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The development of time-temperature indicators for microwave assisted pasteurization processes

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THE DEVELOPMENT OF TIME-TEMPERATURE INDICATORS FOR MICROWAVE
ASSISTED PASTEURIZATION PROCESSES
By
WENJIA ZHANG
A dissertation submitted in partial fulfillment of
the requirements for the degree of
DOCTOR OF PHILOSOPHY
WASHINGTON STATE UNIVERSITY
Department of Biological Systems Engineering
MAY 2014
UMI Number: 3628901
All rights reserved
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a note will indicate the deletion.
UMI 3628901
Published by ProQuest LLC (2014). Copyright in the Dissertation held by the Author.
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To the Faculty of Washington State University:
The members of the Committee appointed to examine the dissertation of WENJIA ZHANG
find it satisfactory and recommend that it be accepted.
____________________________________
Juming Tang, Ph.D., Chair
____________________________________
Shyam Sablani, Ph.D.
____________________________________
Barbara Rasco, Ph.D.
ii
ACKNOWLEGEMENT
My study at Washington State University was rewarding because of the tremendous support
and encouragement from my doctoral committee, colleagues, friends, and family.
Firstly, I would like to thank my advisor, Dr. Juming Tang, for his mentoring and guidance
during my study. His passion and deep insight in research, and his positive attitude towards life
have always made him a role model for me. I really appreciate the opportunities he provided for
me to explore the world of microwave processing. I also would like to thank my doctoral
committee members, Dr. Shyam Sablani and Dr. Barbara Rasco, for their advice and suggestions
for my research and dissertation writing. This dissertation could not have been completed
without their help. I also thank Dr. Gustavo Barbosa for his kind guidance during my PhD study.
I thank Dr. Fang Liu for his patient guidance and help in conducting all the microwave tests.
I also would like to thank Mr. Stewart Bohnet and Ms. Huimin Lin for their help on my lab work
and their support and encouragement during my study. I thank Dr. Zhongwei Tang and Mr. Feng
Li also for their help in conducting the tests.
My colleagues and friends from the Food Engineering Club have always been helpful,
supportive, and encouraging. I thank earlier FEC graduates Dr. Ofero Caparino, Dr. Roopesh
Syamaladevi, Dr. Shunshan Jiao, and Dr. Sumeet Dhawan for their suggestions and help on my
research and professional development. Thanks Dr. Jing Peng, Yang Jiao, Donglei Luan,
Rossana Villa-Rose, Hongchao Zhang, Kanishka Bhunia, and all other FEC members for their
friendship which made my PhD life so memorable. Thanks to my dear officemate, Ellen
Bornhorst, who has always shared her positive energy to me.
iii
I would also like to thank my dearest friends, Xiaojun Yang, Rui Shi, Linyan Zhou, Bin
Jiang, and Yuanyuan Zhang, for their long lasting support and friendship, which helped me go
through my ups and downs. Special thanks to Bob and Colleen Harvey, Bernie and Paige Van
Vie, for their spiritual leadership and family-like love to me during the past four years in
Pullman.
My sincere and deepest love and thanks to my family and husband. I thank my father
Enyang Zhang, and my mother Liping Yang, for bringing me to this wonderful world and family.
I am able to come this far only because of their selfless love and support for all my life. I also
thank them for traveling overseas and staying with me for the past few months when my husband
was far away in Iowa State. Without them I could not easily bear the pressure of preparing for
graduation and being pregnant. I thank my beloved husband Dr. Shuai Zhou, who has always
been loving, supportive, understanding, and encouraging. My life has changed so much since our
marriage.
iv
THE DEVELOPMENT OF TIME-TEMPERATURE INDICATORS FOR MICROWAVE
ASSISTED PASTEURIZATION PROCESSES
Abstract
by Wenjia Zhang, Ph.D.
Washington State University
May 2014
Chair: Juming Tang
A pilot plant scale 915 MHz single-mode microwave assisted pasteurization (MAP) system
was developed at Washington State University for prepackaged food products. Methods to
determine the heating patterns inside the food products in this system are needed to develop
appropriate thermal processes. Such methods are also necessary to validate computer simulation
methods for prediction of interactions between the electromagnetic field within the microwave
cavities and the food packages. The main goal of my dissertation is to develop two
complementary time-temperature indicating methods to directly map the heating patterns and
help visualizing the cold and hot spots in MAP systems for in-package pasteurization
applications.
A time temperature indicating system normally consists of a model food serving as a
chemical marker carrier and chemical marker precursors. Two models, the egg white and whole
egg gel models and the gellan gel models, were selected for this study, based on their relevant
physical properties including dielectric properties, gelation temperature, gel strength, and water
holding capacities. Both models could be formulated into homogeneous gel models with
different solid and salt contents to provide a wide range of dielectric properties to model various
food products. Depending on their gelation temperatures, the egg white, whole egg, and gellan
v
gel models could be used for heating pattern determination in microwave processes at
temperatures higher than 70, 80, and 65 °C, respectively.
D-ribose and L-lysine were added to the model foods as chemical marker precursors. The
formation of the possible chemical marker, and the color change of the model foods were studied
using oil/water baths at pasteurization temperatures. Chemical marker M2 (4-hydroxy-5-methyl3(2H)-furanone) and an unidentified substance(s) with the maximum UV absorbance peak at 268
nm were suitable in the egg white and gellan gel model food, respectively. The heating patterns
determined by both systems were accurate based on the temperature monitored at the
predetermined cold and hot spots. With the development of both time-temperature indicators, the
temperatures at cold and hot spots during food processing could be monitored for proper process
development, and for the improvement of the uniformity of the MAP system.
vi
DEDICATION
This dissertation is dedicated to my beloved parents and my husband for their love and support.
vii
TABLE OF CONTENTS
CHAPTER ONE ........................................................................................................................... 1
INTRODUCTION .......................................................................................................................... 1
1. MICROWAVE FOOD PROCESSING ............................................................................................... 1
2. MICROWAVE STERILIZATION AND PASTEURIZATION ................................................................. 2
3. NON-UNIFORM HEATING AND DEVELOPMENT OF CHEMICAL MARKER METHODS ...................... 3
4. MAJOR OBJECTIVES ................................................................................................................. 8
5. DISSERTATION OUTLINES ....................................................................................................... 10
REFERENCES .............................................................................................................................. 13
CHAPTER TWO ........................................................................................................................ 16
LITERATURE REVIEW ............................................................................................................. 16
1. MODEL FOOD ......................................................................................................................... 16
1.1 Gellan gel ........................................................................................................................ 17
1.2. Egg proteins .................................................................................................................... 21
2. DIELECTRIC PROPERTIES OF THE MODEL FOODS...................................................................... 21
3. GELATION TEMPERATURES OF MODEL FOODS ......................................................................... 24
4. OTHER PHYSICAL PROPERTIES OF THE MODEL FOODS ............................................................. 25
5. CHEMICAL MARKERS .............................................................................................................. 26
6. COMPUTER VISION METHOD ................................................................................................... 30
REFERENCES .............................................................................................................................. 32
CHAPTER THREE .................................................................................................................... 35
viii
PHYSICAL PROPERTIES OF EGG WHITES AND WHOLE EGGS RELEVANT TO
MICROWAVE PASTEURIZATION .......................................................................................... 35
ABSTRACT .................................................................................................................................. 35
1. INTRODUCTION ....................................................................................................................... 36
2. MATERIALS AND METHODS .................................................................................................... 39
2.1. Sample preparation ......................................................................................................... 39
2.2. Dielectric properties ....................................................................................................... 40
2.3. Penetration depth ............................................................................................................ 41
2.4. Electrical conductivity .................................................................................................... 41
2.5. Gelation temperature ...................................................................................................... 42
2.6. Gel strength..................................................................................................................... 43
2.7. Water holding capacity (WHC) ...................................................................................... 43
2.8. Data analysis ................................................................................................................... 44
3. RESULTS AND DISCUSSION ...................................................................................................... 44
3.1. Dielectric properties ....................................................................................................... 44
3.2. Gelation temperature ...................................................................................................... 54
3.3. Gel strength and water holding capacity (WHC) ........................................................... 55
4. CONCLUSIONS ........................................................................................................................ 57
REFERENCES .............................................................................................................................. 58
CHAPTER FOUR ....................................................................................................................... 63
CHEMICAL MARKER M2 (4-HYDROXY-5-METHYL-3(2H)-FURANONE) FORMATION
IN EGG WHITE GEL MODEL FOR HEATING PATTERN DETERMINATION OF
MICROWAVE-ASSISTED PASTEURIZATION PROCESSING ............................................. 63
ix
ABSTRACT .................................................................................................................................. 63
1. INTRODUCTION ....................................................................................................................... 64
2. MATERIALS AND METHODS .................................................................................................... 67
2.1. Sample preparation ......................................................................................................... 67
2.2. Oil bath treatment ........................................................................................................... 68
2.3. Marker yield analysis using HPLC ................................................................................. 69
2.4. Color value determination .............................................................................................. 70
2.5. Effect of salt addition on M2 yield ................................................................................. 72
2.6. Storage stability of M2 ................................................................................................... 72
2.7. Microwave-assisted pasteurization (MAP) treatment .................................................... 73
2.8. Heating pattern analysis for the MAP processed gel models ......................................... 74
2.9. Statistical analysis........................................................................................................... 74
3. RESULTS AND DISCUSSION ...................................................................................................... 74
3.1. M2 formation .................................................................................................................. 74
3.2. Correlation between color parameters and temperature ................................................. 76
3.3. Effect of salt on M2 yield ............................................................................................... 76
3.4. Stability of M2 during storage ........................................................................................ 77
3.5. Validation of M2 application in the MAP process ......................................................... 80
4. CONCLUSIONS ........................................................................................................................ 84
REFERENCES .............................................................................................................................. 85
CHAPTER FIVE ........................................................................................................................ 88
PHYSICAL PROPERTIES OF LOW-ACYL GELLAN GEL AS RELEVANT TO
MICROWAVE ASSISTED PASTEURIZATION PROCESS .................................................... 88
x
ABSTRACT .................................................................................................................................. 88
1. INTRODUCTION ....................................................................................................................... 89
2. MATERIALS AND METHODS .................................................................................................... 93
2.1 Sample preparation .......................................................................................................... 93
2.2 Dielectric properties measurement .................................................................................. 94
2.3 Penetration depth ............................................................................................................. 94
2.4 Gel strength measurement ............................................................................................... 95
2.5 Measurement of water holding capacity (WHC) ............................................................. 96
2.6 Statistical analysis............................................................................................................ 96
3. RESULTS AND DISCUSSION ...................................................................................................... 97
3.1 Combined effect of frequency and temperature on dielectric properties ........................ 97
3.2 Effect of sucrose content on the dielectric properties...................................................... 98
3.3 Effect of salt content on the dielectric properties .......................................................... 102
3.4 Effects of sucrose and salt contents on penetration depth ............................................. 104
3.5 Combined effects of sucrose and salt contents on gel strength ..................................... 106
3.6 Combined effect of sucrose and salt content on water holding capacity....................... 109
4. CONCLUSIONS ...................................................................................................................... 110
REFERENCES ............................................................................................................................ 112
CHAPTER SIX ......................................................................................................................... 116
HEATING PATTERN DETERMINATION USING A GELLAN GEL MODEL FOR
MICROWAVE PASTEURIZATION PROCESSES ................................................................. 116
ABSTRACT ................................................................................................................................ 116
1. INTRODUCTION ..................................................................................................................... 117
xi
2. MATERIALS AND METHODS .................................................................................................. 120
2.1 Model solution preparation ............................................................................................ 120
2.2 UV detection .................................................................................................................. 121
2.3 HPLC measurements for storage stability of the possible marker(s) ............................ 122
2.4. Color value measurements............................................................................................ 123
2.5. Heating pattern validation using microwave process ................................................... 124
3. RESULTS AND DISCUSSION .................................................................................................... 125
3.1 UV absorbance .............................................................................................................. 125
3.2 HPLC analysis of S268 and M-2 ..................................................................................... 129
3.3 Storage stability of S268 and M2 .................................................................................... 130
3.4 Correlation between color values, UV absorbance, and heat treatment temperature .... 133
3.5 Validation of the model system using MAP process ..................................................... 134
4. CONCLUSION ........................................................................................................................ 137
REFERENCES ............................................................................................................................ 139
CHAPTER SEVEN ................................................................................................................... 141
CONCLUSIONS, CONTRIBUTION TO KNOWLEDGE AND RECOMMENDATIONS .... 141
1. MAJOR CONCLUSIONS .......................................................................................................... 141
2. CONTRIBUTION TO KNOWLEDGE ........................................................................................... 142
3. RECOMMENDATIONS ............................................................................................................ 143
xii
LIST OF FIGURES
CHAPTER ONE
Fig.1.1 Principle of computer vision system (Adapted from Pandit et al. 2007) ........................... 7
Fig.1.2 Cold spot identification by (a) Chemical marker and computer vision method, (b)
Computer simulation method, and (c) Temperature measurements for cold and hot spots
validation using fiber optic sensors (Adapted from Resurreccion, 2013) ...................................... 7
Fig.1.3 Project structure for development of time-temperature indicators for microwave
pasteurization process ................................................................................................................... 10
CHAPTER TWO
Fig.2.1 The chemical structure of the repeating units of high acyl (a) and low acyl (b) gellan gum
(adapted from Mao et al., 2000).................................................................................................... 18
Fig.2.2 Models for gellan gelation proposed by (a) Robinson et al. (1991) and (b) Gunning and
Morris (1990) (Adapted from Morris et al., 2012) ....................................................................... 20
Fig.2.3 Reaction pathways of the formation of Melanoidins from Aldose sugars and amino
compounds (Adapted from Hodge, 1953). ................................................................................... 27
Fig.2.4 Reaction pathways for the formation of chemical marker M1 (a), M2 (b), and M3 (a)
(Adapted from Kim, 1996) ........................................................................................................... 29
Fig.2.5 Components of a computer vision system (adapted from Pandit, 2006) .......................... 31
CHAPTER THREE
Fig.3.1 Schematic diagram of the DP measurement system (adapted from Guan et al., 2004) ... 41
Fig.3.2 Effects of frequency and temperature on the dielectric properties of liquid egg white and
whole egg samples (25% solid concentration, wb) (a) Dielectric constant of egg white; (b)
xiii
Dielectric loss factor of egg white; (c) Dielectric constant of whole egg; (d) Dielectric loss factor
of whole egg. ................................................................................................................................. 47
Fig.3.3 Effect of solid concentration (wb) on the dielectric properties of liquid egg white samples
at 915 MHz (a) Dielectric constant (b) Dielectric loss factor ....................................................... 49
Fig.3.4 Effect of salt content on the dielectric constant of liquid egg white (a) and whole egg (b)
samples (25% solid concentration, wb) at 915 MHz .................................................................... 50
Fig.3.5 Dielectric loss factor of liquid egg white and whole egg samples (25% solid
concentration, wb) with different salt additions at 915 MHz ....................................................... 51
Fig.3.6 Effect of salt addition on the electrical conductivities of liquid egg white and whole egg
samples (25% solid concentration, wb) at 22 °C .......................................................................... 51
Fig.3.7 Penetration depth of 915 MHz microwave in liquid egg white and whole egg samples
(25% solid concentration, wb) ...................................................................................................... 53
Fig.3.8 Changes of storage modulus (G′) of liquid egg white and whole egg samples (25% solid
concentration, wb) with different salt additions during heating ................................................... 55
Fig.3.9 Effect of solid concentration on the gel strength (a) and WHC (b) of egg white and whole
egg gels without salt addition ....................................................................................................... 56
CHAPTER FOUR
Fig.4.1 Schematic diagram of the custom-built aluminum thermal kinetics test (TKT) cell ....... 68
