J Biol Chem, 19: 511. Bridgman, P.W. 1914b. Change of phase under pressure. I. Phase diagrams of eleven substances. Phys Rev, 3:153. Cheftel, J.C., Levy, J., and Dumay, E. 2000. Pressure-assisted freezing and thawing: Principles and potential applications. Food Rev Int, 16:453. Cheftel, J.C., Thiebaud, M., and Dumay, E. 2002. Pressure assisted freezing and thawing: A review of recent studies. High Press Res, 22:601. Chevalier, D., Le Bail, A., and Ghoul, M. 2002. Freezing and ice crystals formed in cylindrical model food. Part II. Comparison between freezing at atmospheric pressure and pressure shift freezing. J Food Eng, 46:287. Chevalier, D., Sequeira-Munoz, A., Le Bail, A., Simpson, B.K., and Ghoul, M. 2001. Effect of freezing conditions and storage of ice crystals and drip volume in turbot (Scophthalmus maximus), evaluation of pressure shift freezing vs. air-blast freezing. Innov Food Sci Emerg Technol, 1:193. Chevalier-Lucia, D., Le Bail, A., Ghoul, M., and Chourot, J.M. 2003. High pressure calorimetry at sub-zero temperature: Evaluation of the latent heat and frozen water ratio of gelatine gels. Innov Food Sci Emerg Technol, 4:361. Chourot, J.M., LeBail, A., and Chevalier, D. 2000. Phase diagram of aqueous solution at high pressure and low temperature. High Press Res, 19:191. Douzals, J.P., Perrier-Cornet, J.M., Coquille, J.C., and Gervais, P. 2001. Pressuretemperature phase transition diagram for wheat starch. J Agric Food Chem, 49: 873.

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Fennema, O.R. 1973. Nature of freezing process. In: Low Temperature Preservation of Foods and Living Matter, O.R. Fennema, W.D. Powrie, and E.H. Marth, editors, pp. 151–222. Marcel Dekker: New York. Hayashi, R. 2002. High pressure in bioscience and biotechnology: Pure science encompassed in pursuit of value. Biochim Biophys Acta, 1595:397. Hayert, M., Le Bail, A., Rigenbach, M.H., and Gruss, E. 2003. High pressure calorimetry as a tool to monitor phase transitions in foods: Application to water and selected lipids. In: Advances in High Pressure Bioscience and Biotechnology II, R. Winter, editor. Springer Verlag: London. Kaletunç, G. and Breslauer, K.J. 1996. Construction of wheat-flower state diagram. J Therm Anal, 47:1267. Kalichevsky, M.T., Knorr, D., and Lillford, P.J. 1995. Potential applications of highpressure effects on ice-water transitions. Trends Food Sci Tech, 6:253. Knorr, D. 1999. Process assessment of high-pressure processing of foods: An overview. In: Processing Foods: Quality Optimization and Process Assessment, F.A.R. Oliveira and J.C. Oliveira, editors, p. 249. CRC Press: Boca Ration, FL. Le Bail, A., Boillereaux, L., Davenel, A., Hayert, M., Lucs, T., and Monteau, J.Y. 2003. Phase transition in foods: Effect of pressure and methods to asses or control phase transition. Innov Food Sci Emerg Technol, 4:15. Le Bail, A., Chevalier, D., Chourot, J.M., and Monteau, J.Y. 2001. High pressure calorimetry. Comparison of two systems (differential vs. single cell). Application to the phase change of water under pressure. J Therm Anal Calorim, 66:243. Pham, Q.T. 1987. Calculation of bound water in frozen food. J Food Sci, 52:210. Randzio, S.L. 1985. Scanning calorimeters controlled by an independent thermodynamic variable: Definitions and some metrological problems. Thermochim Acta, 89:215. Randzio, S.L. 1996. Scanning transitiometry. Chem Soc Rev, 25:383. Retrieved from: http://www.transitiometry.com Randzio, S.L. 1997. State variables in calorimetric investigations: Experimental results and their theoretical impact. Thermochim Acta, 300:29. Randzio, S.L. 1998. Recent developments in calorimetry. Ann Rep Prog Chem, Sect C, 94:433. Randzio, S.L. 2002. Recent developments in calorimetry. Ann Rep Prog Chem, Sect C, 98:157. Randzio, S.L., Flis-Kabulska, I., and Grolier, J.P.E. 2002. Re-examination of phase transitions in the starch-water system. Macromolecules, 35:8852. Randzio, S.L., Grolier, J.P.E., and Quint, J.R. 1994. An isothermal scanning calorimeter controlled by linear pressure variations from 0.1 to 400 MPa. Calibration and comparison with the piezothermal technique. Rev Sci Instrum, 65:960. Randzio, S.L. and Orlowska, M. 2005. Simultaneous and under the process conditions of pressure and temperature analysis of thermal and volumetric properties of starch gelatinization over wide pressure and temperature ranges. Biomacromolecules, 6:3045. Rodier-Renaud, L., Randzio, S.L., Grolier, J.P.E., Quint, J.R., and Jarrin, J. 1996. Isobaric thermal expansivities of polyethylenes with various crystallinities over

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the pressure range from 0.1 MPa to 300 MPa and over the temperature range from 303 K to 393 K. J Polym Sci Pol Phys Ed, 34:1229. Rubens, P. and Heremans, K. 2000. Pressure-temperature gelatinization phase diagram of starch: An under the process conditions of pressure and temperature Fourier transform infrared study. Biopolymers, 54:524. Smeller, L. 2002. Pressure-temperature phase diagrams of biomolecules. Biochim Biophys Acta, 1595:11. Stute, R., Heilbronn, R.W., Boguslawski, S., Eshtagi, M.N., and Knorr, D. 1996. Effects of high pressure treatment on starches. Starch/Stärke, 48:399. Zhu, S., Bulut, S., Le Bail, A., and Ramaswamy, H.S. 2004. High-pressure differential scanning calorimetry (DSC): Equipment and technique validation using water-ice phase-transition data. J Food Proc Eng, 27:359. Zhu, S., Ramaswamy, H.S., Le Bail, A. 2004. High-pressure differential scanning calorimetry: Evaluation of phase transitions in pork muscle at high pressures. J Food Proc Eng, 27:377. Zhu, S., Ramaswamy, H.S., and Le Bail, A. 2005. High-pressure calorimetric evaluation of ice crystal ratio formed by rapid depressurization during pressure-shift freezing of water and pork muscle. Food Res Int, 38:193.

Chapter 14 Calorimetric Analysis of Starch Gelatinization by High-Pressure Processing Kelley Lowe and Gönül Kaletunç

Introduction Gelatinization of Starch by Heat Gelatinization of Starch by High Hydrostatic Pressure High-Pressure Processing of Wheat Starch Suspensions Storage of Gelatinized Starch Conclusions References

341 342 344 344 347 348 349

Introduction Starches are used in many food products to increase viscosity or to form gels. Because starch is insoluble in water, a mixture of starch with water forms a suspension. Starch granules in suspension swell with heat, and the viscosity of the suspension increases, depending on starch concentration. Thermal processing changes the physicochemical properties of starch, such as increased water solubility and development of viscoelastic behavior (Fennema 1996; Jobling, 2004). Because starch affects the texture of food products, characterization of aqueous starch suspension behavior and its interaction with other food additives under conditions relevant to food processing and storage is important for assessment of food stability. This chapter provides a review of starch characterization studies by calorimetry relevant 341

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to thermal processing, high hydrostatic pressure (HHP) processing, and retrogradation of processed starch during storage.

Gelatinization of Starch by Heat Aqueous starch suspension undergoes a phase transition between 60 °C and 70 °C. During the phase transition, starch granules swell and optical properties of starch granules, such as light polarization or iodine coloration, change. Starch gelatinization is a water-assisted melting process that exhibits an endothermic transition and is monitored by differential scanning calorimetry (DSC). Figure 14.1 shows the gelatinization endotherms of 15% (w/w) for wheat and corn starches heated in a DSC. The peaks observed are characterized by two parameters, namely, the peak temperature, thermal stability of the starch phase; and the peak area under the curve, enthalpy of gelatinization (ΔH). It is apparent that the gelatinization temperature of corn starch is higher than that of wheat starch in terms of onset (66 °C vs. 58 °C) and peak temperatures (70 °C vs. 64 °C) of the gelatinization endotherm. Corn starch also requires a larger heat energy for gelatinization per gram of dry starch (11.2 Jgds−1 vs. 16.1 Jgds−1). Similar results are reported in the literature for starches of different origins (Roos 1995). Because thermally induced gelatinization requires higher temperatures and exhibits larger enthalpy change for corn starch than wheat starch, corn starch is considered to have a more thermally stable crystalline structure. Starch exists in foods together with other ingredients. Gelatinization characteristics of starch are also affected by the presence of other food ingredients, such as sugars and proteins (Figure 14.2). Curve C in Figure 14.2 is a thermogram of a mixture of starch, sugar, and milk protein, which is a representative composition of milk pudding. Figure 14.2 shows that when the total dry matter is kept constant, replacing some of the starch with sugar only (curve B) and sugar and proteins (curve C) shifts the thermal stability of the corresponding mixture to higher temperatures up to approximately 5 °C in comparison with the thermal stability of the starch-water mixture (curve A). Similarly, the heat energy required for gelatinization changes with the composition of the mixture. Therefore, the parameters obtained by calorimetry are directly applicable to processing protocols and should be taken into account when process conditions are selected.

–0.32 65.72˚C 2.416J/g

Heat Flow (W/g)

–0.34

–0.36 70.33˚C

58.30˚C 1.673J/g

–0.38

–0.40 63.63˚C –0.42 45 Exo Up

50

55

60

65

70

75 80 85 90 Universal V2.6D TA Instruments Temperature (˚C)

Figure 14.1. DSC thermograms of 15% (w/w) native corn and wheat starch suspensions. Corn starch (solid line) and wheat starch (dashed line).

A

B

C

30

40

50

60

70

80 90 100 Universal V2.6D TA Instruments

Temperature (˚C)

Figure 14.2. DSC thermograms of wheat starch (A), wheat starch-sugar (B), and wheat starch-sugar-milk protein (C). Total solids: 30% for all cases.

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Gelatinization of Starch by High Hydrostatic Pressure HHP has been shown to affect high-molecular-weight polymers causing denaturation of proteins (Messens et al. 1997; Famelart et al. 1998) and gelatinization of starch (Douzals et al. 1996, 1998, 2001; Zuo et al. 1999). Ezaki and Hayashi (1992), based on a study including 20 starches, reported that A-type starches (cereals) are more susceptible to high pressure than B-type starches (tubers), and C-type starches (pea, tapioca) show intermediate behavior. A similar study conducted by Stute et al. (1996) on two starches of various crystalline structures stated that A- and C-type starches are susceptible to gelatinization at intermediate pressure levels, and B-type starches are the most pressure resistant. Among different starches, potato starch appears to be the most resistant starch to high-pressure gelatinization. Studies in the literature show that starch can be gelatinized by pressure partially depending on the level of pressure applied (Ezaki and Hayashi 1992; Stute et al. 1996; Douzals et al. 1998).

High-Pressure Processing of Wheat Starch Suspensions The impact of HHP on gelatinization of starch is investigated by applying a pressure treatment between 0.1 and 700 MPa to a 30% wheat starch suspension. The high-pressure-processed starch is then characterized by performing DSC studies. A preliminary experiment at various starch concentrations showed that it requires at least 12% wheat starch concentration to form a gel by using HHP processing. Although the DSC results exhibited a complete gelatinization, a characteristic gel texture cannot be obtained below 12% wheat starch concentration. A similar observation was reported by Stolt et al. (2001) for barley starch: while the increase of viscosity was slight for 10% suspension after pressure treatment at 550 MPa, a strong paste with creamy texture was obtained with 25% suspension. Stute et al. (1996) also stated that HHP processed starches exhibited low viscosity values at the concentrations at which gel formation typically occurs by heat gelatinization, but they formed smooth pastes or rigid gels within the concentration range from 15% to 30% dry matter.

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345

Sample preparation A Hydrostatic High Pressure Unit (Quintas QFP-6; ABB Autoclave Systems, Columbus, OH) with a maximum pressure of 900 MPa was used to gelatinize the starch suspensions at ambient temperature. A water-ethylene glycol (1 : 1 vol/vol) mixture was used as the pressuretransmitting fluid. The rate of pressurization was 400 MPa/min, with a pressure release time of less than 20 s. During pressurization, the pressure, temperature, and time were kept constant using an automatic device and recorded throughout the cycle using a data logger. Fifty milliliters of 30% starch suspension was placed in a sterile, polyethylene bag and sealed under vacuum. The sample bags were placed inside the Hydrostatic High Pressure Unit. HHP processing was performed at 25 °C for 15 min at a range of pressures from 100 MPa to 700 MPa. The pressure-treated samples were analyzed using a DSC (model 2090; TA Instruments, New Castle, DE) to determine the degree of gelatinization. Samples (50–55 mg) of HHP-treated starch were placed in a high-volume stainless steel crucible, and the thermograms were recorded from 1 °C to 100 °C at a heating rate of 5 °C/min. Each thermogram was analyzed to calculate the onset and the peak temperatures and the enthalpy of the endothermic transition corresponding to the melting of ungelatinized starch. Results The degree of gelatinization of a 30% starch solution is directly related to the level of pressure applied at 25 °C. Figure 14.3 shows that the peak area corresponding to melting of ungelatinized starch decreases progressively as the level of applied pressure is increased. In addition, as the pressure level increases, the thermal stability of the ungelatinized starch phase decreases, indicating changes in the crystal structure of starch, although some light microscopy studies show intact starch granules after pressure treatment (Douzals et al. 1998; Stolt et al. 2001). However, some microscopic observations showed some swelling of starch granules (Douzals et al. 1996), which could lead to the observation of reduced thermal stability. The data in Figure 14.3 were analyzed further to calculate the percentage of gelatinized starch as a function of applied pressure (Figure 14.4). Percent starch gelatinization data from Douzals et al. (1996) and Stute et al. (1996) for wheat starch also are included in Figure 14.4. Although the initial starch concentrations are different, 16% (Douzals

–0.30

600 500

–0.32

Heat Flow (W/g)

400 –0.34

300 200

–0.36 100 –0.38

–0.40 50 Exo Up

55

60

65

70 75 80 Universal V2.6D TA Instruments Temperature (˚C)

Figure 14.3. Gelatinization endotherm of 30% wheat starch suspension after HHP processing at various pressure levels. 100

Percent gelatinization

80

60

40

20

0 0

100

200

300

400

500

600

Pressure (MPa)

Figure 14.4. Percent gelatinization of wheat starch as a function of HHP. Open square, 30% starch (this study); filled circle, 16% wheat starch (data taken from Douzals et al. 1996); filled diamond, 25% wheat starch (data taken from Stute et al. 1996).

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347

et al. 1996), 25% (Stute et al. 1996), and 30% (w/w) dry solids (this work), the trend seems to be similar. A fairly sharp increase in the extent of gelatinization starts at 300 MPa for all starch data, and wheat starch suspensions are completely gelatinized after a treatment at 500– 600 MPa. The results suggest that starch can be partially or completely gelatinized by increasing pressure at constant temperature. Douzals et al. (2001) further characterized the behavior of the wheat starch-water suspensions at 5% dry starch concentration over a pressure range of 0.1 and 600 MPa and over the temperature range of −20 ° and 96 °C. The microscopic measurements of the loss of birefringence of the granules calibrated by DSC was used to determine the extent of gelatinization. The data were used to develop the pressure-temperature (P-T) gelatinization diagram. The P-T gelatinization diagram is similar to the P-T diagram of denaturation of proteins. The P-T diagram indicates that starch can be gelatinized under various pressure-temperature combinations; however, the gelatinized starch can have different properties based on the gelatinization conditions. For corn starch, Zuo et al. (1999) report the presence of native starch after a treatment at 700 MPa for 2 min, but complete gelatinization after 5 min, which indicates that starch gelatinization by pressure is a kinetically controlled process. This issue becomes significant for investigation of starch kinetics at HHPs. The starch gelatinization kinetics should be decoupled from the rate of increase of pressure with time. In fact, Stolt et al. (2001) state that treatment of barley starch suspension at 550 MPa with a zero-minute holding time exhibited almost complete gelatinization, indicating pressurization at a rate of 20 MPa·min−1 was sufficiently slow for complete gelatinization.

Storage of Gelatinized Starch Gelatinized starch recrystallizes during storage, affecting the texture and shelf life of food products. This phenomenon is known as retrogradation. Retrogradation contributes to the quality defects in foods, such as loss of viscosity, syneresis, and precipitation. Jouppila et al. (1998) reported that water content, storage temperature, and the temperature difference between storage temperature and glass transition temperature were important factors in retrogradation of thermally treated corn starch. Furthermore, the formation of bonds between the macromole-

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cules during retrogradation depends on the gelatinization conditions, including concentration and degree of gelatinization (Stute et al. 1996). Douzals et al. (1998) reported that crystallization of the HHPgelatinized wheat starch (30% dry matter) during storage approached an asymptotic value after 6 days, and the extent of retrogradation was higher for starch gelatinized by heat than starch gelatinized by pressure at 600 MPa. However, Stolt et al. (2001) reported that the retrogradation of heat-treated (90 °C, 30 min) and pressure-treated (550 MPa, 30 °C, 10 min) 25% (w/w) barley starch stored at 4 °C was similar and did not approach an asymptotic value after 7 days of storage. The results may suggest that retrogradation depends on the botanical source of the starch as well as gelatinization conditions, storage temperature, and starch concentration.

Conclusions An understanding of the effect of high pressure on the properties of starch-based food systems under conditions relevant to food storage are necessary to predict storage stability of such systems so that HHPprocessing protocols can be optimized for successful development of HHP-processed commercial food products. Studies on the retrogradation properties of starch in the presence of common food ingredients are also essential for optimization of processing conditions so as to improve the physical stability and textural characteristics of HHP-processed food products. Because DSC instruments operating under conditions relevant to HHP-processing conditions are not commercially available, starch samples have to be treated before performing DSC on the samples. The thermodynamic basis of starch gelatinization involves initial and final states that can be experimentally defined and energetic and/or structural differences that can be measured using calorimetry. The comparison of various final states as a function of exposure to various levels of pressure, starting from the same initial state, makes it possible to predict the effectiveness of HHP to produce gelatinized starch. However, development of DSC equipment working at high pressures will allow one to perform pressure scans to determine the pressure dependence of the gelatinization event, to investigate the kinetics of starch gelatinization at constant pressure, and to separate the irreversible and reversible events.

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References Douzals J.P., Marechal P.A., Coquille J.C., and Gervais P. 1996. Microscopic study of starch gelatinization under high hydrostatic pressure. J Agric Food Chem, 44(5):1405–1409. Douzals J.P., Perrier-Cornet J.M., Gervais P., and Coqulle J.C. 1998. High-pressure gelatinization of wheat starch and properties of pressure-induced gels. J Agric Food Chem, 46:4824–4829. Douzals J.P., Perrier-Cornet J.M., Coqulle J.C., and Gervais P. 2001. Pressuretemperature phase transition diagram for wheat starch. J Agric Food Chem, 49:873–876. Ezaki S. and Hayashi R. 1992. High-pressure effects on starch: Structural changes and retrogradation. In: High Pressure and Biotechnology, Vol. 224, Balny C., Hayashi R., Heremans M.P., editors, pp. 163–165. Colloque INSERM/John Libbey Eurotext: Montrouge, France. Famelart M.H., Chapron L., Piot M., Brule G., and Durier C. 1998. High pressureinduced gel formation of milk and whey concentrates. J Food Eng, 36:149–164. Fennema O.R. 1996. Food Chemistry, 3rd edition, pp. 201–204. Marcel Dekker: New York. Jobling S. 2004. Improving starch for food and industrial application. Cur Opin Plant Biol, 7:210–218. Jouppila K., Kansikas J., and Roos Y.H. 1998. Factors affecting crystallization and crystallization kinetics in amorphous corn starch. Carbohyd Polym, 36:143–149. Messens W., Van Camp J., and Huyghebaret A. 1997. The use of high pressure to modify the functionality of food proteins. Trends Food Sci Technol, 8:107–112. Roos Y.H. 1995. Phase Transitions in Food. Academic Press: San Diego, CA. Stolt M., Oinonen S., and Autio K. 2001. Effect of high pressure on the physical properties of barley starch. Innov Food Sci Emerg Technol, 1:167–175. Stute R., Heilbronn R.W., Klingler R.W., Boguslawski S., Eshtiaghi M.N., and Knorr D. 1996. Effects of high pressures treatment on starches. Starch/Staeke, 48:399–408. Zuo C., Ma, C., and Zhang S. 1999. Effects of high hydrostatic on gelatinization of corn starch. In: Proceedings of the International Conference on Agricultural Engineering (ICAE), Vol. 4, pp. 91–93 Beijing, China.

Chapter 15 Use of Calorimetry to Evaluate Safety of Processing

Hans Fierz

Scope Concepts Severity: Adiabatic Temperature Rise Probability: Time to Maximum Rate Critical Conditions Autocatalysis Differential Scanning Calorimetry Screening Comparison of Open and Closed Measurement Methods Estimation of q′(T ) Isoconversional Methods High-Sensitivity Calorimetry Adiabatic Measuring Methods Dewar Vessels Accelerating Rate Calorimeter Reactions with Oxygen Screening Test Determination of Self-Ignition Temperature Applications Formation of Hot Spots in Dryers Storage and Hot Discharge Prevention of Molasses Incidents Transport Safety Conclusion References

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352 352 353 353 354 356 356 356 357 357 360 361 361 361 362 363 363 364 364 364 364 365 365 366 366

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Scope Food, as does every other chemical, shows chemical reactivity, and if handled in bulk can be dangerous. Numerous incidents involving wheat, milk powder, coffee, or molasses are known, due to, for example, self-heating, self-ignition of hot spots, or dust explosions. This chapter focuses on the methodology for characterizing the thermal consequences of exothermic decompositions in bulk and the correspondent safety risks. Of course, there are desired synthetic exothermic reactions in bulk, with food presenting a thermal risk, as for example the hydrogenation of fats. These cases are rather rare and may not justify a treatment from a specialized point of view such as food chemistry. However, the methodology of how to treat and quantify the risk of both desired reactions and decompositions is not specific to food chemistry, but was developed for process chemistry in general. A thorough discussion of this topic can be found in books about process safety (Stoessel 2008). One of the major risks in food production is dust explosions in mills or dryers, for example, in sugar refining. Because this chapter deals with applications of calorimetry, we consider dust explosions only insofar as hot spots serve as ignition sources. Further literature about this topic can be found in Bartknecht (1981).

Concepts The risk of an exothermic decomposition can be defined as the product of its severity and its probability, both of which can be discussed within the framework of the so-called adiabatic scenario. Adiabatic means that there is no heat exchange at all between the system and the surroundings. A situation with no or negligible heat exchange could occur in various cases, including failure of heat transfer (breakdown of stirrer or cooling system) during storage of a liquid or storage of reactive solids in bulk. The latter situation can occur either intentionally, for example, when a solid is stored in drums or containers at elevated temperature, or unintentionally in a dryer or a mill when after a technical incident the product is no longer agitated and the product cannot be discharged.