Fig.4.2 Standard curve of chemical marker M2 (4-hydroxy-5-methyl-3(2H)-furanone) in 10 mM
sulfuric acid buffer ........................................................................................................................ 70
Fig.4.3 Components of the computer vision system for sample color and heating pattern analysis
....................................................................................................................................................... 71
xiv
Fig.4.4 Chemical marker M2 yield in egg white gel models heat-treated at various pasteurization
temperatures (n=4) ........................................................................................................................ 75
Fig.4.5 Effect of salt addition on the M2 yield in egg white gel model when heated in 90 °C
water bath for 0, 10, 20, and 30 min (n=4) ................................................................................... 77
Fig.4.6 Retention rate of chemical marker M2 (extracted from egg white gel model samples
treated at 85 °C for various times) during storage at 4 °C (a) and 22 °C (b) for 1, 3, 5, and 9 days
(n=4).............................................................................................................................................. 79
Fig.4.7 Heating pattern results of egg white gel models (middle layer) with chemical marker M2
after microwave-assisted pasteurization processing in 915 MHz single mode microwave system
at 75 °C and 100 °C (Location number 1: Blue, 2: Aqua, 3: Green, 4: Yellow, 5: Red) ............. 80
Fig.4.8 Chemical marker M2 yield (a) and color parameters L* (b) and G values (c) of egg white
gel models after microwave-assisted pasteurization process at 75 °C (n=4) ................................ 82
Fig.4.9 Correlation between chemical marker M2 yield and color parameters G and L* values of
egg white gel model after microwave-assisted pasteurization process at 100 °C (n=4)............... 83
CHAPTER FIVE
Fig.5.1 Effect of frequency and temperature on the dielectric constant (a) and loss factor (b) of
1% gellan gel with 6 mM Ca2+ addition ....................................................................................... 98
Fig.5.2 Effect of frequency and temperature on the dielectric constant (a) and loss factor (b) of
1% gellan gel with 6 mM Ca2+ and 30% sucrose addition ........................................................... 99
Fig.5.3 Effect of sucrose content on the dielectric constant (a) and loss factor (b) of gellan gel at
22 °C between 300 and 3000 MHz ............................................................................................. 100
Fig.5.4 Effect of sucrose content on the dielectric constant (a) and loss factor (b) of gellan gel at
915 MHz between 22 and 100 °C ............................................................................................... 101
xv
Fig.5.5 Effect of frequency and temperature on the dielectric constant (a) and loss factor (b) of
1% gellan gel with 200 mM salt addition ................................................................................... 102
Fig.5.6 Effect of salt content on the dielectric constant (a) and loss factor (b) of gellan gel at 22
°C between 300 and 3000 MHz .................................................................................................. 103
Fig.5.7 Effect of salt content on the dielectric constant (a) and loss factor (b) of gellan gel at 915
MHz between 22 and 100 °C ...................................................................................................... 104
Fig.5.8 Penetration depth of gellan gel with sucrose addition at (a) 915 MHz and (b) 2450 MHz
between 22 and 100 °C ............................................................................................................... 105
Fig.5.9 Penetration depth of gellan gel with salt addition at (a) 915 MHz and (b) 2450 MHz
between 22 and 100 °C ............................................................................................................... 106
Fig.5.10 Shear stress and shear strain at failure of gellan gel with various sucrose and salt
contents at 22 °C ......................................................................................................................... 108
Fig.5.11 Water holding capacity of gellan gel with various sucrose and salt addition at 22 °C 109
CHAPTER SIX
Fig.6.1 Scheme of Maillard reaction pathway (adapted from Martins et al., 2001) ................... 119
Fig.6.2 Whole wavelength spectrum (190~900 nm) for (a) untreated ribose-lysine solution, (b)
ribose-lysine solution treated at 60 °C for 30 min, and (c) ribose-lysine solution treated at 80 °C
for 30 min .................................................................................................................................... 122
Fig.6.3 Typical wavelength ultraviolet scan (190 to 900 nm) for ribose- lysine model solution
after heat treatment at 60 (orange line), 80 (yellow line), 100 (blue line), and 120 °C (pink line)
for 30 min .................................................................................................................................... 126
Fig.6.4 UV absorbance of ribose-lysine solutions after various heat treatments at (a) 268 nm and
(b) 315 nm (results shown were mean value of three replicates) ............................................... 128
xvi
Fig.6.5 HPLC spectrogram at 268 nm and 285 nm of ribose-lysine solutions after heat treatment
at 70 °C for 20 min ..................................................................................................................... 129
Fig.6.6 Effect of storage at 4 and 22 °C for two days on (a) the peak area of unidentified
substance(s) S268 detected at 268 nm and (b) the peak area of chemical marker M-2 detected at
285 nm ........................................................................................................................................ 131
Fig.6.7 Peak area of unidentified substance(s) S268 detected at 268 nm and chemical marker M-2
detected at 285 nm after heat treatment in ribose-lysine solutions? at different time temperature
combinations after storage at 4 °C for one month ...................................................................... 132
Fig.6.8 Images of ribose-lysine solutions in centrifuge tubes after heat treatments at (a) 60, (b)
70, (c) 80, (d) 85, and (e) 90 °C for 10, 20, 30 and 40 min. ....................................................... 133
Fig.6.9 Heating pattern results of obtained by using gellan gel and egg white gel systems in
pouches in two replicated runs of MAP treatments at 90 °C ...................................................... 135
Fig.6.10 Heating pattern results of gellan gel in pouches in two replicated runs of MAP
treatments at 90 °C with Ellab senor at predicted cold spot of Pouch 1 in Carrier 1 and at the
predicted hot spot of Pouch 1 in Carrier 2 .................................................................................. 136
Fig.6.11 Time-temperature profile at cold spot of Pouch 1 in Carrier 1 (blue line) and hot spot of
Pouch 1 in Carrier 2 (red line) in MAP....................................................................................... 137
xvii
LIST OF TABLES
CHAPTER THREE
Table 3.1 Regression constants and coefficients of determination in Eq. (7) for dielectric loss
factor of egg white and whole egg mixtures (solid concentration 25%, wb) at 915 MHz ........... 52
CHAPTER FIVE
Table 5.1 Components of the gellan gel samples with different cation and sucrose contents ...... 94
Table 5.2 Regression constants and coefficients of determination in Eq. (11) for dielectric
constant and loss factor of gellan gel with sucrose or salt addition at 915 MHz ........................ 101
CHAPTER SIX
Table 6.1 Correlation between heat treatment temperature and color and UV absorbance of
ribose-lysine solutions ................................................................................................................ 134
xviii
CHAPTER ONE
INTRODUCTION
1. Microwave food processing
Microwaves are electromagnetic waves in the frequency range of 300 MHz to 300 GHz. In
1947, two frequencies of microwaves were allocated by the Federal Communications
Commissions (FCC) for industrial, scientific, and medical (ISM) applications in North America.
915 MHz microwaves are used for industrial microwave system operations, while 2450 MHz are
used for both domestic and industrial microwave applications (Datta & Anantheswaran, 2000).
Since then, much work has been done on the development of microwave heating.
Because of the fast heating rate, significant decrease in heating time, and ease of operation,
microwave heating has been applied in food processing, including microwave assisted drying,
cooking, thawing, baking, sterilization, and pasteurization (Gupta & Wong, 2007). In
conventional thermal processing methods, the thermal energy is transferred to the food material
through heat transfer methods such as conduction, convection, or radiation from the surface of a
material due to temperature gradients. In microwave heating, on the other hand, electromagnetic
energy is directly conversed into heat volumetrically (Ohlsson, 1991). Two mechanisms
including dipolar and ionic polarization were reported as the main mechanisms of microwave
heating. The dipolar polarization is caused by the dipolar nature of water molecules that try to
realign with the direction of the changing electric field. In a high frequency oscillating electric
field, the water molecules rotate at a very high frequency which causes internal friction of
1
molecules and generates heat. Since water is usually a major component of food materials, it is
very likely that microwave energy can be used for food processing. Another major mechanism of
microwave heating is the migration of ions inside the alternative field by the dissolved
electrolytes such as salt and other minerals (Datta & Davidson, 2000). Since heat generation in
microwave heating do not reply on heat diffusion, possible fast heating of thick materials can be
achieved, and therefore, the processing time can be significantly reduced, which enhances the
quality of food products such as color, aroma, texture, and nutritional attributes.
2. Microwave sterilization and pasteurization
Sterilization and pasteurization processes are used in the food industry for destroying or
inactivating the microorganisms inside of food products to enhance food safety and extent shelf
life (Nott & Hall, 1999). During processing, the food materials are kept at a certain temperature
for a designated period of time for adequate microbial lethality. With the increasing consumer
demands for less processed foods with higher quality, microwave processing holds great
potential in both sterilization and pasteurization applications to produce safe and high quality
pre-packed food products.
In October 2009, a process for mashed potatoes (a homogeneous food) based on the pilot
plant scale 915 MHz single-mode microwave assisted sterilization (MATS) system developed at
Washington State University (WSU) was accepted by the U.S. Food and Drug Administration
(FDA), followed by a second FDA approval for microwave assisted sterilization of salmon fillets
in Alfredo sauce (non-homogeneous food) in December 2010. The U.S. Department of
Agriculture Food Safety and Inspection Service (USDA FSIS) issued an non-objective letter in
2012 for MATS application of processing prepackaged foods that contain egg, poultry, and meat
2
ingredients. Similar to microwave sterilization, microwave pasteurization also utilizes
microwave energy to quickly raise the processing temperature to the desired target temperature
to inactivate viable pathogens. A pilot plant scale 915 MHz single-mode microwave assisted
pasteurization (MAP) system is currently under development at WSU to produce high quality
chilled or refrigerated meals.
3. Need for chemical marker methods
One of the most important goals for thermal processing is to destroy or inactivate
microorganisms in food products. In order to design an effective thermal process to produce safe
food products, FDA requires the process to be reliable and predictable. However, the heating
pattern of microwave processing is usually difficult to analyze due to the highly complex
interaction between microwave energy with the cavity and the food inside (Datta and
Anantheswaran, 2000). Monitoring the temperature of the food, especially at locations of cold
spots (where least amount of energy is generated or absorbed), is essential for the development
of a proper thermal process.
Among the various available temperature measurement devices, thermocouples provide a
fast and inexpensive way for temperature determination. However, due to the interaction
between the metallic probe with the electromagnetic field of a microwave cavity and the semicontinuous process, the measurement using thermal couple can only be conducted after the
process. Infrared sensors are good for surface and 3-D temperature measurements, however, the
measurement also only can be done out of the cavity after the process. MRI (Magnetic
Resonance Imaging) tomographic methods are feasible but very expensive temperature
measurement methods. Fiber optic thermometers are designed and widely used for microwave
3
processing for on-line temperature monitoring, But multipoint measurements are impractical due
to the high cost and difficulty in deciding the points of interests.
In order to predict the temperature distributions in food products during heating, computer
simulation methods were developed based on the governing equations of electromagnetic fields
and heat transfer (Pathak et al., 2003; Zhang and Datta, 2000; Chen et al., 2008). However, the
reliability of computer simulation is sometimes challenged due to the complicated standing
microwave patterns within different food packages, and microwave cavities/systems (Ayappa et
al., 1992; Pandit and Prasad, 2003). Even with the best model, it is still necessary to use
experimental results to validate the simulation results.
The problems and challenges discussed above have highlighted the need for experimental
methods using chemical markers for determining the heat distribution inside packaged foods
during microwave processing (Kim, et al., 1996; Lau et al., 2003; Wang et al., 2004; Pandit et
al., 2006). Chemical markers are chemical compounds that are intrinsic or added into a food
matrix which change in concentration during heating (such a vitamin C, thiamine, or lysine), or
will react and form a product that change in concentration during heating in a predictable manner
(such as a browning reaction compound). A change in the amounts or concentrations of the
chemical markers can be used to predict the sterility of a thermal process. Mulley et al. (1975)
stated the need and importance of a chemical index for indicating the effect of heating in the
food and pharmaceutical industries. Since then, a number of efforts have been made to identify
suitable chemical reactants. One of the most significant finding was reported by Kim and Taub
(1993) from the US Army Natick Research Center. In their research, the carbohydrate profile
changes in heated foods after aseptic processes were monitored using anion exclusion
chromatographic (AEC) separation and photodiode array (PDA) detection together with gas
4
chromatographic mass spectrum (GC-MS). Three markers were separated, namely, M-1 (2, 3dehydro-3,5-dihydroxy-6-methyl-4(H)-pyran-4-one), M-2 (4-hydroxy-5-methyl-3(2H)-furanone),
and M-3 (5-hydroxymethylfurfural). The formation kinetics of these markers were studied and
used in later research for heating uniformity determination in ohmic heating (Kim et al., 1996),
aseptic processing (Ramaswamy et al., 1996), radio frequency processing (Wang et al., 2004),
and microwave-assisted thermal sterilization (Lau et al., 2003; Wang et al., 2004; Pandit et al.,
2006).
In the development of MATS processes, mashed potatoes and whey protein gels (WPG)
were used as model foods, which served as carriers of the chemical markers for heating pattern
determination studies (Wang, 2006). Chemical marker precursors were added during the
preparation of the model foods. The model food and chemical marker systems were then
prepackaged in trays or pouches as the real foods before microwave processing in a package.
After the heat treatments, the resultant browning reaction products at different locations were
monitored as yield of the chemical marker in model foods using High Performance Liquid
Chromatography (HPLC) method. Together with the formation kinetics of the chemical marker,
the hot spots and cold spots could be determined at specific repeatable locations in each model
food and for each package configuration at a suitable level of sensitivity. However, since HPLC
analysis of individual sections of these gels is expensive, tedious, and time-consuming, a
computer vision method was developed. The method was based on using computer software to
analyze and transform the color change of the model system resulting from the formation of
chemical markers following the heat treatment (Pandit et al., 2007). After a MATS process, the
model foods were cut into different layers, then the photographic image of each layer of the
model food are taken and analyzed using IMAQ vision builder to analyze the color patterns as
5
shown in Fig. 1.1. The color change of the model foods in different cross sections can be
transformed into a color image where cold spots are represented in blue color and hot spots in
red. The cold and hot spots obtained from the computer vision method were validated using
temperature measurements and chromatographic quantification of the chemical marker yield.
This method provided a fast and reliable way to indicate the heating patterns inside the package
resulting from microwave heating. It was also used to validate the computer simulation results to
help improve the heating uniformity of the MATS system. Fig.1.2a and 1.2b illustrate the heating
pattern results from both chemical marker method and computer simulation methods agreed with
each other and therefore validate the cold and hot spots locations. With this information, fiber
optic sensors or mobile temperature sensors could be placed at the hot and spot locations to
monitor the temperature history of the process. Data from the chemical marker method was used
as part of the validation protocol for MATS processes that were accepted by FDA for producing
both homogeneous foods (mashed potato) in 2009 and non-homogeneous foods (salmon in an
Alfredo sauce) in 2010.
6
Fig.1.1 Principle of computer vision system (adapted from Pandit et al. 2007)
Fig.1.2 Cold spot identification by (a) Chemical marker and computer vision method, (b)
Computer simulation method, and (c) Temperature measurements for cold and hot spots
validation using fiber optic sensors (adapted from Resurreccion, 2013)
7
However, WPGs and mashed potatoes which were used as model foods for MATS processes
had relatively high gelation temperatures of around 90 °C. They are not suitable for microwave
pasteurization processes due to the operation temperature under consideration for pasteurization
are usually at 70-90 °C. It is, therefore, essential to develop new time-temperature indicators for
modeling different foods and revealing the heating patterns in microwave pasteurization
processes. In this research, the physical properties of three food gels, including egg white, whole
eggs, and low acyl gellan gels, were investigated to evaluate their suitability as model foods for
MAP processes. Moreover, the formation of possible chemical markers including M2 in the
model foods was also studied in order to use computer vision method for heating pattern
determination. After locating the cold and hot spots, adequate heating could be assured by
recording the temperature history at cold spots in real foods that will be subjected to the MAP
processes to develop safe process protocols.
4. Major objectives
The overall objective of this research was to develop one or more time-temperature
indicators, to support the microwave assisted pasteurization applications. The model food
systems will be integrated with proper chemical marker precursors (Fig. 1.3). The timetemperature indicator will be used to show the heating pattern of packaged foods during the 915
MHz single mode microwave pasteurization processes. The model systems could be used for
system and process development and to improve heating uniformity. The specific objectives are
listed below to address specific aspects:
8
1). To evaluate the effect of solid and salt or other ingredient contents on dielectric
properties of gellan gel, egg white and whole egg in order to develop certain model foods to
simulate different foods.
2). To determine the gelation temperature and gel properties of each model food to evaluate
their suitability to be used at pasteurization temperatures.
3). To study the formation kinetics of possible chemical markers in each gel model, and to
correlate the chemical marker yield with color value in order for the use of computer vision
method.
4). To evaluate the sensitivity of the computer vision system to see if it is adequate for the
model food systems processed under pasteurization conditions.
5). To validate the application of the new time-temperature indicator in real food systems. In
order to prove the reliability of the new model food systems, validation of the complete method
will be carried out using real food system processed by microwave pasteurization processes.
After matching the dielectric properties of a certain model food to a target food system, the
location of cold spots can be determined using computer vision method. Optical temperature
sensors can be inserted in both the model food and real food in trays or pouches at the same cold
spot location and go through microwave pasteurization process. The two time-temperature
profiles will show if the model food with chemical marker indicator provides the same prediction
of target temperature and process lethality.
9
Fig. 1.3 Project structure for development of time-temperature indicators for microwave
pasteurization process
5. Dissertation outlines
This dissertation consists of seven chapters.
Chapter One: Introduction. This chapter introduces microwave food processing, including
microwave assisted sterilization and pasteurization processes, and describes the existing
problems which were solved in this study and the main objectives of the current research. An
outline of the whole dissertation is also provided.
Chapter Two: Literature review. This chapter gives an introduction about the reported
physical properties of the possible model foods, such as dielectric properties, gelation
temperature, gel strength and water holding capacities, and the formation pathways of the
chemical markers from Maillard reaction.
Chapter Three: Physical properties of egg white and whole egg relevant to microwave
10
assisted pasteurization processes. This chapter reports the effect of solid and salt contents of egg
white and whole egg gels on their physical properties, which are important criteria for model
food evaluation as for MAP processes. The physical properties include dielectric properties,
gelation temperature, gel strength, and water holding capacity. The results illustrated that egg
white and whole egg gels with certain solid and salt amounts could be used to model various
foods and as carriers of chemical markers for heating pattern determination during MAP
processing. The information also provides knowledge useful for dielectric heating of food
products containing egg proteins.
Chapter Four: Chemical marker M2 (4-hydroxy-5-methyl-3(2H)-furanone) formation in
egg white gel model for heating pattern determination of microwave-assisted pasteurization
processing. This chapter presents the correlation between chemical marker M2 yield and the
color change of the egg white gel model after the oil bath heat treatments. The information was
used for the determination of heating uniformity during the microwave assisted pasteurization
processes. The results also illustrated the possibility of using computer vision system to analyze
the heating pattern with validated cold and hot spots.
Chapter Five: Physical properties of low-acyl gellan gel as relevant to microwave assisted
pasteurization process. This chapter discusses the effect of sucrose and salt addition on the
physical properties of low acyl gellan gel, and reports the range of sucrose and salt contents for a
possible gellan model food to model various foods in microwave assisted pasteurization
processes. The physical properties includes dielectric properties, gel strength, and water holding
capacities, which will also provide information on the dielectric heating of foods with gellan
gum.
11
Chapter Six: Heating pattern determination using a gellan gel model for microwave
pasteurization processes. This chapter introduces a new chemical compound which can be used
as a marker for pasteurization of gellan gel model food instead of the identified marker m2. The
study demonstrated the possibility of using this chemical to correlate with the color change and
show the heating pattern using computer vision method.
Chapter Seven: Conclusions, contribution to knowledge and recommendations. Results of
the whole study are summarized in this chapter. The recommendations for future study are also
presented.
Some chapters in the dissertation maintain the styles of a particular journal where it is
published or to be submitted. Full citations of these chapters included in this dissertation are as
follows:

Chapter Three:
Zhang, W., Liu, F., Nindo, C., & Tang, J. (2013). Physical Properties of Egg Whites and
Whole Eggs Relevant to Microwave Pasteurization. Journal of Food Engineering, 118, 62–69.

Chapter Four:
Zhang, W., Tang, J., Liu, F., Bohnet, S., & Tang, Z. (2014). Chemcial marker M2 (4-
hydroxy-5-methyl-3(2H)-furanone) formation in egg white gel model for heating pattern
determination of microwave-assisted pasteurization processing. Journal of Food Engineering,
125, 69–76.

Chapter Five:
Zhang, W., Tang, J., Rasco, B., Sablani, S., Lin, H., Liu, F. Physical properties of low-acyl
gellan gel as relevant to microwave assisted pasteurization process. Submitted to Journal of Food
Engineering.
12
References
Ayappa, K.G., Davis, H.T., Davis, E.A., & Gordon. J. (1992). Two-dimensional finite element
analysis of microwave heating. AICHE Journal, 38, 1577–1592.
Chen, H., Tang, J., & Liu, F. (2008). Simulation model for moving food packages in microwave
heating processes using conformal FDTD method. Journal of Food Engineering, 88, 294–
305.
Datta, A.K., & Anantheswaran, R.C. (2000). Handbook of Microwave Technology for Food
Applications. Marcell Dekker, Inc. 511pp.
Datta, A. K., & Davidson, P. M. (2000). Microwave and radio frequency processing. Journal of
Food Science, 65, 32–41.
Gupta, M., & Wong, W. L. E. (2007). Microwaves and metals. Singapore: John Wiley & Sons
(Asia) Pte. Ltd.
Kim, H.J., & Taub, I.A. (1993). Intrinsic chemical markers for aseptic processing of particulate
foods. Food Technology, 47, 91–97, 99.
Kim, H.J., Taub, I.A., Choi, Y.M., & Prakash, A. (1996). Principles and applications of chemical
markers of sterility in high-temperature-short-time processing of particulate foods. In Lee,
T.C. and Kim, H.J. (Eds.), Chemical markers for processed and stored foods (pp. 54–69).
Washington, DC: American Chemical Society.
Lau, H., Tang, J., Taub, I.A., Yang, T.C.S., Edwards, C.G., & Mao, R. (2003). Kinetics of
chemical marker formation in whey protein gels for studying high temperature short rime
microwave sterilization. Journal of Food Engineering, 60, 397–405.
Mulley, E. A., Stumbo, C. R., & Hunting. M. A. (1975). Thiamine: a chemical index of the
sterilizing efficacy of thermal processing. Journal of Food Science, 40, 993.
13
Nott, K. P., & Hall, L. D. (1999). Advances in temperature validation of foods. Trends in Food
Science and Technology, 10, 366–374
Ohlsson, T. (1991). Microwave processing in the food industry. European Food and Drink
Review, 7, 9–11.
Pandit, R.B., & Prasad, P. (2003). Finite element analysis of microwave heating of potato––
transient temperature profiles. Journal of Food Engineering, 60, 193–202.
Pandit, R.B., Tang, J., Mikhaylenko, G., & Liu, F. (2006). Kinetics of chemical marker M-2
formation in mashed potatoes—a tool to locate cold spots under microwave sterilization.
Journal of Food Engineering, 76, 353–361.
Pandit, R., Tang, J., Liu, F., & Mikhaylenko, G. (2007). A computer vision method to locate cold
spot in foods during microwave sterilization processes. Pattern Recognition, 40, 3667–3676.
Pathak, S. K., Liu, F., & Tang, J. (2003). Finite difference time domain (FDTD) characterization
of a single mode applicator. Journal of Microwave Power & Electromagnetic Energy, 38, 1–
12.
Ramaswamy, H.S., Awuah, G.B., & Simpson, B.K. (1996). Biological verification of fluidtoparticle interfacial heat transfer coefficients in a pilot scale holding tube simulator.
Biotechnology Progress, 12, 527–532.
Resurreccion, F. P. (2012). Microwave assisted thermal processing of homogeneous and
heterogeneous food packed in a polymeric container. Doctoral dissertation, Washington
State University.
Wang, Y., Lau, M.H., Tang, J., & Mao, R. (2004). Kinetics of chemical marker M-1 formation in
whey protein gels for developing sterilization processes based on dielectric heating. Journal
of Food Engineering, 64, 111–118.
14
Wang, Y., 2006. Using whey protein gel as a model food to study dielectric heating of salmon.
Master thesis, Washington State Universtiy.
Zhang, H., & Datta, A.K. (2000). Coupled electromagnetism and thermal modeling of
microwave oven heating of foods. Journal of Microwave Power and Electromagnetic
Energy, 35, 71–85.
15
CHAPTER TWO
LITERATURE REVIEW
The main goal of this research is to study and select possible model foods and chemical
markers for the MAP processes. This review chapter summarizes important physical properties
of two model food systems based on egg proteins and gellan gum. These properties including
dielectric properties, gelation temperature, and gel properties. This chapter will also reports
literature information on formation of three chemical markers, M1, M2, and M3, and describes a
computer vision method developed at Washington State University for visualization of
microwave heating patterns and rapid detection of cold spots in prepackaged model systems after
microwave processes.
1. Model foods
Whey protein gels (WPG) and mashed potatoes were selected as model foods for MATS
processes since they are homogeneous, inexpensive, and easy to prepare. Most importantly,
WPG and mashed potatoes with addition of the reactants D-ribose and/or L-lysine upon heating
at above 100 °C will show detectable color change which can be used to indicate the heating
patterns in a model food using the computer vision system. WPG also has the additional
advantage of limiting diffusion of the marker since it is easy to form a firm gel which can be cut
into desired shapes (Wang et al., 2009). Considering similar criteria for the new model foods,
various food thickening agents and gels were considered in this study. Among them, three
readily available and relatively inexpensive food gel systems including gellan gel and egg
16
proteins were selected to study their suitability as a model food for MAP processes. The physical
properties under consideration included dielectric properties, gelation temperature, gel strength,
and water holding capacities.
1.1 Gellan gel
Gellan gum is the generic name for the extracellular polysaccharide secreted by the
bacterium Sphingomonas elodea, formerly known as Pseudomonas elodea (Pollock, 1993). It is
a linear anionic monosaccharide with a repeating unit of β-D-glucose, β-D-glucuronic acid, and
α-L-rhamnose (molar ration 2:1:1). The native gellan gum contains a glycerate on C-2 of the 3linked D-glucose, and an acetate on C-6 of the same glucose residue (Kuo and Mort, 1986), it is
known as the high acyl gellan gum. When exposed to alkali and high temperatures, both acyl
groups of the high acyl gellan gum will be hydrolyzed and a low acyl gellan can be obtained.
The chemical structure of both high and low acyl gellan gums are shown in Fig. 2.1.
17
Fig. 2.1 The chemical structure of the repeating units of high acyl (a) and low acyl (b) gellan
gum (adapted from Mao et al., 2000)
Chemical marker precursors need to be added into the model food at a temperature as low as
possible to minimize the heat induced color change during gel model preparation. High acyl
gellan gum has a high dissolving temperature, and the gel formed from it is weak and easy to
break (Mao et al., 2000). On the other hand, low acyl gellan is a cold-set gel and can form a
relatively strong and firm gel with the addition of cations. During the gel preparation, the low
acyl gellen gum is mixed with deionized water to form a dispersion with certain gellan gum
concentration, and then heated to around 90 °C to form a clear solution. The solution is added
with certain amounts of cations and then allowed to cool down so that a firm and strong gel can
be set when cooled to its gelling temperature (TG) (which is the onset temperature when the
gelation occurs). The TG of low acyl gellan gum can be as low as below 60 °C, depending on the
18
gellan gum and cation concentrations (Tang et al., 1997a & 1997b). Therefore, the physical
properties of low acyl gellan were considered to evaluate its possibility as model foods.
The gelation of low acyl gellan gel has been studied by many researchers. A review by
Morris et al. (2012) summarized the studies on the gelation and gel properties of gellan. The
gelation procedure of gellan gel was summarized as: Firstly, the disordered coil state of the
polymer converts to double helix form, which does not really build the cohesive network. The
formation of the gel requires the change of the double helices into stable aggregates. However,
the electrostatic repulsion between the helices inhibits the aggregation. Two ways can be used to
reduce the electrostatic repulsion to form firm and strong gels: one is to reduce the system pH,
and the other is to add salts. The method of reducing pH is based on changing the COO- into
COOH form. The method of adding salt is more commonly used since both cations and anions
reduce electrostatic repulsion with divalent cations having the greater effects. Two different
models of gellan gel formation are illustrated in Fig. 2.2 and show the role of “gel promoting
cations” in the gelation process.
19
Fig. 2.2 Models for gellan gelation proposed by (a) Robinson et al. (1991) and (b) Gunning and
Morris (1990) (adapted from Morris et al., 2012)
The gelation temperature of low acyl gellen gel depends on the gum concentration, cation
type and concentration, pH and sugar contents (Morris et al., 2012), and can be lower than 60 °C.
Therefore, low acyl gellan gum is considered as a possible gel system to be used as a model food
or carrier of the chemical markers for MAP processes. The chemical marker precursors can be
added to the gellan when it is cooled down to close to the gelation temperature, to minimize the
chemical marker formation. Different from WPG and mashed potatoes, gellan gel is a
transparent gel with high clarity. This also provides a great possibility of a heat induced color
changes in 3 dimensions after the microwave processing and indicate a visualized heating
pattern.
20
1.2. Egg proteins
Chicken egg is one of the most important foods consumed by human beings and has been so
since ancient times. A whole egg (WE) consists two major components which are the egg whites
(EW) and egg yolks. EW (or albumen) is a 10% aqueous solution with different proteins and
small amounts of other components. Egg yolk is a fat-in-water emulsion with the dry matter
proportion of around 50%. The ratio of proteins and lipids in egg yolk is about 1:2. Fresh
obtained egg proteins have been reported by Raikos et al. (2007) to have gelation temperatures
from 42 to 78 °C with gelation being dependent upon pH, salt, and sugar levels. These
temperatures are all in the pasteurization temperature range, which opens up the possibility of
using egg proteins as model foods for pasteurization. Furthermore, egg proteins can be purchased
in frozen, powdered, and liquid forms. Among these products, powdered egg products are
homogenized from the liquid eggs, purified by centrifuging, pasteurized, and dried (mostly
spray-dried). They have a much longer shelf life, and can be reconstituted to homogeneous liquid
products with different solid concentrations. In this project, powdered WE and EW products
were used to see if they can form possible model foods at different solid content levels.
2. Dielectric properties of the model foods
In microwave processing, heat is generated volumetrically within the food matrix by
converting electromagnetic energy into thermal energy. The dielectric properties of model foods,
which determine how the microwave energy is absorbed, transmitted, reflected, or concentrated,
are thus of great importance for understanding the behavior of model foods during microwave
heating (Datta and Anantheswaran, 2000). It is also important if the model foods can be
formulated to cover a wide range of dielectric property values to simulate various foods with
21
different dielectric properties. The relative permittivity is defined as εr =ε/ε0, which is the ratio of
the amount of electrical energy stored in a material by an applied voltage (ε), relative to that
stored in a vacuum (ε0). It consists of two parts when used in reality and can be expressed as:
εr = εr′-jεr″
(1)
where j =  1 ; ɛ rʹ is dielectric constant, which indicates the ability of a material to store
electric energy; ɛ r″ is the dielectric loss factor, which reflects the ability of a material to
dissipate electromagnetic energy into heat. The rate of energy generation per unit volume (Q) in
a food can be calculated from:
(2)
where f is the frequency (Hz), ε0 is the permittivity of free space (8.8542×10-12 F/m), εr″ is the
relative loss factor, and E is the strength of electric field of the wave (V/m). Dielectric heating
occurs through two major mechanisms, dipolar relaxation and ionic conduction (Datta et al.,
2005). Water is the major component of foods and its dipolar characteristic and ability to rotate
with changing electric field at even high frequencies contributes to the effectiveness of dielectric
heating. Ions such as salt and other minerals in foods are responsible for the ionic conduction.
Therefore, the dielectric properties of a material can be affected by different factors including
frequency, temperature, and chemical components such as moisture and salt content (Calay et al.,
1995).
Various techniques have been developed for the dielectric properties measurements.
According to different working principles, the methods can be categorized into three general
types including transmission line, resonate cavity, and open-ended coaxial probe (Kraszewski,
1980). Dielectric properties of samples prepared according to certain requirements could be
measured at a certain frequency or a frequency range. Water or oil bath can be designed to
22
connect to the sample holder for temperature control during measurements at different
temperatures.
The information on dielectric properties of gellan gel is very limited. Wang et al. (2003)
studied the DP of gellan gel with 0.17% salt content to evaluate the possibility to be used as a
model food, in order to study the inactivation of codling moth larvae under radiofrequency
heating (27 MHz). Okiror and Jones (2012) reported the effect of temperature on the DP of 1%
low acyl gellan gels with 0.17% and 0.3% CaCl2 at microwave frequency range of 0.2 to 20
GHz. The two ion concentrations were selected as they were recommended as the minimum and
maximum salt concentrations for maximum gel strength. In the present study, the main concern
will be how to develop a gel system that has similar dielectric properties as the target food while
ensuring suitable gelation properties for pre- and post-process handling.
The dielectric properties of EW and WE have been studied by many researchers using
natural components of hen eggs for either storage study or denaturation determination (Bircan
and Barringer, 2002; Ragni et al.,2007; Dev et al., 2008; Wang et al., 2009). However, there was
no study on the effects of solid concentrations or salt content on the dielectric properties of the
egg proteins. Therefore, it is necessary to explore the possibility of generating a wide range of
dielectric property values of EW and WE by altering the formulation to consider different solid
and salt contents to match potential foods to be processed by microwave pasteurization. The data
can also be used as references for the microwave processing of food products containing egg
proteins.
23
3. Gelation temperatures of model foods
In gellan gel model food preparation, gellan gum will be dissolved in water when heated at
an elevated temperature of 90 °C. For better gelling properties, cations are often added to the hot
solution. Before the gel is formed, the chemical marker precursors are added and mixed into the
solution before gelation occurs at the gelation temperature. This procedure requires the T G of the
solutions with different formulation to be as low as possible. Tang et al. (1997a) reported that the
gelation temperatures of gellan solution increased from 30 to 72 °C when the polymer
concentration increased from 0.4 to 2% (w/v) and calcium concentration increased from 2 to 40
mM. They also found that the gellan solution containing divalent cations had a much higher
gelation temperature than those containing monovalent cations (1997b). In this study, a
mathematical model was developed to predict the gelation temperature of low acyl gellan gel
with consideration of gellan gum, and monovalent and divalent cation concentrations as:
1
 3.33 10 3  1.34 10 4 log10[ X p ]  2.33 10 4 log10[0.0726 X Na  0.111 X K  X Ca  X Mg ]
Tgel
(3)
where Tgel is the gelation temperature in K, Xp, XNa, XK, XCa, and XMg are concentrations of
gellen gum (%), and concentrations (mM) of added Na+, K+, Ca2+, and Mg2+. The model was
validated and can be used for the determination of the gelation temperatures of gellen gels with
different cations addition for dielectric properties adjustments, in order to ensure the chemical
markers can be added at proper temperatures.