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Severity: Adiabatic Temperature Rise In an ideal adiabatic environment any heat generated by a decomposition will be accumulated and converted to a temperature rise, ΔTad, ΔTad =

Qr′

(15.1)

cp

where Q′r is the specific heat of reaction and cp the specific heat capacity. The adiabatic temperature rise is a measure of the severity of an incident. An adiabatic temperature rise of 50 K can hardly be considered critical, but one of 400 K could lead to formation of gases, to an explosive rupture of the vessel, and finally to a fire due to self-ignition of the dispersed material. Probability: Time to Maximum Rate The rate of an exothermic reaction is proportional to its heat release rate and if no heat can be removed, to its temperature increase rate:

Q′ dT q ′ = = r ′⋅ r dt c p cp

(15.2)

where q′ is the specific heat production in watts per kilogram and r′ is the specific reaction rate in kilograms per second. As the reaction rate increases with the temperature (law of Arrhenius), temperatures increase quickly resulting finally in a so-called thermal explosion. This can be characterized by an adiabatic temperature rise and a time until the explosion sets in, the so-called time to maximum rate under adiabatic conditions (TMRad). For reactions of nth order, which generate a large amount of heat, the time to maximum rate can be described by the following equation: TMR ad (T ) =

RT 2 Ea

cp q ′(T )

(15.3)

where R is the universal gas constant and Ea the activation energy.

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Temperature [°C]

60 50 t/8

40 t/4

30 t/2

20 t

10 0 0

10

20

30

40

50

60

70

Time [min]

Figure 15.1. Temperature as function of time using the assumption that a 10 ° increase in temperature doubles the reaction rate. Under adiabatic conditions, this produces a thermal explosion after a defined time, as described in the section Probability: Time to Maximum Rate.

The time until the system explodes is a function of the initial heat release rate q′ and thus of the temperature (Figure 15.1). Note that in the beginning, the temperature increase rate is only moderate. The TMRad is therefore related to the probability of an incident. At very long times, countermeasures may be taken, for example, discharging the container or filling it with water. A very short time, however, does not allow any action to be taken, and the thermal explosion cannot be avoided. Critical Conditions Critical heat release rate The adiabatic case is the worst-case assumption. Any real system loses heat to its surroundings either by convection, in the case of liquids, or by conduction, in solids. A steady temperature will be obtained when the heat release rate of the reaction equals this heat loss rate, which is the definition of the critical heat release rate q′crit. Any heat release rate higher than this will lead to heat accumulation, to a temperature increase, and finally to a thermal explosion.

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Critical layer thickness In solids, the dominant heat transport mechanism is conduction. For each heat release rate, there is a critical layer thickness or critical radius rcrit of the bulk, above which the decomposition heat can no longer be dissipated (Equation 15.4). For a given shape of the bulk, the radius will define the volume. r crit = ∂⋅

RT 2 Ea

⋅

λ q′(T )⋅ρ

(15.4)

Critical temperature As the rate is related to the temperature via Arrhenius law, there is also a critical temperature for a given bulk volume. Details can be found in Gray and Lee (1967). The critical temperature, Tcrit, is therefore a function of the layer thickness, r, and its physical properties (density ρ, thermal conductivity λ, geometry δ), as well as of the macrokinetics of the decomposition characterized by (q′(T), Ea). Figure 15.2 shows a typical dependency of the critical radius of the temperature. Many methods originally developed by trial and error use, in fact, the concept of critical temperatures (see below).

Critical radius [m]

1.00E+00

1.00E-01 unstable, thermal explosion

1.00E-02 stable, no thermal explosion 1.00E-03 0

50

100 Temperature [°C]

150

200

Figure 15.2. Dependence of the critical radius r of the temperature T for a typical reaction as described in the text. Combinations of T and r in the overcritical region will lead to a thermal explosion; in the undercritical region, the system will be stable.

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Autocatalysis The concepts described above rely on the assumption that the heat release rate does not depend on the conversion. There are, however, many cases in which the heat release rate initially increases with increasing conversion, reaches a maximum, and then decreases again. This behavior is called formal autocatalysis or, in short, autocatalysis. For such cases, the equations previously discussed for the TMRad and the critical layer thickness can be applied only with caution. It is therefore important to identify this type of formal reaction. However, using temperature-programmed thermoanalytical measurements for this is not obvious and requires experience: Bou-Diab and Fierz (2002) describe an identification approach. Differential Scanning Calorimetry Differential scanning calorimetry (DSC) is often used either as a screening tool or to establish the thermal kinetics of a decomposition. Here, the change in heat flow as function of the oven temperature is recorded. Becasue starting materials and products may be volatile, correct results are obtained only by using closed and pressure-tight crucibles. Measurements in which the samples are allowed to lose mass can show quite different behavior from those made in closed crucibles. An example describing this situation is given for saccharose in the Comparison of Open and Closed Measurement Methods section below. Typically, pressure-resistant gold-coated 40 μl crucibles can be used, which can withstand 400 °C/220 bars. Screening From one temperature-programmed run, both the reaction and decomposition energy and its temperature range can be deduced (ASTM E537-98). As often is the case in calorimetry, the determination of the baseline is difficult because, for reactions, the recorded signals usually cover a broad temperature range. Interpolation of the baseline over such a broad range and therefore determination of the decomposition potential

2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5

120 Mass loss in %

100 80

TGA SDTA

60

357

40 20 0 -20

DeltaT [K]

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0 50 0 45 0 40 0 35 0 30 0 25 0 20 0 15 0 10

50

0

Temperature [°C]

Figure 15.3. Saccharose measured in an open crucible at 4 K/min (thermogravimetric analysis) as an illustration of the difference between using either open or closed crucible. Shown is the thermogravimetry TGA signal (mass loss in percentage as function of temperature) and the qualitative DTA signal (SDTA: ΔT sample, oven).

can be quite difficult. To cool down the sample after a run and to perform a second scan without removing the sample crucible from the sensor helps establish the baseline. Comparison of Open and Closed Measurement Methods Figure 15.3 shows a typical thermogravimetric trace of sugar (saccharose). Thermogravimetric analysis is based on mass loss of the sample; the sample crucible is open to the atmosphere and thus the method is called an open method. At 220 °C, there is a sudden decrease in mass followed by a gradual mass loss until, at 450 °C, all the sample has evaporated. At the same time, the instrument used records a qualitative heat flow signal, which indicates a peak heat release rate at 350 °C. The same substance measured in DSC in a closed crucible shows a quite high decomposition potential at lower temperatures (240 °C) (Figure 15.4). Estimation of q′(T) The detection limit of a modern DSC apparatus is about 1 W/kg. If this value is set equal to q′crit and used to calculate the critical radius rcrit in Equation 15.4, a value of about 0.1 m results, which is equivalent to a cubic container of approximately 8 L, depending on the assumptions

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1 0.5 0 -0.5 -1 0

50

100

150

200

250

300

350

400

Temperature [°C]

Figure 15.4. DSC measurement of saccharose at 4 K/min in a closed pressureresistant crucible. Note that the decomposition occurs at lower temperatures than in Figure 15.3.

made. In other words, lower heat release rates that may lead to a thermal explosion in a bigger volume may remain undetected. Extrapolation of heat release rates to lower temperatures is thus often necessary. A conservative estimate (that is an estimate giving high values) for q′ at lower temperatures can be obtained as follows, Once a baseline is drawn, the deviation of the signal from the baseline at any temperature T is proportional to the heat release rate q′(T ). If this is determined at the beginning of the signal, the influence of the conversion can be neglected, and q′(T ) can be used as reference value (Figure 15.5). In practice, a value of 20 W/kg for q′(T ) is a good compromise between a value too small to be measured and one so high that there is already noticeable conversion. To estimate heat release rates at other temperatures, a value for the activation energy Ea is needed. This is generally not known a priori. To overcome this problem, a very low value of the activation energy, for example 50 kJ/mol, can be used, which in the case of real decompositions is practically never encountered. This can now be used to calculate a too-short and thus conservative value of the TMRad (Figure 15.6) or of the critical radius rcrit. If acceptable TMRad values or critical volumes are then obtained at the desired temperature, no further action has to be taken. If not, more refined kinetic methods are needed. Thus, from one measurement both the severity ΔTad and the probability of a runaway TMRad can be estimated. This approach is in practical use in many laboratories and has been verified theoretically (Keller et al. 1997). Also, by comparing actual adiabatic experiments to the results of the described method, it has been shown that the results

140

Heat release rate [Watt/kg]

120 100 80 60 20 Watt/kg

40 20 0 0

50

100

150

200

250

300

Temperature [°C]

Figure 15.5. Estimation of q′(T) values from a DSC measurement. Shown is a schematic DSC thermogram and the determination of the heat release rate at small conversion.

Figure 15.6. Influence of different activation energies on the safety margin of the extrapolated TMRad. A measured heat release rate (15 W/kg at 160 °C, upper right) can be used to extrapolate the heat release rate corresponding to a TMRad of 24 h using a high activation energy (lower curve) or a low activation energy (upper curve). Extrapolated temperatures of 100 °C and 50 °C, respectively, result.

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so obtained in all studied cases were on the conservative side (Pastré et al. 2000). Some words of caution are necessary, however: • The point of the curve where 20 W/kg is reached should correspond to a conversion of less than 10%. • The method should not be used for extrapolations to higher temperatures. • It works best when the detectable beginning of the signal lies below 250 °C. • It depends on a good definition of the baseline. Especially strongly curved baselines will make the determination of the heat release rate unreliable at small conversion. • One should not extrapolate across a melting point, because decomposition mechanisms in the solid state can be very different from those in the liquid state. Isoconversional Methods Isoconversional methods deliver formal kinetic information by using a set of measurements where the heat release rate is measured at different temperatures and always at the same conversion. Once the kinetic information is known, it can be used to calculate the heat release rate and the conversion at different conditions, such as at fixed temperatures or under adiabatic conditions. Such an “isoconversional” set can consist of measurements at different but constant heating rates. Whereas simple methods are easy to use but limited to nth-order reactions (ASTM E698), the more advanced methods depend on sophisticated computer algorithms (Opfermann and Hädrich 1995; Roduit 2000), which are outside the scope of this chapter. Although the isoconversional method can be considered to be state of the art, it is not always possible to apply it. For example, in cases where the product melts and decomposes in the liquid state, it is often not possible to determine the decomposition kinetics in the solid state. Alternatively, one can use a set of measurements at different but constant temperatures, where the signal is recorded as a function of time. This approach works best with kinetics of nth-order, where the highest heat release rate occurs at the beginning of the isothermal

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phase. An Arrhenius graph of the maxima of the heat release rate q′ is then plotted as a function of the temperatures T, and from this reference the values for the heat release rate and the activation energy can be obtained. It is assumed that at the maximum of the heat release rate there is not yet any significant conversion. This method is easy to understand and to use; however, the temperature range and therefore the number of points in the Arrhenius graph is limited: (1) For isothermal measurements at high temperatures, there is some conversion during heatup, so the assumption of zero conversion therefore may not be true; and (2) at low temperatures, measurement time can be very long, as the baseline is known only after complete conversion. High-Sensitivity Calorimetry High-sensitivity calorimetry (Suurkuus and Wadsö 1982) can be used to avoid the cumbersome extrapolation of heat release rates to lower temperatures. In these instruments, heat release rates as low as 0.001– 0.03 W/kg can be measured directly. The method works at constant temperatures between 30 ° and 80 °C, and measurements usually take several days to perform.

Adiabatic Measuring Methods In an adiabatic calorimeter, there is no heat exchanged with the surroundings. Any heat produced in a system will therefore remain inside the system and produce a temperature rise. This can be measured, and once the heat capacity of the system is known, the heat of reaction can be determined. So at least in principle, one could put the decomposing substance in such a calorimeter and obtain both the adiabatic temperature rise and the TMRad. Dewar Vessels The use of adiabatic calorimeters seems to be an attractive choice, and the type most commonly used is the Dewar vessel (Rogers 1989). These are quite well insulated, but a 500-ml vessel filled with a liquid still has a heat loss of typically 0.04 W/kg/K. Heat release rates of less than 0.2–0.4 W/kg will in this case not lead to self-heating.

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Dewar vessels are usually made of glass and are not pressureresistant. Gases and vapors must be allowed to escape from the system, and because this is normally an endothermic process, it also will contribute to the heat loss. The sensitivity of such an arrangement can be improved (1) by minimizing the temperature difference between Dewar and oven by adapting the temperature of the oven to that of the Dewar and (2) by placing the Dewar vessel in an autoclave to suppress the evaporation of gases and vapors (Grewer 1994). The use of containment is also advisable for reasons of safety and industrial hygiene. This improvement requires the use of rather sophisticated experimental facilities, and especially in the case of autoclaves, a room with concrete walls and with remote control should be used.

Accelerating Rate Calorimeter The accelerating rate calorimeter was first described by Townsend (Townsend and Tou 1980). In this instrument, it is possible to measure both the TMRad and the adiabatic temperature rise with a relatively small sample of a few grams. A small pressure-resistant container is filled with the sample and placed in an oven at a temperature closely matches that of the sample container. There is therefore no heat exchange between sample and oven, and heat generated by a reaction will produce a proportional temperature rise. Despite the small sample size, the instrument is quite sensitive. It can detect approximately 0.3 W/kg, which corresponds to a heat rate of approximately 0.01 K/ min or 0.6 K/h, and its sensitivity corresponds to that of a 400-ml Dewar vessel. The proportionality between the specific heat of reaction Qr′4 and ΔTad or the specific heat production q′ and the rate of temperature rise dT/dt is the specific heat capacity of the system. The heat produced by the sample will heat not only the sample itself but also the sample container. To obtain TMRad and ΔTad values of the substance itself, the measured values TMRmeas and ΔTmeas must be corrected with a factor Φ as follows: TMRadcorr =

meas

TMR ad Φ

(15.5)

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and ΔTadcorr = ΔTadmeas ⋅ Φ

(15.6)

where Φ = 1+

mb × c p,b ms × c p , s

(15.7)

and m is mass, cp is specific heat capacity, and subscript b and s mean sample container and sample, respectively. The correction for the TMRad is valid for reactions of 0th order. At small conversion, nthorder reactions can be treated as being of 0th order, as conversion can be neglected.

Reactions with Oxygen Tests for the determination of the self-ignition temperature simulate a dryer, where the powder sample is in close contact with hot air. Different test arrangements are in use, which can be classified according to how oxygen is delivered to the sample: by forced convection or diffusion (Grewer 1971). In all these tests, the temperature of the hot air is either held constant or increased continuously, and the temperature difference between the sample and either an inert reference or the surroundings is recorded. With methods using relatively big sample volumes, temperatures in the center of the sample can deviate considerably from the temperature of the surrounding air and can reach overcritical values. These instruments will therefore not give quantitative heat release rates, but will be more suited for the detection of critical temperatures. Screening Test Currently, a widely used screening test apparatus, which is described by Grewer (1971), uses preheated air that flows through both a sample and a reference channel containing the respective samples in small wire baskets. An inert substance such as graphite is used as reference.

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Determination of Self-Ignition Temperature In the European Union, a testing method of the diffusion type is in official use (method A16 of the European Commission Joint Research Centre). Here, the sample is placed in a wire basket surrounded by hot air. The temperature of the oven in which the sample reaches 400 °C due to self-ignition is called the self-ignition temperature.

Applications Formation of Hot Spots in Dryers Many solid food products are produced by spray-drying of water-based solutions. Such a solution is sprayed into a stream of hot air. The water is evaporated, and the solute is converted to a powder. Examples are the production of powdered milk or coffee whitener. Reactions with oxygen can lead either to product deterioration or to smoldering if there are deposits. Deposits not only occur in the dryer itself but also in equipment downstream, for example, in filters. As discussed previously, if the heat release rate due to the oxidation in such a product layer is overcritical, the formation of hot spots that can trigger a dust explosion is unavoidable. In general, the self-ignition temperature should therefore be determined. Storage and Hot Discharge Sometimes the product is discharged from a dryer to drums or containers while still hot. In a dryer, heat produced by an ongoing decomposition reaction can be removed by convection because the product is agitated. In a container, however, this agitation is lacking, and heat is removed by conduction only. This is a much less efficient mechanism of heat transfer. The product can therefore self-heat, the self-ignition temperature can be reached, and a fire can occur. If the decomposition kinetics are known, it is possible to calculate the critical temperature for a given container size. Alternatively, one can establish a temperature limit using a series of isothermal Dewar experiments by starting at a relatively high temperature where a signal is observed and then lowering the temperature in steps of 10 K until no signal is observed. From this temperature a safety margin, for

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example 50 K, is deduced. This gives a conservative estimate for the allowed discharge temperature. Prevention of Molasses Incidents Several incidents where big molasses reservoirs burst are recorded, for example, the 1919 Great Molasses Disaster in Boston, Massachusetts, in which a large tank burst, flooding the streets with molasses and killing several people. The bursting could have been caused by various reasons. One reason is certainly the exothermic decomposition in combination with the formation of carbon dioxide (Strecker degradation). The DSC thermogram of the molasses studied in our laboratories shows a decomposition signal between 120 °C and 250 °C, with an energy of several hundred kilojoules per kilogram. Isothermal measurements in the thermal activity monitor showed a heat release rate of 0.004 W/kg at 50 °C and 0.06 W/kg at 75 °C, which gave an activation energy of about 100 kJ/Mol. The critical radius at 30 °C calculated by Equation 15.4 and using the above-determined kinetics is fairly low, approximately 3.5 m, which is equivalent to a capacity of 100,000 l and therefore comparable with the size of actual reservoirs. These findings are confirmed in the literature (Platje, Wittenberg, and Timmermans 2006). Heating of molasses in the reservoir to reduce the viscosity and to facilitate pumping should therefore be avoided. Transport Safety The UN recommendations for the transport of dangerous goods (UNECE 2003) require a chemical to be tested for self-reactivity (Class 4.1) if it releases more than −300 kJ/kg. It is considered to be selfreactive if a 50-kg package has a self-accelerating decomposition temperature (SADT) less than 75 °C. The SADT is defined as ΔT = Ti − Ta ≥ 6 K, where Ti is the temperature of the package and Ta is the temperature of the environment. According to the recommendations, a 50-kg package can be substituted by a Dewar vessel with a specific heat loss of 0.08 W/kg/K. For screening purposes, the specific heat release rate can thus be estimated at 81 °C using the method outlined in the Estimation of q′(T) section. If the specific heat release rate is higher than 0.48 W/kg, a full determination of the SADT must then be made using either the heat

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accumulation storage test (HAST) H.4, based on a 500-ml Dewar vessel, or the isothermal storage test (IST) H.3, based on isothermal measurements. Depending on the outcome of these tests, transport restrictions apply; for example, packages have to be cooled during transport or air transport is not allowed. Typical curves of food products, which currently would probably have to be classified as class 4.1, have been published previously (Raemy and Lambelet 1982). Conclusion Food, especially carbohydrate- and protein-based food, can undergo highly exothermal decompositions. Unlike other chemicals, food itself is by definition not toxic; nevertheless, the consequences of an accident involving bulk food can be devastating. It is possible, however, to assess the risk of such decompositions using the same principles as for process chemistry. If these principles are properly applied and the stability data correctly determined, safe conditions for handling can be established and accidents in the food industry prevented. References ASTM Standard E 698. 2005. Test Methods for Arrhenius Kinetic Constants for Thermally Unstable Material. ASTM International: West Conshohocken, PA. Retrieved from: www.astm.org. ASTM Standard E 537-98. 2007. Standard Test Method for Assessing the Thermal Stability of Chemicals by Methods of Thermal Analysis. ASTM International: West Conshohocken, PA. Retrieved from: www.astm.org. Bartknecht, W. 1981. Explosions. Course Prevention Protection. Springer: Berlin. Bou-Diab, L. and Fierz, H. 2002. Autocatalytic decomposition reactions, hazards, and detection. J Hazard Mater, 93(1):137–146. European Commission Joint Research Centre, Institute for Health and Consumer Protection. Directive 67/548/EEC, Annex V, Method A 16. Retrieved from: http:// ecb.jrc.it/testing-methods. Gray, P., Lee, P.R. editors. 1967. Thermal Explosion Theory, Oxidation and Combustion Reviews, 2nd edition. Elsevier: Amsterdam. Grewer, T. 1971. Zur Selbstentzündung von abgelagertem Staub. Staub-Reinhaltung der Luft, 31(3):97–101. Grewer, T. 1994. Thermal Hazards of Chemical Reactions, Industrial Safety Series, Vol. 4. Elsevier: Amsterdam.

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Keller, A., Stark, D., Fierz, H., Heinzle, E., and Hungerböhler, K. 1997. Estimation of the time to maximum rate using dynamic DSC experiments. J Loss Prevent Process Ind, 10:31–41. Opfermann, J. and Hädrich, W. 1995. Prediction of the thermal response of hazardous materials during storage using an improved technique. Thermochim Acta, 263:29–50. Pastré, J., Wörsdörfer, U., Keller, A. and Hungerböhler, K. 2000. Comparison of different methods for estimating TMRad from dynamic DSC measurements with ADT 24 values obtained from adiabatic Dewar experiments. J Loss Prevent Process Ind, 13:7–17. Platje, T., Wittenberg, A., and Timmermans, A. 2006. Study of the “runaway behaviour” of technical sucrose solutions. Zuckerindustrie, 131(4):231–238. Raemy, A. and Lambelet, P. 1982. A calorimetric study of self heating in coffee and chicory. J Food Technol, 17:451–460. Roduit, B. 2000. Computational aspects of kinetic analysis. Part E: The ICTAC Kinetics Project—numerical techniques and kinetics of solid state processes. Thermochim Acta, 355:71. Rogers, R.R. 1989. The advantages and limitations of adiabatic Dewar calorimetry in chemical hazard testing. Plant Operations Progress, 8:109. Stoessel, F. 2008. Thermal Safety of Chemical Processes: Risk Assessment and Process Design. Wiley-VCH: Weinheim. Suurkuus, J. and Wadsö, I. 1982. A multichannel microcalorimetry system. Chem Scripta, 20:155–163. Townsend, D.I. and Tou, J.C. 1980. Thermal hazard evaluation by an accelerating rate calorimeter. Thermochim Acta, 37:1–30. UNECE Transport Division. 2003. International Recommendations on the Transport of Dangerous Goods, Manual of Test and Criteria, 4th edition. UNECE: New York.