The thermally induced gelation of egg proteins allows the formation of a 3-D network
structure to immobilize and retain a large amount of water. The gelation mechanism involves the
protein denaturation, soluble aggregation, and interaction of aggregations into a network. In the
preparation of egg protein model food, the egg protein powders will be dissolved in water at
24
different solid and salt concentrations. Chemical marker precursors will be mixed into the model
food liquid mixture and then the mixture will be heated to form EW or WE gels and packaged
into trays or pouches. It is desirable that the gelation temperatures of egg proteins to be as low as
possible to minimize the chemical marker formation during the pre-forming of the model food
gels. Salt has been reported to affect the heat-induced gelation of proteins by many researchers.
Kohnhorst and Mangino (1985) suggested that low concentrations of salt could promote the
solubilization of protein before heating. Arntfield et al. (1990) found that salt addition of 0.1–0.5
M (0.6–2.9%) had no effect on the denaturation temperature of EW. However, higher
concentration of salt addition (6%) caused an increase of gelation temperature (Raikos et al.,
2007), due to its inhibition of interactions between water and hydrophilic groups in protein, and
the shielding effect on the repulsive forces between protein molecules (Raikos et al., 2007). In
the present study, the effect of salt addition on the gelation temperature of egg proteins will be
studied.
4. Other physical properties of the model foods
Other physical properties of the three possible food gel systems are also important for
evaluating their suitability as model foods, including the gel strength and water holding
capacities (WHC). The gel strength indicates if the model food has a proper texture to hold its
shape, and if it is easy to be cut into different layers for marker yield and computer vision
analysis. WHC is the ability of a gel to hold water in its gel network structure. It reflects the
ability of the model food to retain its geometry and size during and after the microwave process.
25
5. Chemical markers
The chemical marker method is based on the Maillard reaction between reducing sugars and
amino acids. It is one of the most important reactions in food during heat treatment or long time
storage, and has been studied extensively (Hodge, 1953). Fig. 2.3 shows the normal pathways of
the Maillard reaction. The reaction starts when the amino groups of the amino acids or proteins
react with a glycosidic hydroxyl of reducing sugar, and stops when brown nitrogenous polymers
or melanoidins are formed (Ellis, 1959). The reaction pattern could be different for different
amino acids and carbohydrates, and can also be affected by factors such as temperature, pH,
oxygen, metals, phosphates, sulfuric dioxide, etc.
26
Fig. 2.3 Reaction pathways of the formation of Melanoidins from Aldose sugars and amino
compounds (adapted from Hodge, 1953).
Chemical markers M-1, M-2, and M-3 were identified and separated from heating different
reducing sugars and amino acids at sterilization temperatures of 121 °C (Kim and Taub, 1993).
Among them, M1 is produced from D-glucose or D-fructose and amines through 2,3-enolization
under weak acidic or neutral conditions at sterilization temperatures (Fig. 2.4a). M2 is formed
from D-ribose or D-ribose-phosphate under similar conditions (Fig. 2.4b). M3, also known as 5-
27
hydroxy-methylfurfural (5-HMF) is a major degradation product of D-fructose (Fig. 2.4a). Lau et
al. (2003) studied the formation kinetics and found that the higher reaction rate constant k of M2
than that of M1 suggested that M2 was more suitable for high temperature short time (HTST)
processes. However, these three currently identified chemical markers may not be suitable as
chemical markers for the pasteurization processes due to lower treatment temperatures of
pasteurization ranging from 70 to 100 °C. The critical task of this project is to check if the
current chemical markers can be produced, or if new chemical markers must be found and
identified to indicate the heating patterns of foods at pasteurization temperature.
28
(a)
(b)
Fig. 2.4 Reaction pathways for the formation of chemical marker M1 (a), M2 (b), and M3 (a)
(adapted from Kim, 1996)
29
6. Computer vision method
Since the chemical marker yield analysis using HPLC method was costly and time
consuming, a computer vision method was developed as a fast and convenient way for heating
pattern determination (Pandit et al., 2006). The major components of the computer vision system
are shown in Fig. 2.5. The software of the computer vision system included Photoshop and
IMAQ vision builder software. The heating pattern analysis was based on a script built in the
IMAQ software developed by Pandit et al. (2007). After the marker identification, the formation
kinetics of the chemical markers in new model food systems can be studied to correlate the
marker yield with color change to validate the application of the new model food systems.
In Pandit’s study for heating pattern determination of the microwave assisted thermal
sterilization, the marker yield and color of the model food sample both followed linear
relationships with F0, which indicated the possibility of using different color in the heating
pattern image obtained from the computer vision system to show the lethality, and the cold spots
and hot spots. However, due to the lower processing temperature for microwave pasteurization
process, the color change of the model food caused by chemical marker may not sufficient
because of the lower concentration present at pasteurization temperatures. Therefore, it is
necessary to check the sensitivity of the current computer vision system to see if it is sensitive
enough for samples to detect color changes at a lower concentration.
30
Fig. 2.5 Components of a computer vision system (adapted from Pandit, 2006)
The above review on the background knowledge and current studies of possible model foods
and chemical markers presents the possibility of using egg proteins and gellan gel together with
possible Maillard reaction products as heating pattern indicators for MAP processes. However,
none of those studies were aimed for microwave heating processes. Therefore, the information
on physical properties of egg protein and low acyl gellan gels with different formulations,
especially their dielectric properties, are of great importance for the development of model foods
for MAP processes. After the evaluation of the physical properties of each model food, the
possible chemical markers need to be investigated to ensure the use of computer vision method
for accurate heating pattern results.
31
References
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and rheological properties of heat-induced protein networks from ovalbumin and vicilin.
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Bircan, C., & Barringer, S.A. (2002). Use of dielectric properties to detect egg
proteindenaturation. Journal of Microwave Power and Electromagnetic Energy, 37, 89–96.
Calay, R.K., Newborough, W., Probert, D., & Calay, P.S. (1995). Predictive equations for the
dielectric properties of foods. International Journal of Food Science and Technology, 29,
699–713.
Datta, A.K., & Anantheswaran, R.C. (2000). Handbook of Microwave Technology for Food
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34
CHAPTER THREE
PHYSICAL PROPERTIES OF EGG WHITES AND WHOLE EGGS
RELEVANT TO MICROWAVE PASTEURIZATION
Zhang, W1., Liu, F1., Nindo, C2., Tang, J1* (2013)
Journal of Food Engineering, 118:62-69.
1.
Department of Biological Systems Engineering, Washington State University,
Pullman, WA 99164-6120
2.
UI/WSU bi-State School of Food Science, University of Idaho,
Moscow, ID 83844-2312
Abstract
Microwave pasteurization is a novel thermal processing technology in which non-uniform
heating may be a major challenge. In this study, the suitability of using egg whites (EW) and
whole eggs (WE) as model foods to evaluate the heating uniformity and to determine the cold
and hot spots during microwave pasteurization was investigated. The samples were prepared
from mixtures of water with commercial EW or WE powders at different solid concentrations
(20, 25, 27.5, and 30%) and salt contents (0, 50, 100, and 200 mM). Critical physical properties
supporting their suitability as model food systems include appropriate dielectric properties,
gelation temperatures, gel strengths, and water holding capacities (WHC). The gelation
temperatures of liquid EW and WE were 70 and 80 °C; both fell in the pasteurization
temperature range. At 915 MHz, the dielectric constants of liquid EW and WE samples and their
35
heat induced gels decreased with solid concentration while the loss factor was not affected. Loss
factors of liquid EW and WE samples increased linearly with salt addition, which could be
explained by the linear increase of electrical conductivities by adding salt. The strength and
WHC of heat induced EW and WE gels increased linearly with solid concentration, while salt
addition had no significant effect. The results demonstrated the suitability of using EW and WE
as model foods to determine the heating uniformity during microwave pasteurization process.
Keywords: Egg whites, whole eggs, dielectric properties, gelation, gel strength, water holding
capacity
1. Introduction
Microwave-assisted thermal processing is a novel thermal processing technology that provides
rapid volumetric heating (Ohlsson, 1991). It overcomes the slow heating rate in conventional
thermal processes, so that better product quality could be retained. In October 2009, a process for
mashed potatoes (homogeneous food) based on the 915 MHz microwave assisted thermal
sterilization (MATS) system developed at Washington State University (WSU) was accepted by
the U.S. Food and Drug Administration (FDA), followed by a second FDA acceptance for
microwave assisted sterilization of salmon fillets in Alfredo sauce (non-homogeneous food) in
December 2010. Similar to microwave sterilization, microwave assisted pasteurization (MAP)
also utilizes the microwave energy to quickly raise the processing temperature to desired levels
to inactivate viable pathogens in foods. A 915 MHz MAP system is currently under development
at WSU for cold-storage pre-packed foods.
36
A major challenge for developing microwave-assisted thermal processes is the non-uniform
heating pattern caused by the factors influencing the electromagnetic field, such as food
properties, package geometry, and location of the product inside the microwave applicators
(Keefer and Ball, 1992; Stanford, 1990). In the development of MATS processes, whey protein
gels (WPG) with chemical marker precursors were found useful to map the heating patterns
using a computer vision method (Pandit et al., 2007; Wang et al., 2009b). However, WPGs are
not suitable for microwave pasteurization due to their high gelling temperature at around 90 °C.
It is thus essential to develop new model food systems for MAP processes.
Natural egg components including egg white and whole egg can form heat-induced gels.
Their gelation temperatures vary from 42 to 78 °C at different pH, salt, and sugar levels (Raikos
et al., 2007), which are all in the pasteurization temperature range. Powdered eggs (produced by
drying, mostly spray-drying, from the liquid eggs) in homogeneous form have an extended shelf
life, and can be easily and consistently reconstituted into the liquid form at different solid
concentrations. Thus, EW and WE can be conveniently used to form model foods.
In microwave processing, heat is generated volumetrically inside the material by converting
electromagnetic energy into thermal energy. The dielectric properties of the model foods dictate
how the microwave energy is absorbed, transmitted, reflected, or concentrated. They are of great
importance for understanding the behavior of model foods during microwave heating (Datta and
Anantheswaran, 2001). The dielectric properties of usual interest are the dielectric constant εr′
and the dielectric loss factor εr″, which are the real and imaginary parts of the relative complex
permittivity εr as:
εr = εr′ - jεr″
37
(1)
where j =  1 , εr′ indicates the ability of a material to store electric energy, and εr″ reflects the
ability of the material to dissipate electromagnetic energy into heat (Nelson and Datta, 2001).
The rate of energy generation per unit volume (Q) in the material can be calculated from
(Dibben, 2001):
Q = 2πf ε0εr″E2
(2)
where E is the strength of electric field of the wave, ε0 (8.8542×10-12 F/m) is the permittivity of
free space, and f is the frequency.
The dielectric properties of natural hen egg components have been investigated by many
researchers for either storage studies or protein denaturation determinations (Bircan and
Barringer, 1998; Ragni et al., 2007; Dev et al., 2008; Wang et al., 2009a). However, there was no
study on the effects of solid concentration or salt content on the dielectric properties of the egg
proteins. One of our objectives was to explore the possibility of generating a wide range of
dielectric property values of EW and WE by changing solid and salt contents to match potential
foods to be processed by MAP.
The heat induced gelation of egg proteins is a transition from a fluid-like to a solid-like
viscoelastic structure (Montejano et al., 1984). Physical properties such as gelation temperature,
gel strength, and water holding capacity (WHC) are also important for evaluating the suitability
of EW and WE as model foods. The gelation temperature (the onset temperature at which the
gelation occurs) determines whether the liquid EW and WE can solidify at the pasteurization
temperatures to form solid model foods. It can be affected by the protein concentration, ion
concentration, pH, and possible interaction between protein and other components (Yasuda et al.,
1986). Gel strength indicates if the model food has a proper texture to hold its shape, and if it is
proper for cutting and post-process evaluation. WHC is the ability of a gel to hold water in its
38
network structure, indicating the ability of the model food to retain its geometry and size during
and after the process.
The objectives of this study were to investigate the effects of solid concentration and salt
content on physical properties (including dielectric properties, electrical conductivity, gelation
temperature, gel strength, and WHC) of liquid egg white and whole egg samples and their heat
induced gels, in order to evaluate their suitability as model foods for microwave pasteurization.
The data can also be used as reference for processing of egg products using microwave
pasteurization.
2. Materials and methods
2.1. Sample preparation
Commercial “Just Whites” all natural egg white powder (0% total fat, 2% sodium; Deb-El
Foods Corporation, Elizabeth, NJ) and “Honeyville Farms” whole egg powder (8% total fat, 3%
sodium; Honeyville Food Products, Brigham City, UT) were used to produce homogeneous
liquid EW and WE samples. To study the effect of solid concentration on the physical properties,
EW and WE were prepared with solid concentrations of 20, 25, 27.5, and 30% (wb). Salted EW
and WE samples were prepared by adding salt of 0, 50, 100, and 200 mM into liquid EW and
WE samples with solid concentration of 25%. In the preparation of the liquid samples, a predetermined amount of EW or WE powder was reconstituted using 35 °C double deionized (DDI)
water and mixed for 3 min using a magnetic stirrer. Pre-determined amounts of table salt were
added to the mixtures and further stirred for 15 min. The mixtures were held in a water bath at
35 °C for 20 min, and then kept at room temperature overnight before use. Part of the liquid
mixtures was then used for the determination of dielectric properties, electrical conductivities,
39
and gelation temperature. Separate samples were filled into glass bottles (diameter=40 mm;
height=30 mm), heated in a water bath at 85 °C for 30 min, and cooled in tap water for 20 min to
form gels. The gels were used for the determination of gel strength and WHC. For each physical
property measurement, duplicate sets of samples were prepared.
2.2. Dielectric properties
An HP 8752 C Network Analyzer (frequency range: 300 to 3000 MHz) and 85070B OpenEnd Coaxial Dielectric Probe (Agilent Technologies, Santa Clara, CA) were used for the
dielectric properties measurement (Fig. 3.1). After the instrument was warmed up and calibrated,
liquid EW and WE samples were filled into the custom-built stainless steel test cell (20 mm inner
diameter, 94 mm height). The test cell was connected to a circulating oil bath (Ethylene: water
=9:1) with programmable circulator (1157, VWR Science Products, Radnor, PA) for temperature
control. The liquid in the oil bath was pumped into the space between the two walls of the test
cell to heat the sample from 22 to 100 °C. A thermocouple was inserted into the sample from the
lower end of the test cell to monitor the temperature. The measurement was triggered at every 10
°C temperature increment. A stainless steel spring and piston inside the test cell compresses the
sample tightly to the dielectric probe after heat-induced gelation in order to ensure the contact
between the probe and the sample. 201 points were recorded through the whole frequency range.
After each measurement, the test cell was dipped into ice to cool down. A more detailed
description of the system and measurement procedure was described by Guan et al. (2004). All
measurements were conducted in duplicate.
40
Fig. 3.1 Schematic diagram of the DP measurement system (adapted from Guan et al., 2004)
2.3. Penetration depth
Penetration depth (Dp) of microwave power is the depth where the incident power decreases
to 1/e (e=2.718) of its original value at the material surface. Dp can be calculated as:
Dp 
c
   " 2

2f 2 '     1  1
  ' 



(m)
(3)
where c is the speed of light in free space as 3×108 m/s, f is the frequency (Hz) (Buffler, 1993),
which is 915 MHz in this study.
2.4. Electrical conductivity
A CON-500 Electrical Conductivity meter (Cole-Parmer Instrument Co., Vernon Hills, IL)
was used for the electrical conductivity measurements of liquid EW and WE samples (25% solid
concentration, wb) at room temperature. The probe was kept immersed in the solutions and the
41
readings were recorded after the temperatures reached equilibrium. All measurements were
carried out in triplicate.
2.5. Gelation temperature
The gelation temperature can be studied by different methods including Differential
Scanning Calorimetry (DSC) (Ahmed et al., 2007) and rheological methods such as small
amplitude oscillatory shear (SAOS) (Ould Eleya and Gunasekaran, 2002; Croguennec et al.,
2002; Raikos et al., 2007). Since the SAOS method avoids the fracture of the formed protein
network during measurement, it was chosen in this study as a more sensitive and accurate
method.
An AR 2000 rheometer (TA Instruments, New Castle, DE, USA) was used to monitor the
gelation temperature of liquid EW and WE samples using SAOS mode. For each measurement,
approximately 1.26 mL of each sample was loaded between the 40 mm parallel plate geometry
with a gap of 1.00 mm. In order to maintain a water saturated atmosphere to prevent evaporation
of the sample during heating, the well in the upper plate was filled with distilled water and
covered with a solvent trap cover. Once loaded, the sample was equilibrated to 30 ˚C for 1 min,
and heated by a Peltier-plate temperature-control system to 90 ˚C at a heating rate of 5 ˚C /min.
During heating, the SAOS test was carried out with a strain of 0.01 at a frequency of 1Hz. Each
measurement was carried out in duplicate.
Different approaches were used to extract the gelation temperature information from the
rheological data for heat induced protein and polysaccharide gels. These include using the cross
over point between the values of storage modulus (G′) and loss modulus (G′′) (Clark et al., 1991;
Sun and Arntfield; 2011), the maximum G′′ point (Stading and Hermansson, 1990), the
42
temperature where G′ becomes larger than the background noise (Ross-Murphy, 1995;
Gunasekaran and Ak, 2000; Ould Eleya and Gunasekaran, 2002), and extrapolating the rapid
increase of G′ during the initial heating phase to intercept the temperature axis (Hsieh et al.,
1993; Steventon et al., 1991; Tang et al., 1997). In our study, the method of extrapolating the
rapid increase of G′ during the initial heating phase to intercept the temperature axis was chosen
to determine the gelation temperature.
2.6. Gel strength
A TA-XT2i Texture Analyzer (Texture Technologies Corp, Scarsdale, NY) equipped with a
25 kg load cell and 40 mm plate probe was used to conduct the uniaxial compression tests for gel
strength determination. The gel samples were cut into cylindrical specimens (diameter=21 mm,
height=20 mm) using a stainless steel tube with sharp edges. The cross-head of the texture
analyzer was set to move at a speed of 1 mm/s till 40% deformation, and return to the original
position with a speed of 2 mm/s after the compression. The gel strength was represented by the
maximum force indicated on the deformation curve. Each measurement was carried out in
triplicate.
2.7. Water holding capacity (WHC)
The WHC of gels was determined following the method reported by Barbut (1995). The gel
samples were cut into cylindrical specimens (diameter=21 mm, height=10 mm). The weight of a
weighing plate with two pieces of 90-mm-dia Whatman filter paper # 541 was measured as W0.
A piece of gel specimen weighing Ws was placed between the two pieces of filter paper for a
compression test using TA-XT2i Texture Analyzer. The compression test was set at 40%
43
deformation and 30 s holding time. After compression, the weight of the plate and the wet filter
paper were measured as W1. The WHC value was calculated as:
WHC = [1– (W1– W0)/Ws]×100%
(4)
All measurements were carried out in triplicate.
2.8. Data analysis
Test data was analyzed using Microsoft Excel (Microsoft Corporation, Redmond, WA) to
calculate the mean values and standard deviations. Linear regression tests and statistical
difference determinations (p=0.05) were conducted with Minitab (Minitab Inc., State College ,
PA).
3. Results and discussion
3.1. Dielectric properties
3.1.1 Effect of frequency and temperature
The dielectric constant of both liquid EW and WE samples (25% solid concentration, wb)
decreased with increasing frequency from 300 to 3000 MHz (Fig. 3.2a and 3.2c). The result
agreed with those reported influence of microwave frequency on other food materials such as
egg albumen and yolk (Ragni et al., 2007), liquid and precooked egg whites and whole eggs
(Wang et al., 2009a), mashed potatoes (Guan et al., 2004), and whey protein gels (Nelson and
Bartley, 2002). The dielectric loss factors of EW and WE also decreased with increasing
frequency in the tested frequency range (Fig. 3.2b and 3.2d). The result confirmed earlier reports
that the dielectric loss factor of most foods tended to decrease with increasing frequency (Calay
44
et al., 1995), and that the loss factor of many foods was reduced almost by a half with the
increase of frequency from 0.9 to 2.8 GHz (Ohlsson and Bengtsson, 1975).
Effects of temperature (22 to 100 °C) on the dielectric properties of EW and WE can also be
seen in Fig. 3.2. Calay et al. (1995) stated that the influence of temperature on the dielectric
properties depended on the operating frequency, the bound-water and free-water content ratio,
and the ionic conductivity of the material. For EW samples at frequencies higher than 500 MHz
(Fig. 3.2a) and WE samples at higher than 400 MHz (Fig. 3.2c), the dielectric constants
decreased as the temperature increased from 22 to 100 °C. However, at lower frequencies, the
change of dielectric constant with temperature was not consistent. It was reported that the
dielectric constant increased with temperature for liquid and pre-cooked natural egg white and
whole eggs (Wang et al., 2009a) and whey protein gel (Wang et al., 2003) at 27 and 40 MHz.
The dielectric constants of some biological tissues or agricultural products were even higher than
80 (Wang et a., 2003; Stuchly et al., 1982). Some authors attributed the high dielectric constant
values to the poorly conditioned calibration at low frequencies (Sheen and Woodhead, 1999).
However, the lowest frequency used in our study was 300 MHz, much higher than the ones in
the former reports. We have no clear explanation for the different trends of dielectric constant
with temperature at frequency range of 300 to 500 MHz for EW and 300 to 400 MHz for WE
samples.
The changes of dielectric loss factor of EW and WE with temperature are shown in Fig. 3.2b
and 3.2d. For EW at frequencies lower than 2000 MHz and WE at frequencies lower than 800
MHz, the dielectric loss factor increased with temperature. However, with the increase of
frequency, the trends started to switch. Similar observations were reported for samples such as
mashed potato (Guan et al., 2004), egg white and whole eggs (Wang et al., 2009a), and whey
45
proteins (Wang et al., 2003). According to Mudgett (1986), the dielectric loss factor can be
expressed as:
ε″ = ε″d + ε″σ
(5)
where ε″d is the dipole loss component due to the dipole rotation, and ε″σ is the ionic loss
component due to the displacement of charged ions (Muddget, 1986). At lower frequencies, the
loss factor was mainly contributed by the ionic loss component ε″σ. With the increase of
frequency, the contribution of dipole component ε″d started to increase. However, the importance
of the dipole loss component decreased with increasing temperature, which meant the switch of
trend of loss factor for low temperatures started at a lower frequency than the high temperature
samples. Wang et al. (2009b) explained this phenomena by the shift of dispersion region of free
water to higher frequencies at higher temperatures.
46
65
(a)
(b)
Dielectric loss factor
Dielectric constant
1000 MHz
Temperature
60
55
50
22 °C
40 °C
60 °C
80 °C
100
100 °C
45
300
3000
50
45
40 °C
60 °C
80 °C
40 °C
22 °C
3000
(d)
Dielectrci loss factor
Dielectrci constant
60 °C
Frequency (MHz)
(c)
22 °C
80 °C
300
Frequency (MHz)
55
100 °C
10
100 °C
100 °C
5
40
300
800 MHz
50
80 °C
60 °C
40 °C
22 °C
300
3000
Frequency (MHz)
3000
Frequency (MHz)
Fig. 3.2 Effects of frequency and temperature on the dielectric properties of liquid egg white and
whole egg samples (25% solid concentration, wb) (a) Dielectric constant of egg white; (b)
Dielectric loss factor of egg white; (c) Dielectric constant of whole egg; (d) Dielectric loss factor
of whole egg.
3.1.2. Effect of solid concentration
Effect of solid concentration on the dielectric properties of liquid EW samples is shown in
Fig. 3.3. The dielectric constant decreased with the increase of solid content (Fig. 3.3a).
47
However, the differences among dielectric constants of samples with different solid contents
decreased with the increase of temperature. At 100 °C, no significant difference among the
dielectric constants was found for all samples (p>0.05). Sun et al. (1995) reported that for food
samples with moisture content higher than 40%, water in free form was supposed to be the
dominant component governing the dielectric properties of the material. In our study, the
samples at lower temperatures were in liquid form. Therefore, the increase of solid content
resulted in decrease of free water content, and caused the decrease of the dielectric constant.
With the increase of temperature where the denaturation of egg proteins started to occur and
combine with a large amount of water, the amount of free water decreased and resulted in a
similar dielectric constant value for egg white samples with different solid contents. However,
Guan et al. (2004) found that moisture content had no effect on the dielectric constants of
mashed potatoes. The difference could be attributed to the smaller solid content differences used
in their study (from 12.2%~18.4%). The effect of solid concentration on the dielectric loss factor
was not significant (p>0.05) (Fig. 3.3b). The results confirmed the findings of Mudgett et al.
(1980) that dielectric loss factor showed little dependence on moisture content.
48
70
Dielectric loss factor
27.5%
30%
60
55
50
(a)
45
10
20%
70
25%
65
Dielectric constant
80
20%
25%
27.5%
60
30%
50
40
30
(b)
20
30
50
70
90
110
10
Temperature (°C)
30
50
70
90
110
Temperature (°C)
Fig. 3.3 Effect of solid concentration (wb) on the dielectric properties of liquid egg white
samples at 915 MHz (a) Dielectric constant (b) Dielectric loss factor
3.1.3. Effect of salt content
As shown in Fig. 3.4, salt addition of up to 200 mM had no significant effect on the
dielectric constant of liquid EW and WE samples (25% solid concentration, wb) at 915 MHz
(p>0.05). However, the dielectric loss factor of both samples increased significantly with the
increase of salt addition (p<0.05) (Fig. 3.5). The result agreed well with the reported effect of salt
on dielectric properties of mashed potatoes (Guan et al., 2004). This could be explained by the
increased electrophoretic migration (Mudgett, 1986) achieved by adding salt. In Equation 5, the
ionic loss component (ε″σ) due to the displacement of charged ions can be expressed as:
ε″σ =

2f 0
(6)
where σ is the electrical conductivity (S/m). The equation indicates that the ionic loss factor
component increases linearly with electrical conductivity. This relationship was confirmed by
49
data shown in Fig. 3.6 for liquid EW and WE samples (25% solid concentration, wb) with
different salt addition at 22 °C. The following linear relationship between loss factor and salt
addition was developed as:
ε"= a + b T +cS
(7)
where a, b, and c are constants, T is temperature (22~100 °C), and S is the salt content (0~200
mM). The regression analysis results and the coefficients of determination R2 for egg white and
whole egg are shown in Table 1.
control-e'
60
60
Control-e'
50 mM-e'
50 mM-e'
200 mM-e'
55
50
45
40
Dielectric constant
Dielectric constant
100 mM-e'
100 mM-e'
55
200 mM-e'
50
45
(a)
10
30
50
70
90
110
Temperature (°C)
(b)
40
10
30
50
70
90
110
Temperature (°C)
Fig. 3.4 Effect of salt content on the dielectric constant of liquid egg white (a) and whole egg (b)
samples (25% solid concentration, wb) at 915 MHz
50
120
100
Dieletric loss factor
EW 25% control
EW 25% + 50 mM
80
EW 25% + 100 mM
EW 25% + 200 mM
60
WE 25% control
WE 25% + 50 mM
40
WE 25% + 100 mM
WE 25% + 200 mM
20
0
10
30
50
70
90
110
Temperature (°C)
Fig. 3.5 Dielectric loss factor of liquid egg white and whole egg samples (25% solid
Electrical conductivity (mS/m)
concentration, wb) with different salt additions at 915 MHz
30
y = 0.0662x + 13.287
R² = 0.9994
25
WE
20
EW
15
10
y = 0.0731x + 5.23
R² = 0.9991
5
0
0
100
200
300
Salt addition (mM)
Fig. 3.6 Effect of salt addition on the electrical conductivities of liquid egg white and whole egg
samples (25% solid concentration, wb) at 22 °C
51
Table 3. 1 Regression constants and coefficients of determination in Eq. (7) for dielectric loss
factor of egg white and whole egg mixtures (solid concentration 25%, wb) at 915 MHz
Sample
a
b
c
R2
Egg white
8.500
0.582
0.152
0.940
Whole egg
- 3.190
0.364
0.199
0.939
As shown in Fig. 3.5, higher electrical conductivity of EW sample partially contributed to
the larger loss factors of EW than that of WE at the same solid and salt contents. In addition, the
lower fat content of EW samples than that of WE was also part of the reason (Sun et al., 1995),
which agreed well with the findings by Ragni et al. (2007), Dev et al. (2008), and Wang et al.
(2009a). Therefore, a wide range of loss factors was provided by EW and WE with different salt
contents. In our study, loss factor ranges of 12.7±0.9 to 51.3±0.4 at 22 °C and 25.5 ± 0.8 to
111.6±0.3 at 100 °C were obtained. The regression results shown in Table 1 can be used to
calculate the salt content of liquid EW and WE samples (solid concentration 25%, wb) so that
desired loss factor could be obtained to model various foods at different temperatures.
3.1.4. Penetration depth (Dp)
The Dp of 915 MHz microwave in liquid EW and WE samples (25% solid concentration,
wb) without added salt at 22 °C were 13.8 and 29.9 mm, respectively, which were comparable
with that of egg white (19.4 mm), egg yolk (21.3 mm) (Dev et al., 2008), egg albumen (20 mm),
and egg yolk (26 mm) (Guo et al., 2007). As shown in Fig. 3.7, the Dp of 915 MHz microwave in
both EW and WE decreased linearly with temperature (R2>0.98). This was caused by the
increase of dielectric loss factor and slight decrease of dielectric constant with increasing
52
temperature. Similar effects of temperature on Dp were reported for mashed potatoes (Guan et
al., 2004) and whey protein gels (Wang et al., 2009b).
The addition of salt also caused decreasing of Dp in both liquid EW and WE samples due to
the large influence on dielectric loss factor. It has been suggested that the thickness of a food
material should not be more than two or three times that of the Dp for uniform pasteurization
with dielectric heating (Schiffmann, 1995). Furthermore, water/solid concentration has no
significant effect on Dp (Guan et al., 2004). It is, therefore, important to monitor the salt content
of EW and WE to be used as model foods. Based on the results of the penetration depth, the
maximum thickness of the model food using formulas in this study could range from
approximately 30 mm for EW with 200 mM salt to 90 mm for WE without salt.
35
Penetration depth (mm)
30
WE 25% control
25
WE 25% + 50 mM
WE 25% + 100 mM
20
WE 25% + 200 mM
15
EW 25% control
EW 25% + 50 mM
10
EW 25% + 100 mM
EW 25% + 200 mM
5
0
10
30
50
70
90
110
Temperature (°C)
Fig. 3.7 Penetration depth of 915 MHz microwave in liquid egg white and whole egg samples
(25% solid concentration, wb)
53
3.2. Gelation temperature
Plots of storage modulus (G′) of liquid EW and WE samples (solid concentration 25%, wb)
versus temperature are shown in Fig. 3.8. The initial gradual increase of G′ was due to the
denaturation of the less heat stable albumen protein, conalbumin (Montejano et al., 1984). When
the temperature was further increased to around 70~74 °C, ovalbumin which is the major protein
in egg whites (Powrie, 1977) started to denature and caused the rapid increase of rigidity,
indicating the quick formation of three-dimensional gel network. In our study, the gelation
temperatures of liquid EW and WE samples determined by extrapolating the rapid increase of G′
during the initial heating phase to intercept the temperature axis were 70 and 80 °C, respectively.
Both gelation temperatures were in the pasteurization temperature range, indicating that those
gels could be formed during the microwave pasteurization process as model foods. The higher
gelation temperature of WE was due to the higher thermostability of egg yolk than egg white
proteins. The results were comparable with the reported gelation temperatures of natural EW and
WE as 74.3 and 72.0 °C, respectively (Raikos et al., 2007). However, the lower gelation
temperature of WE reported by Raikos et al. (2007) could be explained by the effect of protein
concentration on gelation temperature. Ould Eleya and Gunasekaran (2002) revealed the
decrease of gelation temperature with increasing protein content. The gelation temperature of
EW with solid concentration of 10, 15, and 20% determined in their study were 78, 75, and 70
°C.
Salt has been reported to affect the heat-induced gelation of proteins by many researchers.
Kohnhorst and Mangino (1985) suggested that low concentrations of salt could promote the
solubilization of protein before heating. Arntfield et al. (1990) found that salt addition of 0.1-0.5
M (0.6~2.9%) had no effect on the denaturation temperature of EW. However, higher
54
concentration of salt addition (6%) caused an increase of gelation temperature (Raikos et al.,
2007), due to its inhibition of interactions between water and hydrophilic groups in protein
(Danilenko et al., 1985), and the shielding effect on the repulsive forces between protein
molecules (Raikos et al., 2007). In our study, salt addition of up to 200 mM (resulting in final
salt content of 1.8 % in EW and 2.0 % in WE) had no significant (p>0.05) effect on the gelation
temperatures (Fig. 3.8).
50000
2000
1500
G̒ (Pa)
40000
G̒ (Pa)
30000
1000
500
0
45
20000
10000
0
25
55
65
75
85
Temperature (°C)
WE
WE 50 mM
WE 100 mM
WE 200 mM
EW
EW 50 mM
EW 100 mM
EW 200 mM
35
45
55
65
95
75
85
95
Temperature (°C)
Fig. 3.8 Changes of storage modulus (G′) of liquid egg white and whole egg samples (25% solid
concentration, wb) with different salt additions during heating
3.3. Gel strength and water holding capacity (WHC)
Fig. 3.9 shows the linear increase of gel strength and WHC with solid concentration for EW
and WE gels (R2> 0.94). The gel strength of EW was much higher than that of WE with the same
solid concentration. This could be explained by the more compact network formed in EW gels
55
due to higher protein content. However, solid concentrations of 20~25% for EW and 25~30% for
WE should be used for model food preparation since other solid concentrations may result in
gels with either strong network structure that is difficult to cut, or low gel strength for handling.