Index

Accelerating rate calorimeter, 362–63 Activation enthalpy, protein heatinduced transformations with, 129 Adiabatic measurement methods accelerating rate calorimeter, 362–63 Dewar vessels, 361–62 food-processing safety with, 361–63 Adiabatic temperature rise, 353 ADSC. See Alternating DSC Aggregation DSC technique v. vessel/heating mode with, 28t globular proteins in bulk phase system, 124–29, 126f–128f heat effects of, 88 heating mode with, 36–37, 37f protein postdenaturation, 110–12 Alcohols, protein denaturation affected by, 99–100, 101f 11S globulin, 99–100, 101f Setschenow equation for, 100 Alfalfa, 93 Alginate, denaturation temperature of β-lactoglobulin with, 110

Alternating DSC (ADSC), foodprocessing design with, 204 AMF. See Anhydrous milk fat Ampoule mixing vessel, mixing and reaction heat flux microcalorimeter with, 30, 31t Anhydrous milk fat (AMF) Avrami plots from, 139f calorimetric parameters observed with, 136t heat-induced transformations with, 133–41, 135f, 136t, 137f, 139f, 139t, 140f, 184–87, 186f, 187f heating/cooling curves, 135f isothermal curves, 137f protein-stabilized, 135f surfactant added, 135f, 136t, 137f Antibiotics, 153–55, 153f, 155f Apple, heat capacity for, 36t Arabic gum, denaturation temperature of 11S globulin with, 106t Autocatalysis, 356 Autoclave, 54, 54f, 55f, 57 Avrami equation, oil-in-water emulsions with, 138–39, 139f, 139t

369

370

Index

Bacillus megaterium, DSC analysis of, 149 Bacteria Bacillus megaterium DSC analysis of, 149 calorimetry with growth of, 43 Citrobacter freundii DSC analysis of, 149 Clostridium perfringens DSC analysis of, 149–53, 151f results for, 150–52, 151f sample preparations for, 149–50 DSC analysis of foodborne, 147–64, 151f, 153f, 155f, 157f, 160f, 162t antibiotics’ effect on, 153–55, 153f, 155f cold shocking, 148 heat shocking, 148, 151 DSC technique v. vessel/heating mode with, 28t endothermic/exothermic effects of, 19t Escherichia coli DSC analysis of, 148, 155–58, 157f erythromycin treatment of, 154–55, 155f heat inactivation parameters of, 159–60, 160f nonthermal treatment of, 162–63, 162t food-processing’s effect on, 10–11 food-processing treatment evaluation by DSC for, 158–64, 160f, 162f antimicrobials in, 163–64 heat inactivation parameters of bacteria in, 158–61, 160f, 162f HHP in, 161

nonthermal treatment of bacteria in, 161–63, 162f hydrostatic pressure resistance of, 44–45, 44f inactivation of, 10 Lactobacillus plantarum DSC analysis of, 155–58, 157f Listeria monocytogenes DSC analysis of, 149–53, 151f antibiotics’ effect on, 153–54, 153f heat inactivation parameters of, 159 results for, 152–53, 152f sample preparations for, 149–50 Mycoplasma laidlawii DSC analysis of, 149 Staphylococcus aureus nonthermal treatment, 162–63, 162t Batch high-pressure vessel, mixing and reaction heat flux microcalorimeter with, 29, 31t Batch mixing vessel, high sensitivity heat flux calorimeter with, 27, 28t Batch standard vessel, mixing and reaction heat flux microcalorimeter with, 29, 31t Benzene, transitiometry verification test using, 322, 323f Binding data quantifies high-affinity, 75–77 mixing and reaction calorimetry with, 41–42 processes, protein in dilute solution with, 78, 79, 80f Blank test heat flow equation, 32

Index Bovine β-lactoglobulin, 94–95 Bovine serum albumin (BSA), protein denaturation affected by, 104 Broad beans, 11S globulin from, 99–100, 101f, 105 BSA. See Bovine serum albumin Bulk phase system, denaturationaggregation of globular proteins in, 124–29, 126f–128f Butter, heat capacity for, 36t Butyric acid, melting point of, 171f Cabbage, heat capacity for, 36t Calibration, 23–25, 24f, 25f Calvet type calorimeter, 23, 206 food-processing design, 206 heat calibration procedure for HP-DSC, 60f, 61–63, 62f high pressure calorimetry, 314 HP-DSC, 57–63, 60f, 62f Joule effect in, 23–24, 24f, 25f MICROCALIX, 177 necessity for HP-DSC, 57 parameters for, 58 heating rate, 58 pan type, 58 sample mass, 58 temperature, 58 reference substances for, 58 indium, 58, 60, 61 lead, 58 tin, 58, 59 zinc, 58 scanning transitiometry, 322–23 table of corrections for, 57 temperature calibration procedure for HP-DSC, 58–61 uncertainty with HP-DSC, 58

371

Calorimeter accelerating rate, 362–63 symmetrical, two-chamber, 17–18 Calorimetry. See also Calvet type calorimetry; Differential scanning calorimetry; Heat flux calorimeters; Heat flux microcalorimetry; High pressure calorimetry; High pressure differential scanning calorimetry; High-sensitivity calorimetry; High sensitivity heat flux calorimeter; Isothermal calorimetry; Isothermal solution calorimetry; Isothermal titration calorimetry; Microcalorimetry; Mixing and reaction calorimetry; Mixing and reaction heat flux microcalorimeter; Pressure calorimetry advantages for using, 7 applications of, 15–45 under controlled relative humidity, 45 food dehydration understood with, 289–309 calorimetric glass transition measurement for, 293–96, 294f, 296f dielectric and mechanical relaxations with, 296f, 297–98 freeze-drying for, 290, 306–7 freezing in, 301–3, 302f, 303f glass transition and stability of, 307–8, 308f phase and state transitions of, 290, 292–93, 293f

372

Index

Calorimetry (continued) spray-drying for, 290, 305–6, 306f state diagrams with, 303–7, 304f thermal analysis in, 298–301, 299f, 300f food industry interest in, 226 food-processing design in, 202–6 alternating DSC, 204 Calvet type calorimetry, 203, 206 differential scanning calorimetry, 202–6 differential thermal analysis, 203 dynamical mechanical analysis, 225 dynamical mechanical thermal analysis, 225 methods, 205–6 modulated DSC, 204 samples, 206 techniques, 203–5, 204f food-processing safety evaluated with, 351–66, 354f, 355f, 357f–359f adiabatic measurement methods for, 361–63 applications for, 364–66 concepts for, 352–56, 354f, 355f critical conditions in, 354–56, 355f critical heat release rate in, 354–55 critical temperature, 355–56, 355f estimation of q’(T) in, 357–60, 359f formation of hot spots in dryers, 364

high-sensitivity calorimetry in, 361 isoconversional methods in, 360–61 open v. closed measurement methods in, 357, 357f, 358f prevention of molasses incidents, 365 reactions with oxygen in, 363–64 screening in, 356–57 storage and hot discharge, 364–65 transport safety, 365–66 isothermal, 38 isothermal performance of, 19 isothermal solution, 220 methods with food using, 5–13 parameters of, 8 interpretation of overlapping peaks, 8 magnitude of heat flow, 8 moisture loss, 8 time scale, 8 pressure, 43–45, 44f scanning mode, 35–36, 44 solution, 218 step heating in, 40, 40f suitability for food of, 52 ultrasensitive to proteins, 8 Calvet principle, 22–23, 22f, 23f, 26 Calvet type calorimetry, 22–23, 22f, 23f, 203 calibration of, 23, 206 food-processing design with, 203, 206 Capric acid, melting point of, 171f Caproic acid, melting point of, 171f, 172f Caprylic acid, melting point of, 171f Carbohydrates

Index C80 technique v. vessel/heating mode with, 31t cereal with, 12 endothermic/exothermic effects of, 19t gelatinization of starch-water systems, 207 glass transition with, 294 hydrophilic component of, 291 thermal analysis of cereal nonstarch, 276–78, 277f thermal behavior of food constituents in, 206–8, 207f, 208f Carboxymethylcellulose, denaturation temperature of 11S globulin with, 106t Carp, heat capacity for, 36t κ-Carrageenan denaturation temperature of 11S globulin with, 106t denaturation temperature of β-lactoglobulin with, 110 λ-Carrageenan denaturation temperature of 11S globulin with, 106t denaturation temperature of β-lactoglobulin with, 110 Carrots, isothermal traces at temperatures for, 39f Caseins, 122–23 CB. See Cocoa butter Cellobiose, calorimetric curves of, 208f Centre National de la Recherche Scientifique (CNRS), 176 Cereal, 12 C80 technique v. vessel/heating mode with, 31t Cereal processing, thermal analysis to design/monitor, 265–85

373

nonstarch carbohydrates, 276–78, 277f process applications, 278–85, 280f–284f proteins, 272–76, 274f, 275f starch, 268–72, 289f–272f Chaotropic salts (Salting-in salts), 95 Chocolate C80 technique v. vessel/heating mode with, 31t DSC technique v. vessel/heating mode with, 28t Citrobacter freundii, DSC analysis of, 149 Closed measurement method, 357, 357f, 358f Clostridium perfringens DSC analysis of, 149–53, 151f results for, 150–52, 151f sample preparations for, 149–50 CNRS. See Centre National de la Recherche Scientifique Cocoa butter (CB) DSC and XRD with, 179–84, 180f, 183f MICROCALIX for, 182–84, 183f polymorphism, 181–82f polymorphism of 1,2-dipalmitoyl3-oleoylglycerol, 182–84, 183f Coffee, C80 technique v. vessel/ heating mode with, 31t Cold denaturation, protein in dilute solution with, 75 Cold shocking, 148 Complete reaction, test for, 255 Cream, heat capacity for, 36t Critical conditions, food-processing safety with, 354–56, 355f heat release rate, 354–55 temperature, 355–56, 355f

374

Index

Crystallization DSC technique v. vessel/heating mode with, 28t heating mode with, 36 isothermal, 39 lard, 190, 191f, 191t lipids, 210–11 milk fat, 189–90 oil-in-water emulsions, 132–36, 135f, 136t water in pork muscle with, 327f CSC, 21 Dairy, heat capacity for, 36t Debye-Höckel approximation, 98, 99t Dehydration. See Food dehydration Denaturation cold, protein in dilute solution with, 75 defined, 122 DSC technique v. vessel/heating mode with, 28t globular proteins in bulk phase system, 124–29, 126f–128f 11S globulin, 89–92, 91f alcohol’s effect on, 99–100, 101f different pH values in, 91f, 92 polysaccharides’ effect on, 105–6, 106t, 110 salt’s effect on, 96–98, 97f, 99t two-state model to analyze, 89 heat effects of, 88 heating mode with, 36–37, 37f Kunitz inhibitor, polysaccharides’ effect on, 107–10, 108f β-lactoglobulin, 125 methodological approaches to study, 89 of protein, 87–113, 91f, 97f, 99t, 101f, 103f, 106t, 108f

effects of alcohols on, 99–100, 101f effects of odorants on, 102–4, 103f effects of pH on, 89–95, 91f effects of polysaccharides on, 104–10, 106t, 108f effects of salts on, 95–99, 97f, 99t reversibility of, 123 two-state model of, 123 Dewar vessels, 361–62 Dextran, denaturation temperature of 11S globulin with, 106t, 108f Dextran sulfate, 106t, 108f Differential scanning calorimetry (DSC), 6–7, 9, 16, 265 Bacillus megaterium analysis by, 149 calibration of, 23–25, 24f, 25f Calvet principle with, 22–23, 22f, 23f, 26 Calvet type of, 22–23, 22f, 23f, 203, 206 calibration of, 23, 206 efficiency ratio of, 23f schematic of, 22f Citrobacter freundii analysis by, 149 Clostridium perfringens analysis by, 149–53, 151f results for, 150–52, 151f sample preparations for, 149–50 cold denaturation with, 75 data quantifies high-affinity binding with, 75–77 assumptions of, 75 concentration in, 77 equilibrium in, 76–77

Index hydrogen ion buffer selection in, 77 purity in, 77 two-state, reversible transitions in, 76–77 efficiency ratio of flat-shaped, 21f Escherichia coli analysis by, 148, 155–58, 157f erythromycin treatment of, 154–55, 155f heat inactivation parameters of, 159–60, 160f nonthermal treatment of, 162–63, 162t foodborne bacteria analysis by, 147–64, 151f, 153f, 155f, 157f, 160f, 162t antibiotics’ effect on, 153–55, 153f, 155f cold shocking, 148 heat shocking, 148, 151 food-processing design in, 202–6 methods, 205–6 samples, 206 techniques, 203–5, 204f with XRD, 225 food-processing safety with, 355f, 356–61, 357f–359f estimation of q’(T) for, 357–60, 359f high-sensitivity calorimetry for, 361 isoconversional methods for, 360–61 open v. closed measurement methods for, 357, 357f, 358f screening for, 356–57 food-processing treatment evaluation by, 158–64, 160f, 162f antimicrobials in, 163–64