Salt addition (up to 200 mM) had no significant effect on gel strength of EW and WE (p>0.05,
results not shown). The result was different from the decreasing hardness of EW and WE gels
caused by adding 6% salt (Raikos et al., 2007). The reason could be the lower salt and protein
concentration ratio in our study, where less salt was added to more concentrated reconstituted
liquid EW and WE samples. The WHC of WE was higher than that of EW with the same solid
concentration, whereas both were high enough to retain gel form during and after the microwave
processes. The effect of salt content on WHC of both EW and WE gels was also not significant
(p>0.05, results not shown).
Gel Strength (N)
80
y = 6.0634x - 100.09
R² = 0.9672
Water holding capacity (%)
100
WE
60
EW
40
y = 1.1368x - 16.384
R² = 0.9926
20
(a)
0
15
20
25
30
100
y = 0.2468x + 91.244
R² = 0.9415
98
WE
96
EW
94
(b)
90
35
y = 0.4119x + 84.041
R² = 0.9788
92
15
Solid concentration (%)
20
25
30
35
Solid concentration (%)
Fig. 3.9 Effect of solid concentration on the gel strength (a) and WHC (b) of egg white and
whole egg gels without salt addition
56
4. Conclusions
The dielectric properties of liquid egg white and whole egg samples decreased with
frequency from 300 to 3000 MHz. Dielectric constant decreased with solid concentration while
loss factor was not affected. Salt addition caused significant increase of loss factor of both egg
samples, resulting in a wide range of loss factor values so that various foods could be modeled
following the regression equations obtained. Heat-induced gelation of egg whites and whole eggs
with solid concentration of 25% occurred at 70 and 80 °C independent of the salt content in the
tested range. Both temperatures imply that egg white and whole eggs can form gel model foods
during microwave pasteurization processes. The gel strength and water holding capacity of both
gels increased linearly with solid concentration and was not affected by salt addition. The gel
strength of both egg white and whole eggs under the tested concentrations are adequate for postprocess evaluation. However, egg white with solid concentration of higher than 25% formed gels
with very high strength, making them difficult to cut. The water holding capacities of both gels
were high enough to retain the shape during and after the process. The results demonstrated that
egg whites and whole eggs are suitable to model various foods in microwave pasteurization
processes. In future studies, we will include chemical markers in the gel system to indicate the
heating patterns in prepackaged foods during microwave pasteurization processes.
Acknowledgements
This project was supported by the Agriculture and Food Research Initiative of the USDA
National Institute of Food and Agriculture, grant number #2011-68003-20096. The authors also
thank the Chinese Scholarship Council for providing a scholarship to Wenjia Zhang for her
Ph.D. studies at Washington State University.
57
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62
CHAPTER FOUR
CHEMICAL MARKER M2 (4-HYDROXY-5-METHYL-3(2H)-FURANONE)
FORMATION IN EGG WHITE GEL MODEL FOR HEATING PATTERN
DETERMINATION OF MICROWAVE-ASSISTED PASTEURIZATION
PROCESSING
Zhang, W., Tang, J., Liu, F., Bohnet, S., Tang, Z. (2014)
Journal of Food Engineering, 125: 69-76.
Department of Biological Systems Engineering, Washington State University,
Pullman, WA 99164-6120
Abstract
Microwave-assisted pasteurization (MAP) is a potential thermal processing technology in
which the non-uniform heating presents a challenge. This study evaluated the application of a
chemical marker M2 (4-hydroxy-5-methyl-3(2H)-furanone) in an egg white gel model on the
determination of possible heating patterns in prepackaged foods during MAP processing. The gel
model samples were prepared by heating a homogeneous liquid egg white mixture (25% egg
white, 1% D-ribose, 0.5% L-lysine) at 70° C for 30 min. The chemical marker M2 formation was
studied by heating the gel model samples in 75, 80, 85, 90, 95, and 100 °C oil bath for 5, 10, 15,
20, and 30 min. The marker yields were determined using high-performance liquid
chromatography (HPLC). The color values of the heat-treated samples were measured using CIE
63
L*a*b* and RGB models. The stability of M2 was evaluated at storage temperatures of 4 and 22
°C for 1, 3, 5, and 9 days. In order to validate the application of the new gel model system, the
heating patterns and marker yields of samples taken from 5 different locations of the MAPprocessed gel models at 75 and 100 °C were analyzed. Results showed that the M2 formation in
egg white increased linearly with heat treatment time at 75 to 95 °C, while a slight concavity was
observed for samples treated at 100 °C. Color parameters L* and G values were found to be
significantly correlated with the heating temperatures. During storage, the M2 retention rate
decreased with increasing time and temperature, while samples treated for longer times were
more stable. Salt addition had no significant effect on the M2 yield within the studied timetemperature combination. The color change of egg white gel models due to different M2 yield
after the MAP process could be clearly recognized using a computer vision method.
Keywords: Microwave-assisted pasteurization (MAP); Egg white; Gel model; Chemical marker;
Heating pattern.
1. Introduction
Microwave-assisted thermal processing is a promising food processing technology that
utilizes microwave power as a heating source to inactivate microbes to produce safe prepackaged
foods (Ohlsson, 1991; Salazar-González et al, 2012). The fast volumetric heating from the inside
of the food overcomes the disadvantage of slow heating rates produced in conventional thermal
processes. Therefore, the processing time can be significantly reduced to retain better product
quality (Vadivambal and Jayas, 2010).
64
A major challenge in the development of microwave-assisted thermal processes is the nonuniform heating (Keefer and Ball, 1992; Tang et al., 2008; Koskiniemi et al., 2011). In order to
achieve certain microbial lethality, it is essential to ensure that the cold spots of the food (which
receives the least amount of heat) be adequately heated. Since it is impractical to use temperature
sensors for multi-point temperature monitoring during the process, a chemical marker method
was developed at the United States Army Natick Research Center to map the heating pattern and
locate the cold spots of foods (Kim and Taub, 1993). This method is based on the Maillard
reaction between amino acids and reducing sugars, in which various chemical compounds can be
produced (Hodge, 1953). Three chemical markers, namely, 2,3-dihydro-3,5-dihydroxy-6-methyl(4H)-pyran-4-one
(M-1),
4-hydroxy-5-methy-3(2H)-furanone(M-2)
and
5-
hydroxymethylfurfural (M-3), were identified as time-temperature integrators formed from
different reactants under various conditions. These three chemical markers have been used to
determine the heating uniformity in ohmic heating (Kim et al., 1996), aseptic processing
(Ramaswamy et al., 1996), radio frequency processing (Wang et al., 2004), and microwaveassisted thermal sterilization (MATS) (Prakash et al., 1997; Lau et al., 2003; Wang et al., 2004;
Pandit et al., 2006; Wang et al., 2009).
However, the heat distribution determination using chemical marker analysis by highperformance liquid chromatography (HPLC) is expensive and time consuming, while the
reliability of the computer simulation results needs validation (Resurrection et al., 2013). A
computer vision method was developed by Pandit et al. (2007 a & b) for rapid visualization of
the heating patterns in food after a particular heating process. The method is based on correlating
the color value of the gel model (mashed potato with chemical marker M2) in gray scale with
thermal lethality F0 and M2 yield. All the studies above focused on the sterilization process, in
65
which the product temperature exceeds 110 °C. For microwave-assisted pasteurization (MAP)
process, in which the product temperature is lower than 100 °C, the model food systems used for
MATS system including whey protein gels (Wang et al., 2004 & 2009) and mashed potatoes
(Pandit et al., 2006) can not be applied due to their high gelation temperatures and low
processing temperature of MAP. Therefrore, new model food systems need to be developed for
heating pattern determination of MAP process. In a recent study, Zhang et al. (2013) explored
the use of egg white as a suitable gel model for pasteurization applications. In order for it to be
used as an indicator of heat distribution, certain chemical marker precursors need to be added to
it as color agents, so that color change of the model system can be detected after processing
using the computer vision system. Among the three identified chemical markers, M2 is formed
by rearrangement of the Amadori compound by 2, 3-enolization after the reaction between
reducing sugar and amino acid when the environmental pH is higher than 4.5 (Kim et al., 1996).
It is thus suitable to be used in the alkaline condition of egg white mixture. Moreover, the yield
of M2 can be relatively high to ensure enough color change especially when using ribose as a
reactant. Ashoor and Zent (1984) reported that among the different amino acids they studied, Llysine exhibited a high tendency toward Maillard browning. Therefore, in this study, M2 was
chosen as the possible chemical marker and D-ribose and L-lysine as the reactants to reflect heat
distribution in the egg white gel model system for the MAP process.
The objectives of this study were to investigate the formation of chemical marker M2 in egg
white gel model when using D-ribose and L-lysine as reacting chemicals under pasteurization
conditions, in order to correlate the color change with processing temperature, and to validate the
application of the new gel model system in heating uniformity evaluation of the MAP process
using a computer vision method. Since salt needs to be added to the gel model system to obtain
66
different dielectric properties to model various foods (Zhang et al., 2013), the effect of salt
addition on the M2 yield was also investigated. Moreover, the stability of the chemical marker
M2 during storage was studied to understand the effect of storage time and temperature on the
accuracy of the heating pattern results using the computer vision method.
2. Materials and methods
2.1. Sample preparation
In order to produce homogeneous egg white gel model with enough gel strength,
commercial “Just Whites” all natural egg white powder (0% total fat, 2% sodium; Deb-El Foods
Corporation, Elizabeth, NJ) was used to prepare the liquid egg white mixtures with a 25% solid
content. A predetermined amount of egg white powder was mixed with 35 °C double deionized
(DDI) water on a magnetic stirrer for 3 min. The mixture was then kept in a 35 °C water bath for
20 min for further rehydration. Chemical marker precursors, D-ribose and L-lysine, were chosen
at concentrations of 1% and 0.5%, due to their reaction ratio of 2:1 and a lysine content ranging
from 1.1% to 1.6% in heated and unheated egg albumin at a 25% solid content (Boctor and
Harper, 1968). The chemicals were added to the liquid mixture when it was cooled to room
temperature. The mixture was then stirred for 1h to ensure uniform distribution of the chemical
reactants in the liquid matrix. The foam formed on top of the mixture during stirring was
removed before the homogeneous mixture was filled and sealed in custom-built aluminum
thermal kinetics testing (TKT) cells (Fig. 4.1) designed at Washington State University. The
TKT cells have an inner diameter of 50 mm and an inner height of 5 mm, in order to minimize
the come-up-time (CUT, time needed for the sample temperature to achieve the processing
temperature) and to get enough samples for color measurement after the heat treatments. The
67
samples in TKT cells were heated in a 70 °C water bath for 30 min and cooled in ice water
immediately to form gel models. The samples were equilibrated to room temperature prior to the
oil bath treatments.
Fig. 4.1 Schematic diagram of the custom-built aluminum thermal kinetics test (TKT) cell
2.2. Oil bath treatment
In order to cover a likely range of time-temperature combinations for MAP processing, the
egg white gel models were kept sealed in the TKT cells and heated in an oil bath (oil was
68
replaced by ethylene glycol as the heating medium) at 75, 80, 85, 90, 95, and 100 °C. A 0.1 mm
diameter type-T (copper-constantan) thermal couple (Omega Engineering, Stamford, CT) was
inserted through the top lid to 1 mm under the surface of the gel model to monitor the sample
temperature during heating. The CUT, which was defined as the time for the sample temperature
to reach 0.5 °C below the set temperature, was around 2 to 2.5 min. Timing was started after the
CUT. After each heating time interval of 5, 10, 15, 20, and 30 min, the heating cells were taken
out of the water bath and cooled immediately in ice water to minimize the thermal effect during
cooling.
2.3. Marker yield analysis using HPLC
The egg white gel model samples were taken out of the TKT cells and equilibrated to room
temperature. A sample weight 0.2 g was taken out precisely from the region at surface of each
gel sample around the thermal couple sensor tip for HPLC analysis. The gel sample was ground
in 2 mL extraction buffer (10 mM H2SO4) using a glass mortar and pestle. After grinding, the
extraction mixture was transferred into a 2 mL plastic centrifuge tube and centrifuged at 14000
rpm for 10 min using an Eppendorf centrifuge (Eppendorf AG, Hamburg, Germany). The
supernatant was collected and filtered through a 0.45 μm PTFE syringe filter (Pall Corporation,
Port Washington, NY), and then sealed in a C4011-1w glass HPLC sample vial (National
Scientific Company, Rockwood, TN).
The M2 yield was determined using an Agilent 1100 HPLC system (Agilent Technology,
Santa Clara, CA) equipped with a diode array detector. 0.25 µL of each marker sample was
injected into the HPLC system by an automatic injection system and flowed through a 100×7.8
mm fast acid analysis column (Bio-Rad Laboratories, Hercules, CA) with 10 mM H2SO4 mobile
69
phase at a rate of 1mL/min. The detecting wavelength was set at 285 nm (Kim and Taub, 1993).
An M2 standard curve was obtained by running commercial M2 (Sigma-Aldrich Co. LLC, St.
Louis, MO) solutions prepared at different concentration levels (Fig. 4.2) by the same HPLC
procedure. The marker yield (mg marker per g of sample) was calculated as:
Marker yield 
Peak area Volume of extract (2 mL)

55235
Sample weight (g)
(1)
Peak Area (mAu*s)
6.E+04
5.E+04
4.E+04
3.E+04
y = 55235x
R² = 0.994
2.E+04
1.E+04
0.E+00
0
0.2
0.4
0.6
0.8
1
1.2
M2 concentration (mg/mL)
Fig. 4.2 Standard curve of chemical marker M2 (4-hydroxy-5-methyl-3(2H)-furanone) in 10 mM
sulfuric acid buffer
2.4. Color value determination
A computer vision system (CVS) was used for the sample color analysis (Fig. 4.3). The
CVS consisted of an EOS D60 digital camera (Canon Inc., Melville, NY), a 910-20 Copystand
(Bencher, Inc., Antioch, IL) as sample and camera stand, an ALZO 300 Table Top Studio with
24" Riser Platform and 2 "Cool Lites" (Akces Media LLC, Bethel, CT) as lighting system, and a
70
desktop computer with image analysis software. The lights were amounted on both sides of the
sample with a height of 100 cm. The digital camera was mounted downwardly at a height of 120
cm above the sample stand. The lights of the lighting system were turned on 15 min before each
image taking process to warm up the light bulbs for consistent light intensity. The fresh and oil
bath treated gel model samples (diameter = 50 mm, height = 5 mm) were taken out of the TKT
cells and placed on a black background.
Fig. 4.3 Components of the computer vision system for sample color and heating pattern analysis
According to Yam and Papadakis (2004), among the three mostly used color models (CIE
L*a*b*, RGB, and CMYK), L*a*b* color parameters are independent of the input or output
devices, while RGB colors are device dependent but most closely resemble the way that human
eyes perceive color. Therefore, the L*a*b* and RGB color parameter values were used to study
the correlation between color values and treatment temperatures. In the L*a*b*model, L* stands
for luminance or lightness component, while a*and b* are chromatic parameters which stand for
green to red and blue to yellow, respectively. In the RGB model, the R, G, and B values stand for
the intensity of the three primary color spectrum of red, green and blue, respectively (Leόn et al.,
2006). On each sample image, the color values of 3 points were obtained using the histogram
71
tool in CS6 Photoshop Software (Adobe system, Inc., San Jose, CA) to represent the color of the
whole sample.
2.5. Effect of salt addition on M2 yield
A predetermined amount of salt was added to the liquid egg white mixtures prepared as
mentioned above to a final salt addition of 0, 50, 100, or 200 mM. The liquid mixtures were
filled and heated in the TKT cells at 70 °C for 30 min to form gel models. The gels were then
heated in a 90 °C oil bath for 10, 20, or 30 min. Samples without salt addition were used as a
control for each treatment. The chemical marker M2 yield in the heated gel samples were then
determined using the HPLC method as mentioned above.
2.6. Storage stability of M2
The egg white gel model samples heat-treated at 85 °C for 5, 10, 15, 20, and 30 min were
used to study the effects of storage time and temperature on the stability of M2 formed in egg
white gel model. The marker samples were extracted following the same extraction procedures
as mentioned above, sealed in glass HPLC vials, and stored at 4 °C and 22 °C in dark for 1, 3, 5,
and 9 days. The chemical marker M2 concentrations were monitored during storage after each
storage time interval. The storage ability of M2 was represented by the retention rate value,
which was calculated as:
Rentention rate 
Cs0- Cs
 100%
Cs0
(2)
where Cs0 is the M2 concentration of freshly treated samples, and Cs is the M2 concentration of
the sample after storage.
72
2.7. Microwave-assisted pasteurization (MAP) treatment
8 oz egg white liquids (without salt addition) were filled in to 10 oz plastic trays (14 cm×9.5
cm×3 cm) and heated in a 70 °C water bath for 30 min to form the egg white gel models. After
heating, the gel models were cooled at 4 °C and then vacuum-sealed in 8-oz plastic pouches (16
cm×12 cm) for MAP treatments. The single mode 915 MHz MATS (Microwave-Assisted
Thermal Sterilization) system developed at Washington State University was used to validate the
application of the new gel model system at pasteurization temperatures. The MATS system
consisted of pre-heating, microwave heating, holding, and cooling sections. In order to ensure
the consistence of the process, the MATS system was warmed up for 10 min before running.