375 heat inactivation parameters of bacteria in, 158–61, 160f, 162f HHP in, 161 nonthermal treatment of bacteria in, 161–63, 162f glass transition with, 294f heat flux type of, 20–21, 26–30, 27f, 28t heating’s role in, 88 high pressure, 51–64, 54f–56f, 60f, 62f applications of, 63 calibration of, 57–63, 60f, 62f construction of, 53–57, 54f–56f Lactobacillus plantarum analysis by, 155–58, 157f Listeria monocytogenes analysis by, 149–53, 151f antibiotics’ effect on, 153–54, 153f heat inactivation parameters of, 159 results for, 152–53, 152f sample preparations for, 149–50 microcalorimetry v., 16, 19–25, 20f–25f heat flux microcalorimetry, 19–25, 20f–25f Mycoplasma laidlawii analysis by, 149 power compensated type of, 20–22 protein in dilute solution with, 68–77, 71f equations, 70–74, 71f heat capacity change origins for, 74 information content, 68–69 instrumentation, 69–70

376

Index

Differential scanning calorimetry (DSC) (continued) simulated DSC thermogram of, 70, 71f van’t Hoff enthalpy change, 72–74 schematic of plate-shaped sensor for, 20f schematic representation of, 299f sensor plate thickness in efficiency of, 21f Staphylococcus aureus nonthermal treatment with, 162–63, 162t starch analysis with, 268–72, 289f–272f starch gelatinization by heat monitored with, 342, 343f two types of, 20 X-ray diffraction with, 169–94, 171t, 172t, 173f, 177f, 180f, 183f, 186f, 187f, 189f, 191f, 192f applications for, 179–93, 180f, 183f, 186f, 187f, 189f, 191f, 192f cocoa butter in, 179–84, 180f, 183f lard in, 190–93, 191f, 192f MICROCALIX using, 170, 176–79, 177f, 180f, 183f, 186f, 187f, 189f, 191f, 192f milk fat in, 184–90, 186f, 187f, 189f results using, 179–93, 180f, 183f, 186f, 187f, 189f, 191f, 192f triacylglycerols in, 169–76, 171t, 172t, 173f Differential thermal analysis (DTA), 53, 173 food-processing design with, 203

Dilute solution, calorimetry of protein in, 67–84, 71f, 80f, 83f Dilute systems, 10 Dissolution, mixing and reaction calorimetry with, 41 DMA. See Dynamical mechanical analysis DMTA. See Dynamical mechanical thermal analysis Drying freeze-, 290, 306–7 hot spots with, 13 spray-, 290, 305–6, 306f DSC. See Differential scanning calorimetry DTA. See Differential thermal analysis Dynamical mechanical analysis (DMA) food dehydration with, 296f, 297–98 food-processing design in, 225 glass transition detected with, 296f, 297 thermal analysis with, 266 Dynamical mechanical thermal analysis (DMTA) food dehydration with, 296f, 297–98 food-processing design in, 225 glass transition detected with, 296f, 297 thermal analysis with, 266 Eggs, isothermal traces at temperatures for, 39f Electromotive force (Emf), 23, 25 Electron spin resonance (ESR), 301 Emulsions lipids, 214

Index oil-in-water, 132–41, 135f, 136t, 137f, 139f, 139t, 140f anhydrous milk fat, 133–41, 135f, 136t, 137f, 139f, 139t, 140f, 184–87, 186f, 187f Avrami equation for, 138–39, 139f, 139t crystallization in, 132–36, 135f, 136t fat crystal growth in, 138 Gompertz model for, 139–40, 139t, 140f ice cream, 133 kinetics of, 136–41, 137f, 139f, 139t, 140f melting of fat droplets in, 132–36, 135f, 136t triacylglycerols, 133 whipped cream, 133 protein’s role in, 10 Enthalpy, 265 activation, 129 estimate of apparent denaturation, 112 reaction, 255 van’t Hoff enthalpy change, 72–74 Entropy, protein heat-induced transformations with, 129 Enzymatic reactions, mixing and reaction calorimetry with, 42, 42f, 43f Enzyme C80 technique v. vessel/heating mode with, 31t DSC technique v. vessel/heating mode with, 28t endothermic/exothermic effects of, 19t Erythromycin, Escherichia coli treatment with, 154–55, 155f Escherichia coli

377

DSC analysis of, 148, 155–58, 157f erythromycin treatment of, 154–55, 155f heat inactivation parameters of, 159–60, 160f hydrostatic pressure resistance of, 44–45 nonthermal treatment of, 162–63, 162t ESR. See Electron spin resonance Exothermic decomposition, 13 Fat. See also Lipids C80 technique v. vessel/heating mode with, 31t DSC technique v. vessel/heating mode with, 28t endothermic/exothermic effects of, 19t oxidative stability of, 211–12 Fatty acids, 170–73, 171t, 172t, 173f crystallographic/energetic properties of, 172f hexagonal, 172, 172f, 173f melting point of, 171f, 172f orthorhombic perpendicular, 172, 172f, 173f triclinic parallel, 172, 172f, 173f Fermentation, mixing and reaction calorimetry with, 43 Fish, heat capacity for, 36t Fluid mixing vessel, high sensitivity heat flux calorimeter with, 27, 28t Foams, protein’s role in, 10 Food dehydration, 289–309 calorimetric glass transition measurement for, 293–96, 294f, 296f

378

Index

Food dehydration (continued) dielectric and mechanical relaxations with, 296f, 297–98 freezing in, 301–3, 302f, 303f glass transition and stability of, 307–8, 308f phase and state transitions of, 290, 292–93, 293f state diagrams with, 303–7, 304f freeze-drying, 290, 306–7 spray-drying, 290, 305–6, 306f thermal analysis in, 298–301, 299f, 300f Food flavorings, 89 odorants with, 102 Food-processing design, 201–27 food industry interest in calorimetry for, 226 related techniques for, 225 DMA, 225 DMTA, 225 DSC combined with XRD, 225 safety aspects for, 217–18 thermal analysis/calorimetry on, 203–6, 204f methods, 205–6 samples, 206 techniques, 203–5, 204f thermal behavior of food constituents in, 206–17, 207f, 208f, 210f, 213f, 215f, 216f carbohydrates, 206–8, 207f, 208f lipids, 208–14, 210f, 213f proteins, 214–16, 215f, 216f sugars, 206–8, 207f, 208f water, 216–17 thermal behavior of raw/ reconstituted food in, 217

thermodynamic parameters for, 218–25, 221f–223f heat of combustion, 225 heat of solution, 218–24, 221f–223f specific heat, 224–25 Food-processing safety adiabatic measurement methods with, 361–63 accelerating rate calorimeter, 362–63 Dewar vessels, 361–62 applications for, 364–66 formation of hot spots in dryers, 364 prevention of molasses incidents, 365 storage and hot discharge, 364–65 transport safety, 365–66 calorimetry for, 351–66, 354f, 355f, 357f–359f concepts with, 352–56, 354f, 355f adiabatic temperature rise, 353 autocatalysis, 356 probability, 353–54, 354f severity, 353 time to maximum rate, 353–54, 354f critical conditions with, 354–56, 355f heat release rate, 354–55 temperature, 355–56, 355f differential scanning calorimetry for, 355f, 356–61, 357f–359f estimation of q’(T) with, 357–60, 359f high-sensitivity calorimetry with, 361 isoconversional methods with, 360–61

Index open v. closed measurement methods with, 357, 357f, 358f screening with, 356–57 reactions with oxygen in, 363–64 determination of self-ignition temperature for, 364 screening test for, 363 Formal autocatalysis, 356 Fourier transform infrared spectroscopy, 11 Free protein, denaturation temperature of 11S globulin with, 106t Freeze-drying, 290, 306–7 Fruit, heat capacity for, 36t Galactose, calorimetric curves of, 208f Gas-flow vessel, mixing and reaction heat flux microcalorimeter with, 29, 31t Gelatin, endothermic/exothermic effects of, 19t Gelatin gels, frozen water ratio in, 326–29, 328f Gelatinization DSC technique v. vessel/heating mode with, 28t heating mode with, 38 starch, 12, 274, 278–79, 280f calorimetric analysis by HPP of, 341–49, 343f, 346f heat in, 342, 343f high pressure calorimetry on, 330–36, 332f–334f, 335t storage of, 347–48 thermodynamic data for, 335t wheat, 344–47, 346f starch-water systems, 207 Gelatin molecules, 122

379

Gelation, heating mode with, 37–38 Gibbs function, 267 Glass transition, 290–92 behavior of lactose with, 300f calorimetric measurement for, 293–96, 294f, 296f cooling/heating with, 295 DSC technique v. vessel/heating mode with, 28t DSC with, 294f Gordon-Taylor equation with, 300 mechanical/dielectric relaxations in, 296f, 297 stability of dehydrated materials with, 307–8, 308f studies referring to, 291 sugar/carbohydrates with, 294 two or more components with, 295 Globulin, 7S, different pH values in denaturation of, 93 Globulin, 11S alcohols effect on protein denaturation using, 99–100, 101f broad beans, 99–100, 101f, 105 denaturation of, 89–92, 91f different pH values in, 91f, 92 two-state model to analyze, 89 polysaccharides effect on protein denaturation using, 105–6, 106t, 110 β-glucosidase, ITC of binding inhibitors to, 80f Gompertz model, oil-in-water emulsions with, 139–40, 139t, 140f Gordon-Taylor equation, 300, 304 Grapefruit, heat capacity for, 36t Guar gum, denaturation temperature of β-lactoglobulin with, 110

380

Index

Heat calibration procedure, HP-DSC, 60f, 61–63, 62f Heat capacity constant pressure processes with, 30 defined as ratio, 30 determination, 30–35, 33f, 34f, 36t foods in, 35, 36t liquids in, 34–35, 34f temperature-scanning mode in, 31–33, 33f temperature step mode in, 33–34 DSC with, 6 formula for heat flux with, 17 Heat flow blank test heat flow equation, 32 magnitude of, 8 Heat flux electrical signal’s correlation with, 24 pork muscle crystallization with, 327f pork muscle thawing with, 326f power dissipation’s correlation with, 24 pressure shift freezing with, 330f ratio of measured to total, 20–21, 21f Heat flux calorimeters, 20–21, 26–30, 27f, 28t high sensitivity, 26–27, 27f, 28t batch mixing vessel for, 27, 28t fluid mixing vessel for, 27, 28t mixing vessels for, 27, 27f temperature control in, 26 thermal conductive block of, 26 mixing and reaction, 29–30, 31t

ampoule mixing vessel for, 30, 31t batch high-pressure vessel for, 29, 31t batch standard vessel for, 29, 31t gas-flow vessel for, 29, 31t membrane mixing vessel for, 29–30, 31t mixing vessel for, 29, 31t Heat flux calorimetric principle, 17–19, 18f, 19t parts of, 17 temperature equivalent formula for, 17 thermal contribution due to heat capacity formula for, 17 Heat flux microcalorimetry, DSC v., 19–25, 20f–25f calibration in, 23–25, 24f, 25f Calvet principle in, 22–23, 22f, 23f, 26 Heat-induced transformations oil-in-water emulsions with, 132–41, 135f, 136t, 137f, 139f, 139t, 140f anhydrous milk fat, 133–41, 135f, 136t, 137f, 139f, 139t, 140f, 184–87, 186f, 187f Avrami equation for, 138–39, 139f, 139t crystallization in, 132–36, 135f, 136t fat crystal growth in, 138 Gompertz model for, 139–40, 139t, 140f ice cream, 133 kinetics of, 136–41, 137f, 139f, 139t, 140f melting of fat droplets in, 132–36, 135f, 136t

Index triacylglycerols, 133 whipped cream, 133 peak temperatures and heat of reaction in, 128f protein solutions with, 119–32, 126f–128f, 130f, 131t, 132f, 141 activation enthalpy of, 129 denaturation-aggregation of globular proteins in, 124–29, 126f–128f entropy of, 129 kinetics of, 129–32, 130f, 131t, 132f Lumry-Eyring model for, 129, 130f, 131t protein structures in, 121–23 thermodynamics of, 123–24, 129–32, 130f, 131t, 132f whey protein isolate in, 125, 126f, 128f, 132f Heating mode, 35–40, 37f, 39f, 40f aggregation, 36–37, 37f crystallization, 36 denaturation, 36–37, 37f gelatinization, 38 gelation, 37–38 isothermal calorimetry, 38 isothermal crystallization, 39 oxidative stability, 38 retrogradation, 28t, 38 scanning calorimetry, 35–36 shelf life, 38, 39f step heating in calorimetry, 40, 40f Heat of combustion, parameters for food-processing design of, 225 Heat of solution, parameters for food-processing design of, 218–24, 221f–223f Heat release rate, critical, 354–55

381

Heat shocking, 148, 151 HHP. See High hydrostatic pressure processing High hydrostatic pressure processing (HHP), 9 food-processing treatment DSC evaluation with, 161 starch gelatinization by, 341–49, 343f, 346f starch gelatinization with, 12 wheat starch suspensions by, 344–47, 346f results with, 345–47, 346f sample preparation for, 345 High pressure calorimetry, 311–38, 313f–315f applications of, 324–37 frozen water ratio in gelatin gels, 326–29, 328f gelatinization of starch, 330–36, 332f–334f, 335t phase stability of lipid containing systems, 336–37, 337f pressure shift freezing, 329–30, 329f–331f water in pork muscle, 324–26, 326f, 327f calibration of, 314 calorimetric signal processing in, 313f calorimetric vessels in, 313f differential calorimetric detector in, 313f experimental setup of, 314f experimental vessel for, 315f high-pressure pump in, 313f hydraulic fluid reservoir in, 313f pressure detector in, 313f pressure-transmitting fluid with, 316 schematic diagram of, 313f

382

Index

High pressure differential scanning calorimetry (HP-DSC), 51–64, 54f–56f, 60f, 62f advantages of, 63–64 applications of, 63 autoclave of, 54, 54f, 55f, 57 availability of, 63 calibration of, 57–63, 60f, 62f heat calibration procedure for, 60f, 61–63, 62f necessity for, 57 parameters for, 58 reference substances for, 58 table of corrections for, 57 temperature calibration procedure for, 58–61 uncertainty with, 58 ceramic housing of, 54, 55f construction of, 53–57, 54f–56f danger with, 54 differential thermal analysis with, 53 disadvantages of, 64 fluid medium as limit to, 52 furnace of, 54, 56, 56f operating temperature range of, 56 power compensation principle with, 53 setup of, 54f spindle pump of, 54, 54f High-sensitivity calorimetry, food-processing safety with, 361 High sensitivity heat flux calorimeter, 26–27, 27f, 28t batch mixing vessel for, 27, 28t fluid mixing vessel for, 27, 28t mixing vessels for, 27, 27f temperature control in, 26 thermal conductive block of, 26 Hot spots

dryers with, 364 drying and, 13 HP-DSC. See High pressure differential scanning calorimetry Hydrocolloid C80 technique v. vessel/heating mode with, 31t DSC technique v. vessel/heating mode with, 28t endothermic/exothermic effects of, 19t IC. See Isothermal calorimetry Ice cream heat capacity for, 36t heat-induced transformations with, 133 ICTAC. See International Confederation for Thermal Analysis and Calorimetry Indium, HP-DSC calibration using, 58, 60, 61 Initial calorimetric signal θ0, calculation of, 252 International Confederation for Thermal Analysis and Calorimetry (ICTAC), 20 Interpolyelectrolyte complex formation, 107 Isoconversional methods, foodprocessing safety with, 360–61 Isothermal calorimetry (IC), 265 heating mode with, 38 shelf life analysis with, 237–61 calculation for rate constant, 254–55 calculation for reaction enthalpy, 255 calculation for reaction halflife, 254

Index calculation for reaction order, 252–53 calculation for total heat released, 253–54 calculation of initial calorimetric signal θ0, 252 calculation of QT, 256–60, 260f determination of K, 255–56 empirical model fitting for, 246–49, 246f, 249f, 250f qualitative studies on, 239–45, 241f quantitative studies on, 245 reaction kinetics based model of, 249–51 reactions that proceed to completion for, 252–55 reactions that proceed to equilibrium for, 255–60, 260f test for complete reaction, 255 Isothermal crystallization, heating mode with, 39 Isothermal solution calorimetry, 220 Isothermal titration calorimetry (ITC), 9–10 dependence of model with, 82 heat of interaction measured with, 84 protein in dilute solution with, 68, 77–84, 80f, 83f binding processes in, 78, 79, 80f data analysis for, 79–82 information content, 77–78 instrumentation, 78–79, 80f power compensation design in, 78 range of applicability with, 82–83, 83f shape of titration curve with, 82–83, 83f

383

ITC. See Isothermal titration calorimetry Joule effect, 23–24, 24f, 25f, 32 K, determination of, 255–56 KCl, salt-protein interaction with, 98, 99t KI. See Kunitz inhibitor Kirchhoff ’s law, 93, 96, 100 Kosmotropic salts (Salting-out salts), 95 Kunitz inhibitor (KI), 94 interpolyelectrolyte complex formation’s effects on, 107 polysaccharides’ effect on protein denaturation with, 107–10, 108f dextran sulfate with, 108f soybean seeds with, 107 Lactobacillus plantarum, DSC analysis of, 155–58, 157f β-lactoglobulin κ-carrageenan with, 110 λ-carrageenan with, 110 denaturation of, 125 milk protein with, 94–95 porcine, 95 salt’s effect on protein denaturation using, 96–98, 97f, 99t Lactose glass transition behavior of, 300f state diagrams of, 304f Lard crystallization in, 190, 191f, 191t DSC and XRD with, 190–93, 191f, 192f DSC curves of, 191f SAXS of, 191t, 192f WAXS of, 191t, 192f

384

Index

Lauric acid melting point of, 171f MICROCALIX calibration with, 177 Lead, HP-DSC calibration using, 58 Linoleic acid, melting point of, 171f Linolenic acid, melting point of, 171f, 172f Lipids, 169. See also Triacylglycerols antioxidant efficacy of, 212 crystallization kinetics of, 210–11 emulsifier-water systems with, 212–14 emulsions, 214 melting profile of, 209 oxidative stability of, 211–12 polymorphism of, 209–10, 210f quality control for, 211 thermal behavior of food constituents in, 208–14, 210f, 213f Liquids, heat capacity determination for, 34–35, 34f Listeria monocytogenes antibiotics’ effect on, 153–54, 153f DSC analysis of, 149–53, 151f heat inactivation parameters of, 159 results for, 152–53, 152f sample preparations for, 149–50 Lumry-Eyring model, 104 protein heat-induced transformations with, 129, 130f, 131t Lyophilization, DSC technique v. vessel/heating mode with, 28t Maltodextrin (MD), moisture content of, 222–24, 223f

MASC. See Modulated adiabatic scanning calorimetry MD. See Maltodextrin MDSC. See Modulated DSC Meat, heat capacity for, 36t Membrane mixing vessel, mixing and reaction heat flux microcalorimeter with, 29–30, 31t Methyl cellulose, denaturation temperature of 11S globulin with, 106t MicroCal, 21 MICROCALIX, 170, 176–79, 177f, 180f, 186f, 187f, 189f, 191f, 192f cocoa butter in, 182–84, 183f experimental setup of, 177f experiments using, 179 laboratory with conventional x-ray source and, 177–78 lauric acid for calibration of, 177 synchrotron radiation XRD bench with, 178–79 temperature-controlled cryostat for, 177f temperature controller for, 177f Microcalorimetry DSC v., 16, 19–25, 20f–25f calibration in, 23–25, 24f, 25f Calvet principle in, 22–23, 22f, 23f, 26 heat flux, 16, 19–25, 20f–25f methods of, 30–45, 33f, 34f, 36t, 37f–44f controlled relative humidity in, 45 heat capacity determination, 30–35, 33f, 34f, 36t heating mode in, 35–40, 37f, 39f, 40f

Index mixing and reaction calorimetry, 40–43, 41f–43f pressure calorimetry, 43–45, 44f Milk bovine β-lactoglobulin of, 94–95 DSC technique v. vessel/heating mode with, 28t endothermic/exothermic effects of, 19t heat capacity for, 36t isothermal traces at temperatures for, 39f protein, 94–95, 131 skim milk powder, 222–24, 223f Milk fat anhydrous, 133–41, 135f, 136t, 137f, 139f, 139t, 140f, 184–87, 186f, 187f crystallization properties of, 189–90 DSC and XRD with, 184–90, 186f, 187f, 189f globules, 188–89, 189f Mixing and reaction calorimetry, 40–43, 41f–43f batch mixing in, 40 binding, 41–42 dissolution, 41 enzymatic reactions, 42, 42f, 43f fermentation, 43 flow mixing in, 41 neutralization, 41, 41f solubility, 41 Mixing and reaction heat flux microcalorimeter, 29–30, 31t ampoule mixing vessel for, 30, 31t batch high-pressure vessel for, 29, 31t batch standard vessel for, 29, 31t gas-flow vessel for, 29, 31t

385

membrane mixing vessel for, 29–30, 31t mixing vessel for, 29, 31t Mixing vessel, mixing and reaction heat flux microcalorimeter with, 29, 31t Modulated adiabatic scanning calorimetry (MASC), 265 Modulated DSC (MDSC), foodprocessing design with, 204 Moisture content maltodextrin, 222–24, 223f skim milk powder, 222–24, 223f thermodynamic response with, 219–20 Moisture loss, calorimetry with, 8 Molasses incident, 365 Mycoplasma laidlawii, DSC analysis of, 149 Myristic acid, melting point of, 171f NaCl denaturation temperature of 11S globulin with, 106t salt-protein interaction with, 98, 99t Neutralization, mixing and reaction calorimetry with, 41, 41f (NH4)2SO4, salt-protein interaction with, 98, 99t NMR. See Nuclear magnetic resonance Nuclear magnetic resonance (NMR), 301 Odorants food flavorings with, 102 protein denaturation effected by, 102–4, 103f BSA in, 104 Lumry-Eyring model in, 104 ovalbumin in, 102–4, 103f

386

Index

Oersted law, 25 Oil. See also Lipids C80 technique v. vessel/heating mode with, 31t DSC technique v. vessel/heating mode with, 28t endothermic/exothermic effects of, 19t oxidative stability of, 211–12 Oil-in-water emulsions, 132–41, 135f, 136t, 137f, 139f, 139t, 140f anhydrous milk fat, 133–41, 135f, 136t, 137f, 139f, 139t, 140f, 184–87, 186f, 187f Avrami equation for, 138–39, 139f, 139t crystallization in, 132–36, 135f, 136t fat crystal growth in, 138 Gompertz model for, 139–40, 139t, 140f ice cream, 133 kinetics of, 136–41, 137f, 139f, 139t, 140f melting of fat droplets in, 132–36, 135f, 136t triacylglycerols, 133 whipped cream, 133 Oleic acid, melting point of, 171f One-cell calorimetric principle, 18f Open measurement method, 357, 357f, 358f Orange juice, heat capacity for, 36t Ovalbumin, protein denaturation effected by, 102–4, 103f Overlapping peaks, interpretation of, 8 Oxidative stability, heating mode with, 38