After warming up, the temperature of circulating water inside the system was set up to obtain
desired process temperatures (75 and 100 °C, which were respectively the lowest and highest
temperatures used in the oil bath treatment, monitored at the inlet of the microwave heating
cavity). The gel models in pouches were placed on the food package conveyor belt and loaded to
the pre-heating section (30 °C) for pre-conditioning with hot water. After 20 min preheating, the
pouches were moved through the microwave heating cavities with a conveyor speed of 40
inch/min. The microwave power outputs of each cavity were set at 2.5kW, 3.1kW, 1.5kW, and
1.4kW for desired process temperature of 75 °C, or 4.6kW, 6.4kW, 2.5kW, and 2.4kW for
100 °C. The sample temperature was rapidly increased by both microwave power and circulating
water. The gel models were then moved to a holding section with circulating water of 75 or
100 °C. A cooling section was followed where the samples were cooled down using tap water
(25 °C) and unloaded from the system.
73
2.8. Heating pattern analysis for the MAP processed gel models
According to the former experimental data (Tang et al., 2008) and computer simulation
results (Resurreccion et al., 2013), the cold spots of MATS system were always in the middle
layer. Therefore, the MAP processed gel models were cut horizontally in the middle. The middle
layer images of the gel models were taken using the computer vision system described above.
The software for heating pattern analysis (Pandit et al., 2007) included a CS6 Photoshop (Adobe
system, Inc., San Jose, CA) and an IMAQ vision builder software (National Instrument Product,
Austin, TX). The analysis was based on a script built in the IMAQ software developed by Pandit
et al. (2007).
2.9. Statistical analysis
The marker yield, color values, and kinetic parameters were obtained from replicated
measurements using Microsoft Excel (Microsoft Corporation, Redmond, WA) and were shown
as Means ± Standard deviation. The version 14.1 Minitab software (Minitab Inc., State College,
PA) was used to obtain the correlation coefficients and ANOVA tests with a significance level of
P = 0.05.
3. Results and discussion
3.1. M2 formation
The chemical marker M2 yields in egg white gel models treated at 75, 80, 85, 90, 95 and
100 °C for 5, 10, 15, 20, and 30 min are shown in Fig. 4.4. The marker yield increased with both
heating time and temperature. Within 30 min of heat treatment, the chemical marker M2 yield
74
increased linearly at temperature of 75 to 95 °C (R2 >0.98). In the review by van Boekel (2001)
on kinetics aspects of Maillard reaction, the formation of the relatively more stable intermediate
and advanced Maillard reaction products (MRP) were reported to follow zero-order kinetics,
which was the same as shown in our results at 75 to 95°C. The reason could be that the amounts
of these MRPs were usually low when compared to the concentrations of the reaction precursors.
However, when the treatment temperature increased to 100 °C, a slight concavity was observed
on the curve showing a deviation from zero order kinetics. Some researchers have reported that
the formation of M1 in broccoli (Kim and Taub, 1993) and M2 in whey protein gels (Lau et al.,
2003) and mashed potatoes (Pandit et al., 2006) followed first order kinetics at sterilization
temperatures (116, 121, 126, and 131 °C). It could be inferred that with the increase of
processing temperature, the larger amount of M2 production began to become a limiting factor
which reduced the reaction rate.
0.6
75 °C
Marker yield (mg/g sample)
0.5
80 °C
85 °C
0.4
90 °C
95 °C
0.3
100 °C
0.2
0.1
0
0
5
10
15
20
25
30
35
Time (min)
Fig. 4.4 Chemical marker M2 yield in egg white gel models heat-treated at various pasteurization
temperatures (n=4)
75
3.2. Correlation between color parameters and temperature
The correlation coefficients between color parameters of the heat treated samples (including
L*, a*, b*, and R, G, B values) and temperatures (75~100 °C) at each treatment time of 5 to 30
min are summarized in Table 1. For all time intervals, significant negative correlations were
found between the temperatures and color parameters L*and G values (P<0.05). L * represents the
luminance of the sample in the L*a*b* color system, while the G value represents the green color
in the RGB model. The negative correlations indicated that after a certain time of heat treatment,
the luminance and greenness of samples processed at higher temperatures were less intense.
Combining with the result that the marker yield increased with heat treatment temperatutre, a
negative correlation between the L*and G values with marker yield can be deduced, which
indicates that the locations on processed gel models with higher L* or G values receives lower
amount of thermal energy. Therefore, for a gel model processed using the MAP system, it is
possible to analyze its heating pattern using certain statistical software to locate the cold spots as
locations with the highest L* or G values.
3.3. Effect of salt on M2 yield
Salt addition was used to adjust the dielectric loss factor of the egg white gels to model
various foods with different dielectric properties (Zhang et al., 2013). The effect of salt addition
on the M2 yield in egg white gel model is shown in Fig. 4.5. No significant difference was found
for samples with different salt addition (0 to 200 mM) and heated at 90 °C for 0, 10, 20, and 30
min (P>0.05). The results agreed well with the report of Pandit et al. (2007) that salt addition of
1% did not affect the yield of M2 in mashed potato when heated at 121 °C. Thongraung and
Kangsanan (2010) also reported that the addition of salt (0.5~2.5%, w/v) inhibited the formation
76
of final stage Maillard reaction products but showed no significant effect on the intermediate
Maillard reaction prodcts. It can be concluded that salt additon will not change the heating
pattern results of the egg white gel models.
Marker yield (mg/g sample)
0.3
0 mM
0.25
50 mM
100 mM
0.2
200 mM
0.15
0.1
0.05
0
0
10
20
30
Heating time (min)
Fig. 4.5 Effect of salt addition on the M2 yield in egg white gel model when heated in 90 °C
water bath for 0, 10, 20, and 30 min (n=4)
3.4. Stability of M2 during storage
The stability of M2 (extracted from egg white gel models heated at 85 °C) represented by
the retention rate during storage at 4 °C and 22 °C are shown in Fig. 4.6a and 4.6b, respectively.
The retention rate of M2 decreased with storage time for samples stored at both temperatures.
Since M2 is an intermediate product of the Maillard reaction (Kim and Taub, 1993), a certain
amount of the M2 was transformed into other chemical compounds under the storage conditions.
77
The effect of storage temperature on the M2 retention rate was also significant. Ata storage
temperature of 22 °C, the decrease of M2 retention rate was more significant than that at 4 °C.
After 9 days of storage at 4 °C, the retention rate of M2 extracted fromthe sample treated for 5
min decreased to 80.4%, while that of the same sample stored at 22 °C decreased to 66.4%. The
result suggested that the degradation or transformation of M2 into other compounds was favored
at higher storage temperatures.
The retention rate of M2 was also affected by the heat treatment time. After a certain storage
time, the M2 retention rate for samples heat treated for longer times were larger than those
treated for shorter time intervals. As shown in Fig. 4.6a, after 9 days of storage at 4 °C, the
retention rate of M2 extracted from the sample heat treated for 5 min decreased to 80.4%, while
that of the sample heat-treated for 30 min was 97.9%. The reason for the difference could be the
much higher original M2 concentrations in the samples heat-treated for longer times before
storage. A similar phenomenon was found for samples stored at 22 °C. The retention rate of the 5
min treated sample decreased to 66.4%, while that of the samples treated for 15, 20, and 30 min
ranged from 90.9~93.5% without significant difference (P>0.05).
It can be concluded from the result that, in order to ensure the accuracy and reliability of the
chemical marker method, the storage time and temperature after the processing of the gel model
must be carefully controlled. For microwave processings at higher temperatures and for longer
times, the gel models can stay relatively stable after storage at 4 °C for a few days. For example,
M2 from the gel models heated at 85 °C for 20 and 30 min could be considered as stable after
storage at 4 °C for up to 9 days since the M2 retention rates were higher than 95%. However, for
processes at lower temperatures or for shorter times, it is essential to make sure the marker yield
or heating pattern analysis is carried out shortly after the microwave process.
78
100
Retention rate (%)
95
90
Treatment
time 5 min
85
10 min
15 min
20 min
30 min
80
(a)
75
0
2
4
6
8
10
Storage time (days)
Retention rate (%)
100
90
Treatment
time 5 min
80
10 min
15 min
20 min
70
30 min
(b)
60
0
2
4
6
8
10
Storage time (days)
Fig. 4.6 Retention rate of chemical marker M2 (extracted from egg white gel model samples
treated at 85 °C for various times) during storage at 4 °C (a) and 22 °C (b) for 1, 3, 5, and 9 days
(n=4)
79
3.5. Validation of M2 application in the MAP process
The color changes of gel model samples in trays after MAP process at 75 and 100 °C were
analyzed using computer vision system and the results were shown in Fig. 4.7a and 4.7b. The
figures were used to differenciate the color change of different locations due to the accumulated
M2 production from various time-temperature profiles. Since the temperature at different
locations were not monitored, no temperature-indicating scale was included. The differences
between heating patterns shown in Fig. 4.7a and 4.7b were due to the different processing
temperatures and microwave power setups.
1
3
2
5
4
4
5
3
1
2
(a)
(b)
Fig. 4.7 Heating pattern results of egg white gel models (middle layer) with chemical marker M2
after microwave-assisted pasteurization processing in 915 MHz single mode microwave system
at 75 °C and 100 °C (Location number 1: Blue, 2: Aqua, 3: Green, 4: Yellow, 5: Red)
80
Based on the application of computer vision method on the heating pattern determination of
MATS system, the parts of the gel model in red color in the images were defined as hot regions
(hot spots) which received the highest amount of thermal energy during the processes, whereas
the parts in blue color were cold regions (cold spots) which have received the lowest amount of
thermal energy. Other colors between blue and red described the regions which have received
midium amount of thermal energy. In order to ensure that the computer vision method results are
also reliable for the new model system for MAP process, the color parameters L* and G values
and the M2 yield of samples taken from the MAP-processed egg white gel models (from
locations 1 to 5 as shown in Fig. 4.7a and 4.7b) were determined. The results of gel models
MAP-processed at 75 and 100 °C are shown in Figs. 4.8 and 4.9, respectively. As shown in Fig.
4.8a, the marker yield of locations 1, 2, and 3 for 75 °C processed gel model were close due to
their low concentration level, while those of locations 4 and 5 were much higher and location 5
showed the highest M2 yield. It could be concluded that locations number 5 obsorbed the highest
amount of heat during the process, followed by location 4 and then the other three. Therefore,
location 5 can be concluded as the hot spot for 75 °C processed gel model. The L* and G values
of the same sample decreased from locations 1 to 5, which agreed with our findings that the L*
and G values negatively correlated with the process temperature. Furthermore, it can be
concluded that the computer vision method had a higher sensitivity than the marker yield
analysis by HPLC. For gel model samples MAP-processed at 100 °C, the marker yield also
increased from locations 1 to 5, while the L* and G values decreased. A linear relationship was
found between marker yield with both L* and G values (Fig. 4.9), which proved the proposed
method of using L* and G values for cold/hot spot determination.
81
Marker yield (mg/g sample)
0.12
5
0.1
0.08
0.06
4
0.04
1
2
3
1
2
3
0.02
(a)
0
0
4
5
6
Location number
100
260
1
2
255
3
96
G value
L* value
98
4
5
94
250
2
245
3
4
5
240
92
90
1
235
(b)
(c)
230
0
1
2
3
4
Location number
5
6
0
1
2
3
4
Location number
5
6
Fig. 4.8 Chemical marker M2 yield (a) and color parameters L* (b) and G values (c) of egg white
gel models after microwave-assisted pasteurization process at 75 °C (n=4)
82
100
250
1
1
L* value
5
y = -48.34x + 97.34
R² = 0.989
85
80
4
3
90
4
3
230
(b)
210
0.05
0.1
0.15
0.2
0
0.25
5
y = -133.62x + 248.3
R² = 0.987
220
(a)
0
2
240
G value
2
95
0.05
0.1
0.15
0.2
0.25
Marker yield (mg/g sample)
Marker yield (mg/g sample)
Fig. 4.9 Correlation between chemical marker M2 yield and color parameters G and L* values of
egg white gel model after microwave-assisted pasteurization process at 100 °C (n=4)
In order to further validate the cold or hot spots, a mobile metallic ELLAB sensor was used
to monitor the temperatures of cold/hot spots determined from the heat distribution results
obtained by using computer vision system. The ELLAB sensors installed inside a protective
metal tube with 2mm diameter and 50 mm length (Luan et al., 2013) was inserted into the
horizontal middle layer of the egg white gel model at the cold and hot spots predetermined for a
90 °C process. The samples in trays were then MAP-processed at microwave heating and
holding temperatures of 90 °C. Results showed that the temperatures of hot spots were all higher
than those of the cold spots, which well verified the cold and hot spot locations. With the
assistance of chemical marker M2 and egg white gel model system to locate the cold and hot
spots, the temperatures of the cold and hot spots could be monitored for calculation of required
thermal lethality or quality parameters to develop safe, reliable, and uniform microwave assisted
pasteurization processes.
83
4. Conclusions
The formation of chemical marker M2 in egg white gel models at pasteurization
temperatures of 75 to 100 °C increased with both time and temperature. The retention rate of M2
samples extracted from the heat-treated egg white gel models decreased with increasing storage
time. The retention rate of samples stored at 4 °C was much higher than that at 22 °C. Samples
treated at higher temperatures or for longer times could stay stable during 4 °C storage. However,
for the more mild microwave-assisted pasteurization processes, it is recommended to analyze the
gel model samplesshortly after the process. With the addition of 1% D-ribose and 0.5% L-lysine,
the egg white gel models at the central cut by MAP-process at temperatures higher than 75 °C
could clearly show the heating pattern by the computer vision method. The negative correlation
between the L* and G color values with the treatment temperatures suggests a possibility of
using statistical methods to locatethe cold spots of the gel models.
Acknowledgments
This project was supported by the Agriculture and Food Research Initiative of the USDA
National Institute of Food and Agriculture, grant number #2011-68003-20096. The authors also
thank the Chinese Scholarship Council for providing a scholarship to Wenjia Zhang for her
Ph.D. studies at Washington State University.
84
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87
CHAPTER FIVE
PHYSICAL PROPERTIES OF LOW-ACYL GELLAN GEL AS
RELEVANT TO MICROWAVE ASSISTED PASTEURIZATION PROCESS
Wenjia Zhang1, Juming Tang1*, Shyam Sablani1, Barbara Rasco2, Huimin Lin1, Fang Liu1
1. Department of Biological Systems Engineering, Washington State University,
Pullman, WA 99164-6120
2. UI/WSU bi-State School of Food Science and Human Nutrition, Washington State University,
Pullman, WA 99164-6120
Abstract
Various model foods were needed to be used as chemical marker carriers for the heating
pattern determination in developing microwave heating processes. It is essential that these model
foods have matching physical properties with the food products that will be microwave
processed, such as meat, vegetables, pasta, etc. In this study, the physical properties of low acyl
gellan gel were investigated to evaluate its suitability to be used as a possible model food for the
development of single mode 915 MHz microwave assisted pasteurization processes. These
physical properties included the dielectric properties, gel strength and water holding capacities.
In order to adjust the dielectric constant and loss factor, various amounts of sucrose (0, 10, 30,
and 50%) and salt (0, 100, 200, and 300 mM) were added to 1% gellan gel (with 6 mM Ca2+
addition). Results showed that sucrose and salt addition could be used to adjust the dielectric
constants and loss factor of gellan gels 915 MHz, respectively. Regression equations were
88
developed to predict the relationship between the dielectric properties of gellan gel with sucrose
content, salt content, and temperature (22-100 °C) at 915 MHz, and thus can be used to
determine the formulation of a gellan gel model food to match the dielectric properties of a
certain food that will be processed. The gellan gels with 0, 10, and 30% sucrose content showed
relatively high gel strength for post microwave process handling. However, the addition of 50%
sucrose significantly decreased the gel strength, resulting in highly deformable gels. The water
holding capacities of all the gels increased with increasing sucrose content, while the effect of
salt was not consistent.
Keywords: Low acyl gellan gel; dielectric properties; gel strength; water holding capacity
1. Introduction
915 MHz single mode microwave assisted thermal processing systems have been developed
for producing both shelf stable and refrigerated food products with better quality and nutrition
attributes compared with the traditionally thermal processed foods (Zhang et al., 2013).
However, similar to conventional thermal processing such as canning, it is critical to monitor the
temperature profile at cold spots inside the food package to develop a reliable process which
ensures adequate thermal sterility. Due to the difficulty of heating pattern determination for
microwave processes, different model food and chemical marker systems were developed to
model the real foods and predict the cold spot and hot spot locations (Pandit et al., 2007). Whey
protein gels (WPG) and mashed potatoes are used as model foods for microwave assisted
thermal sterilization (MATS) processes (Lau et al., 2003; Wang et al., 2009; Guan et al., 2004;
89
Pandit et al., 2006). However, due to the relatively high gelation temperature of the model foods
used for MATS processes, new model food systems including egg white and whole egg gel
models were developed for microwave assisted pasteurization (MAP) processes (Zhang et al.,
2013 & 2014).