Oxygen, reactions with, 363–64 determination of self-ignition temperature for, 364 screening test for, 363 Palmitic acid, melting point of, 171f, 172f Parameter Be, salt-protein interaction with, 98, 99t Pectin, denaturation temperature of 11S globulin with, 106t pH denaturation temperature of 11S globulin with, 106t 7S globulin denaturation with different values of, 93 11S globulin denaturation with different values of, 91f, 92 protein denaturation affected by, 89–95, 91f RBPC denaturation with different values of, 92 Phaseolin, 93 Phase transitions, food dehydration in, 290, 292–93, 293f Polypeptide chains, 121 Polysaccharides denaturation temperature of 11S globulin with, 106t protein denaturation affected by, 104–10, 106t, 108f 11S globulin in, 105–6, 106t, 110 Kunitz inhibitor in, 107–10, 108f protein thermodynamic incompatibility with, 109–10 Pork heat capacity for, 36t muscle heat flux of crystallization for, 327f

Index thawing heat flux of, 326f water in, 324–26, 326f, 327f Postdenaturation aggregation aggregation rate determined by denaturation rate with, 111 estimate of apparent denaturation enthalpy with, 112 irreversible, 110 kinetic parameters of, 111 of protein, 110–12 reversible, 110 Potato, heat capacity for, 36t Power compensation principle, 53 Pressure calorimetry, 43–45, 44f Pressure shift freezing (PSF) basic procedure of, 329f heat flux during, 330f high pressure calorimetry with, 329–30, 329f–331f ice crystal/sample mass ratio formed during, 331f pressure during, 330f temperature during, 330f Propylene glycol, denaturation temperature of β-lactoglobulin with, 110 Protein 20 amino acids constituting, 120 behavior upon heating of, 89 bovine β-lactoglobulin of milk, 94–95 calorimetry of dilute solution of, 67–84, 71f, 80f, 83f cold denaturation with, 75 DSC data quantifies highaffinity binding with, 75–77 DSC for, 68–77, 71f ITC for, 68, 77–84, 80f, 83f cereal with, 12 conformation stability of, 121 DSC technique v. vessel/heating mode with, 28t

387 emulsions/foams, role in, 10 free, 106t heat-induced transformations in solutions of, 119–32, 126f–128f, 130f, 131t, 132f, 141 denaturation-aggregation of globular proteins with, 124–29, 126f–128f kinetics of, 129–32, 130f, 131t, 132f protein structures with, 121–23 thermodynamics of, 123–24, 129–32, 130f, 131t, 132f hydrophilic component of, 291 milk, 94–95, 131 polysaccharides’ thermodynamic incompatibility with, 109–10 postdenaturation aggregation of, 110–12 structures, 121–23 caseins, 122–23 gelatin molecules, 122 polypeptide chains, 121 secondary structures, 121 tertiary structures, 121–22 thermal analysis of cereal processing with, 272–76, 274f, 275f gluten fix of water molecules for, 273 soluble in aqueous media for, 273 starch gelatinization with, 274 thermal behavior of food constituents in, 214–16, 215f, 216f thermal denaturation of, 87–113, 91f, 97f, 99t, 101f, 103f, 106t, 108f effects of alcohols on, 99–100, 101f

388

Index

Protein (continued) effects of odorants on, 102–4, 103f effects of pH on, 89–95, 91f effects of polysaccharides on, 104–10, 106t, 108f effects of salts on, 95–99, 97f, 99t reversibility of, 123 two-state model of, 123 thermodynamic compatibility of denatured/native, 89 ultrasensitive calorimetry to, 8 Protein-protein interactions (Exothermic reaction), 127–28 PSF. See Pressure shift freezing QT, calculation of, 256–60, 260f Rate constant, calculation for, 254–55 RBPC. See Ribulose 1,5 biphosphate carboxylase Reaction enthalpy, calculation for, 255 Reaction half-life, calculation for, 254 Reaction kinetics, model of based on, 249–51 Reaction order, calculation for, 252–53 Retrogradation DSC technique v. vessel/heating mode with, 28t heating mode with, 28t, 38 starch, 270 Ribulose 1,5 biphosphate carboxylase (RBPC), different pH values in denaturation of, 93

Saccharides, dissolution behavior of, 219 Safety. See Food-processing safety; Transport safety Salmon, heat capacity for, 36t Salting-in salts. See Chaotropic salts Salting-out salts. See Kosmotropic salts Salts C80 technique v. vessel/heating mode with, 31t chaotropic, 95 kosmotropic, 95 protein denaturation affected by, 95–99, 97f, 99t Debye-Höckel approximation for, 98 β-lactoglobulin, 96–98, 97f, 99t Scanning mode, 35–36, 44. See also Temperature-scanning mode heating curves for different rates of, 127f Scanning transitiometry, 311–38, 317f–319f, 321f, 323f, 324f applications of, 324–37 frozen water ratio in gelatin gels, 326–29, 328f gelatinization of starch, 330–36, 332f–334f, 335t phase stability of lipid containing systems, 336–37, 337f pressure shift freezing, 329–30, 329f–331f water in pork muscle, 324–26, 326f, 327f benzene as verification test for, 322, 323f calibration of, 322–23 calorimetric vessels in, 319–20 piston pump in, 320

Index precautions for, 321–22 pressure detector in, 320 schematic diagram of, 319f scheme of basic principles of, 317f temperature and energy scales of, 322 thermodynamic scheme of, 318f transitiometric vessels for, 321f Self-ignition temperature, 364 Setschenow equation, 100 SFC. See Solid fat content Shelf life, 237–61 empirical model fitting for analysis of, 246–49, 246f, 249f, 250f heating mode with, 38, 39f qualitative studies on, 239–45, 241f quantitative studies on, 245 reaction kinetics based model of, 249–51 reactions that proceed to completion for analysis of, 252–55 calculation for rate constant, 254–55 calculation for reaction enthalpy, 255 calculation for reaction halflife, 254 calculation for reaction order, 252–53 calculation for total heat released, 253–54 calculation of initial calorimetric signal θ0, 252 reactions that proceed to equilibrium in analysis of, 255–60, 260f calculation of QT, 256–60, 260f determination of K, 255–56

389

test for complete reaction, 255 Skim milk powder (SMP), moisture content of, 222–24, 223f Small-angle X-ray diffraction (SXRD), 176 lard in, 191t, 192f SMP. See Skim milk powder Sodium alginate, denaturation temperature of 11S globulin with, 106t Solid fat content (SFC), 209–10 Solubility, mixing and reaction calorimetry with, 41 Solution calorimetry, 218 Specific heat moisture content’s effect on thermodynamic response with, 219–20 parameters for food-processing design of, 224–25 pharmaceutical substances, 219 solution calorimetry with, 218 Spindle pump, HP-DSC, 54f Spray-drying, 290, 305–6, 306f Staphylococcus aureus hydrostatic pressure resistance of, 44, 44f nonthermal treatment of, 162–63, 162t Starch C80 technique v. vessel/heating mode with, 31t cereal with, 12 DSC technique v. vessel/heating mode with, 28t endothermic/exothermic effects of, 19t gelatinization, 12, 274, 278–79, 280f calorimetric analysis by HPP of, 341–49, 343f, 346f heat in, 342, 343f

390

Index

Starch (continued) high pressure calorimetry on, 330–36, 332f–334f, 335t storage of, 347–48 thermodynamic data for, 335t wheat, 344–47, 346f HHP’s effects on, 12 retrogradation, 270 thermal analysis of cereal processing with, 268–72, 289f–272f aqueous suspension of starch granules for, 268 DSC for, 268–72, 289f–272f State diagrams food dehydration in, 303–7, 304f freeze-drying, 290, 306–7 lactose, 304f spray-drying, 290, 305–6, 306f State transitions, food dehydration in, 290, 292–93, 293f Stearic acid, melting point of, 171f, 172f Step heating, calorimetry with, 40, 40f Sucrose, calorimetric curves of, 207f, 208f Sugar C80 technique v. vessel/heating mode with, 31t calorimetric curves of sucrose, 207f, 208f DSC technique v. vessel/heating mode with, 28t glass transition with, 294 thermal behavior of food constituents in, 206–8, 207f, 208f SXRD. See Small-angle X-ray diffraction TA. See Thermal analysis

TCC. See Temperature-controlled cryostat Temperature, critical, 355–56, 355f Temperature calibration procedure, HP-DSC, 58–61 Temperature-controlled cryostat (TCC), 177f Temperature modulated DSC (TMDSC), 265 Temperature-scanning mode blank test heat flow equation, 32 heat capacity determination using, 31–33, 33f Temperature step mode, heat capacity determination using, 33–34 TG. See Triacylglycerols TGA. See Thermogravimetry Thermal analysis (TA). See also Differential thermal analysis; Dynamical mechanical thermal analysis design/monitor of cereal processing, 265–85 nonstarch carbohydrates, 276–78, 277f process applications, 278–85, 280f–284f proteins, 272–76, 274f, 275f starch, 268–72, 289f–272f food dehydration understood with, 298–301, 299f, 300f food-processing design with, 203–6, 204f methods, 205–6 samples, 206 techniques, 203–5, 204f Thermogravimetry (TGA), 265 Time scale, calorimetry with, 8 Tin, HP-DSC calibration using, 58, 59

Index TMDSC. See Temperature modulated DSC Total heat released, calculation for, 253–54 Transitiometry scanning technique, 12 Transition state theory, 111 Transport safety, 365–66 Triacylglycerols (TG) composition of, 169 DSC and XRD in study of, 169–76, 171t, 172t, 173f fatty acids, 170–73, 171t, 172t, 173f crystallographic/energetic properties of, 172f hexagonal, 172, 172f, 173f melting point of, 171f, 172f orthorhombic perpendicular, 172, 172f, 173f triclinic parallel, 172, 172f, 173f heat-induced transformations with, 133 main types of, 173f melting profile of, 209 polymorphism of, 170–73, 171t, 172t, 173f, 209–10, 210f Trypsin inhibitor, 94 Vanillin, 102 Van’t Hoff enthalpy change, DSC measured, 72–74 Vegetable, heat capacity for, 36t Water. See also Oil-in-water emulsions emulsifier-water systems with lipids, 212–14 frozen water ratio in gelatin gels, 326–29, 328f gluten fix with molecules of, 273

391

pork muscle with, 324–26, 326f, 327f starch-water systems, 207 thermal behavior of food constituents in, 216–17 Wheat, starch gelatinization by HHP for, 344–47, 346f results with, 345–47, 346f sample preparation for, 345 Whey protein isolate, heat-induced transformations with, 125, 126f, 128f, 132f Whipped cream, heat-induced transformations with, 133 Wide-angle X-ray diffraction (WXRD), 175 lard in, 191t, 192f WXRD. See Wide-angle X-ray diffraction Xanthan, denaturation temperature of β-lactoglobulin with, 110 X-ray diffraction (XRD), 11 applications with DSC and, 179–93, 180f, 183f, 186f, 187f, 189f, 191f, 192f cocoa butter in DSC and, 179–84, 180f, 183f DSC with, 169–94, 171t, 172t, 173f, 177f, 180f, 183f, 186f, 187f, 189f, 191f, 192f food-processing design with DSC and, 225 lard in DSC and, 190–93, 191f, 192f MICROCALIX using DSC and, 170, 176–79, 177f milk fat in DSC and, 184–90, 186f, 187f, 189f

392

Index

X-ray diffraction (XRD) (continued) results using DSC and, 179–93, 180f, 183f, 186f, 187f, 189f, 191f, 192f triacylglycerols in DSC and, 169–76, 171t, 172t, 173f X-ray diffraction with temperature function (XRDT), 11, 176 X-ray diffraction with time function (XRDt), 11, 176 XRD. See X-ray diffraction XRDT. See X-ray diffraction with temperature function XRDt. See X-ray diffraction with time function

Yeast C80 technique v. vessel/heating mode with, 31t DSC technique v. vessel/heating mode with, 28t endothermic/exothermic effects of, 19t Yogurt processing, DSC technique v. vessel/heating mode with, 28t Young’s modulus E, 265 Zinc, HP-DSC calibration using, 58