In order to develop a model food system that can be used for processes at lower than 70 °C,
which also generates a relatively large range of physical property values, other food gel systems
which can be formed at pasteurization temperatures were considered. Gellan gum is an
extracellular polysaccharide secreted by the bacterium Sphingomonas elodea (formerly known as
Pseudomonas elodea) (Pollock, 1993). It is a linear anionic monosaccharide with a repeating unit
of β-D-glucose, β-D-glucuronic acid, and α-L-rhamnose (molar ration 2:1:1) (Kuo and Mort,
1986). Low acyl gellan gum is a deacyled form of the native gellan gel. It is formed when both
acyl groups of the native gellan gum are hydrolyzed when exposed to alkali and high
temperatures. Unlike the high acly gellan gum, low acyl gellan gum forms strong, clear and
brittle gels with addition of proper concentrations of cations (Mao et al., 2000). It has the
advantages of relatively low gelation temperature (Morris et al., 2012) and high gelling
efficiency to produce a wide range of mechanical properties (Sworn, 2009). Therefore, the
physical properties of low acyl gellan gel were studied.
Dielectric properties is one of the important criteria for selecting proper model foods for
microwave heating since it determines how the microwave energy is absorbed, transmitted,
reflected, or concentrated inside a food material (Datta and Anantheswaran, 2001). The relative
permittivity is defined as εr =ε/ε0, which is the ratio of the amount of electrical energy stored in a
material by an applied voltage (ε), relative to that stored in a vacuum (ε0). It consists of two parts
when used in reality and can be expressed as:
90
εr = εr′-jεr″
(1)
where j =  1 ; ɛ rʹ is dielectric constant, which indicates the ability of a material to store
electric energy; ɛ r″ is the dielectric loss factor, which reflects the ability of a material to
dissipate electromagnetic energy into heat. The rate of energy generation per unit volume (Q) in
a food can be calculated from:
(2)
where f is the frequency (Hz), ε0 is the permittivity of free space (8.8542×10-12 F/m), εr″ is the
relative loss factor, and E is the strength of electric field of the wave (V/m). The only published
study that has been done on the dielectric properties of gellan gel was reported by Okiror and
Jones (2012) on the effect of temperature on the dielectric properties of 1% gellan gel containing
0.17% and 0.3% CaCl2. However, in order to use gellan gel as a model food with a wide range of
dielectric properties, it is necessary to have more comprehensive information on how its
dielectric properties can be adjusted. Since the dielectric constant is mainly affected by the free
water inside a food material (Sun et al., 1995), sucrose was considered to be added to the gellan
gel network to decrease the free water contents, so that the dielectric constant values could be
adjusted. Moreover, it was reported that adding sucrose could increase the gellan gel clarity and
strength at proper cation concentration levels (Tang et al., 2001). Therefore, the effect of sucrose
addition of 0-50% was investigated in this study. Salt addition has been widely reported to
increase the dielectric loss factor of food materials (Guan et al., 2004; Wang et al., 2009; Zhang
et al., 2013). Therefore, the effect of 0~300 mM salt addition to gellan gel was also evaluated.
Since the chemical markers (which serve as color agents for heating pattern analysis) need
to be added before the gelation of model food gels, gelation temperature is another important
factor for the evaluation of a possible model food.
91
Due to the relatively low processing
temperature of 70 to 100 °C during MAP processes, it is important that the gelation temperature
of the possible model foods to be lower than 70 °C. Tang et al. (1997a) studied the gelation
temperatures of gellan solutions covering a relatively large cation (Ca2+, 2 to 40 mM) and gellan
gum (0.4 to 2%, w/v) concentration range. The results showed that gellan gel gelation
temperatures varied from 30 to 72 °C depending on the polymer and cation concentrations. They
also developed a mathematical model to predict the gelation temperature of gellan gel with
consideration of gellan gum, monovalent and divalent cation concentrations as:
1
 3.33 10 3  1.34 10 4 log10[ X p ]  2.33 10 4 log10[0.0726 X Na  0.111 X K  X Ca  X Mg ]
Tgel
(3)
where Tgel is the gelation temperature in K, Xp, XNa, XK, XCa, and XMg are concentrations of
gellen gum (%), and concentrations (mM) of added Na+, K+, Ca2+, and Mg2+ (Tang et al., 1997b).
With the addition of 6 mM Ca2+ and 0, 100, 200, and 300 mM Na+, the gelation temperatures of
the 1% gellan gel used in this study were calculated according to Equation 3 as 44.4, 52.7, 57.5,
and 60.9 °C, respectively. Tang et al. (2001) also reported that the addition of sucrose (0~35%,
w/v) increased the gelation temperature of gellan gel by 1.5~3 °C, which indicated the gelation
temperature of gellan gel used in this study be highly possible below 70 °C.
Gel strength and water holding capacities are two other important criteria for model foods
evaluation (Zhang et al., 2013). The gel strength determines whether the gel is strong enough for
post microwave process handling, while water holding capacity indicates the stability of a gel to
hold its original shape during process and storage. The effects of gellan gum, cations, and sugar
contents on the gellan gel texture and water holding capacities have been widely studied
(Moritaka et al., 1991; Tang et al., 1994, 1995, 1996). However, the effect of the combination of
cations and sucrose addition on the gel strength and water holding capacity of gellan gel was not
covered and will be investigated in this study.
92
2. Materials and methods
2.1 Sample preparation
Low acyl gellan gum samples (KELCOGEL F) was provided by CP Kelco Inc. (Atlanta,
GA). Gellan gum powder was slowly added to distilled water at room temperature to obtain a
final dispersion of 1% (w/v) polymer concentration. The mixture was further mixed on a
magnetic stir for 1h, and left at room temperature overnight for better rehydration. The mixture
was then gently heated to 90 °C and became a clear solution. CaCl2·2H2O (J.T. Baker, Avantor
Performance Materials, Inc., Phillipsburg, NJ) was added to the hot solution at 90 °C to obtain a
Ca2+ concentration of 6 mM, to ensure the formation of a strong gellan gel network. Besides
Ca2+, NaCl and/or sucrose were also added to the hot solution following the experimental design
shown in Table 1 to obtain Na+ concentrations of 0, 100, 200, and 300 mM and/or sucrose
concentrations of 0, 10, 30, and 50% (w/v). These solutions were further heated and stirred at 90
°C for 30 s, and allowed to cool at room temperature to around 75 °C and filled into 50 mL
plastic centrifuge tubes. The tubes were left at room temperature overnight to form consistent
gels. The gel samples of batch I and II were later used for dielectric property measurements
while sample batch III were used for the determination of gel strength and water holding
capacity. For each physical property measurement, triplicate sets of samples were prepared.
93
Table 5. 1 Components of the gellan gel samples with different cation and sucrose contents
Sample batch
Ca2+ (mM)
Na+ (mM)
Sucrose (%, w/v)
I
6
0, 100, 200, and 300
0
II
6
0
0, 10, 30, and 50
III
6
0, 100, 200, and 300
0, 10, 30, and 50
2.2 Dielectric properties measurement
Dielectric properties of gellan gel samples were measured using an HP 8752 C Network
Analyzer (frequency range: 300 to 3000 MHz) and 85070B Open-End Coaxial Dielectric Probe
(Agilent Technologies, Santa Clara, CA). A stainless steel tube with sharp edges was used to cut
the samples into cylindrical specimens (diameter=21 mm) to fit in the test cell. The
measurements were carried out following the procedures reported before (Zhang et al., 2013) at
temperatures from 22 to 100 °C with an increment of 10 °C. All measurements were conducted
in triplicate.
2.3 Penetration depth
Penetration depth (Dp) of microwave power is the depth where the incident power decreases
to 1/e (e=2.718) of its original value at the material surface. It indicates the heating uniformity
and is an important parameter for microwave process development. Dp can be calculated as:
Dp 
c
   " 2

2f 2 '     1  1
  ' 



94
(m)
(3)
where c is the speed of light in free space (3×108 m/s), and f is the frequency (Hz) (Buffler,
1993), which is 915 MHz in this study.
2.4 Gel strength measurement
Gel samples in batch III were used for gel strength measurements. A TA-XT2i Texture
Analyzer (Texture Technologies Corp, Scarsdale, NY) equipped with a 25 kg load cell and 40
mm plate probe was used to conduct the uniaxial compression tests for gel strength
determination. A stainless steel tube with sharp edges was used to cut the gel samples into
cylindrical specimens (diameter=21 mm, height=20 mm). The specimens were placed under the
plate probe and deformed at a constant cross head speed of 1 mm/s till 70% deformation. The
probe then returned to the original position with a speed of 2 mm/s. The maximum force (Fmax)
and deformation (ΔLmax) at failure for each gel specimen were obtained. The compression tests
were repeated six times. The true gel stress (σmax) and gel strain (εmax) at failure for cylindrical
specimens during compression tests were calculated according to Hamann (1983) as:
(4)
(5)
where L is the original length of the gel specimen and R is the original radius. Due to the failure
during compression test takes place in shear, the shear stress (τmax) and shear strain (γmax) at
failure can be calculated from:
(6)
(7)
95
where ν is the Possion’s ratio (assumed as 0.5 for impressible gel sample). γmax represents the
extensibility of gel at the point of failure, while τmax reflects the strength of the gel (Tang et al.,
1995).
2.5 Measurement of water holding capacity (WHC)
Small cylindrical specimens (diameter= 5 mm, length=10 mm) were taken from the gels in
sample batch III using a plastic tube. The weight of the specimen was recorded as w 0. The
sample was then placed in Costar Spin-X Centrifuge Tube Filters (Cole-Parmer, Vernon Hills,
IL) with pores of 0.45 μm and centrifuged at 2000 rpm for 5 min (Mao et al., 2001). The weight
of the specimen right after the centrifuge was then recorded as w. Four replicates were conducted
for each measurement. The water holding capacity was calculated as:
(8)
2.6 Statistical analysis
The results for each physical property obtained from replicated measurements were
processed using Microsoft Excel (Microsoft Corporation, Redmond, WA) and were shown as
Means ± Standard deviation. The version 14.1 Minitab software (Minitab Inc., State College,
PA) was used to obtain the correlation coefficients and ANOVA tests with a significance level of
p = 0.05.
96
3. Results and discussion
3.1 Combined effect of frequency and temperature on dielectric properties
The dielectric constant and loss factor of 1% gellan gel with 6 mM Ca2+ addition at
frequencies between 300 and 3000 MHz and temperatures between 22 and 100 °C are shown in
Fig. 5.1. The dielectric constant of gellan gels decreased slightly with frequency and
temperature, while the changes of loss factor were more complicated. The dielectric loss factor is
mainly contributed by two parts, namely, the dipole loss (εd″) and ionic loss (εσ″). Ionic loss is
caused by the moving charged particles in an alternating electric field due to dissolved
electrolytes. It typically predominates at frequencies lower than 1 GHz (Wang et al., 2011). The
relationship of ionic loss and frequency (f) was deducted by Guan et al. (2004) as:
(9)
where for a certain material with a certain ionic conductivity (σ), the constant C is:
(10)
This can be used to explain the linear decrease of loss factor with increasing frequency at lower
than 500 MHz shown in Fig. 5.1b. The loss factor started to increase at a certain frequency value
between 500 to 200 MHz depending on different temperature, which indicated the increasing
effect of dipole loss caused by the dipole polarization of water molecules. Similar phenomena
were found in many other researches for materials such as mashed potato (Guan et al., 2004),
gellan gel (Okiror and Jones, 2012), and egg white (Zhang et al., 2013). At frequencies higher
than 2000 MHz, the loss factor increased with increasing frequency while decreased with
temperature, showing the predominant role of dipole loss.
97
85
40 ⁰C
50 ⁰C
75
60 ⁰C
70
70 ⁰C
80 ⁰C
65
90 ⁰C
100 ⁰C
60
30 ⁰C
Dielectric loss factor
Dielectric constant
22 ⁰C
30 ⁰C
80
55
50
22 ⁰C
40 ⁰C
50 ⁰C
60 ⁰C
70 ⁰C
80 ⁰C
90 ⁰C
100 ⁰C
(b)
(a)
5
300
300
3000
Frequency (100 MHz)
3000
Frequency (100 MHz)
Fig. 5.1 Effect of frequency and temperature on the dielectric constant (a) and loss factor (b) of
1% gellan gel with 6 mM Ca2+ addition
3.2 Effect of sucrose content on the dielectric properties
The overall effects of temperature and frequency on dielectric properties of gellan gel with
30% sucrose content are shown in Fig. 5.2. Adding sucrose to the gellan formulation significant
reduced the dielectric constant (Fig. 5.2a). The declining of dielectric constant with frequency
was more significant at lower temperatures and higher frequencies. Sucrose addition also
changed the overall effect of temperature and frequency on the dielectric loss factor (Fig. 5.2b).
The frequencies at which the loss factor changed from decreasing to increasing with frequency
moved to lower frequency range of 400 to 700 MHz depending on gel temperature. This
phenomenon could be attributed to the change of relaxation time due to sucrose addition. Adding
sucrose reduced the free water content of the gellan gel and increased the amount of water bound
to sucrose molecules which had much longer relaxation times, and therefore, lower relaxation
frequencies (Tang, 2005).
98
30
70
22 ⁰C
22 ⁰C
30 ⁰C
30 ⁰C
Dielectric loss factor
Dielectric constant
75
40 ⁰C
65
50 ⁰C
60 ⁰C
70 ⁰C
60
80 ⁰C
90 ⁰C
55
50
100 ⁰C
(a)
300
3
Frequency (100 MHz)
40 ⁰C
50 ⁰C
60 ⁰C
70 ⁰C
80 ⁰C
90 ⁰C
300
3000
100 ⁰C
(b)
Frequency (100 MHz)
3000
Fig. 5.2 Effect of frequency and temperature on the dielectric constant (a) and loss factor (b) of
1% gellan gel with 6 mM Ca2+ and 30% sucrose addition
The effect of sucrose content on the dielectric properties of gellan gel at 22 °C throughout
the frequency range of 300 to 3000 MHz is shown in Fig. 5.3. The dielectric constants of gellan
gel without sucrose were similar to that of distilled water, which was also found by Okiror and
Jones (2012) for gellan gel with 0.17% or 0.3% Ca2+. Morris et al. (2012) stated that most water
in biopolymer networks was free water, and therefore these biopolymers would show similar
dielectric properties as free water. The dielectric constants decreased with increasing sucrose
content at all frequencies, and the slopes of samples with higher sucrose contents were more
inclined (Fig. 5.3a). It is likely that adding sucrose reduced the mobility of water due to the
sugar-water association (Morris et al., 2012). The loss factor of gellan gel with no sucrose first
decreased and then increased with increasing frequency. With the increase of sucrose content,
the point where the loss factor started to increase moved to lower frequencies, which could be
attributed to the decrease of relaxation frequency due to the increase of sucrose content.
99
20
80
18
70
0
10%
60
30%
50
50%
40
30
Dielectric loss factor
Dielectric constant
90
(a)
300
16
10%
12
30%
10
50%
8
6
Frequency(100 MHz)
0
14
(b)
300
3000
3000
Frequency (100 MHz)
Fig. 5.3 Effect of sucrose content on the dielectric constant (a) and loss factor (b) of gellan gel at
22 °C between 300 and 3000 MHz
At 915 MHz (the operation frequency of the MAP system), the changes in dielectric
constant and loss factor of samples with sucrose over the tested temperature range of 22 to 100
°C are shown in Fig. 5.4. Similar as at the other frequencies, the dielectric constant significantly
decreased with sucrose content. The room temperature dielectric constants were reduced from
79.6 for samples without sucrose to 76.4, 67.4, and 48.9 for samples with 10, 30, and 50%
sucrose contents, respectively. The effect of sucrose content on dielectric loss factor depended on
temperature. At temperatures lower than 50 °C, the loss factor increased with increasing sucrose
content. However, an opposite trend was found at temperatures higher than 60 °C. Similar results
were found for gellan gels at 2450 MHz (data not shown). The regression equations relating the
dielectric constant and loss factor with temperature and sucrose contents of the gellan gel were
developed as:
(11)
100
where a, b, c, d, e, and f are constants, T is temperature (22-100 °C), and S is sucrose content (050%). The regression constants results and the coefficients of determination R2 values are
summarized in Table 2.
0
85
0
16
30%
75
50%
65
55
(a)
45
10%
Dielectric loss factor
Dielectric constant
10%
14
30%
50%
12
10
8
(b)
6
10 20 30 40 50 60 70 80 90 100 110
10 20 30 40 50 60 70 80 90 100 110
Temperature (°C)
Temperature (°C)
Fig. 5.4 Effect of sucrose content on the dielectric constant (a) and loss factor (b) of gellan gel at
915 MHz between 22 and 100 °C
Table 5. 2 Regression constants and coefficients of determination in Eq. (11) for dielectric
constant and loss factor of gellan gel with sucrose or salt addition at 915 MHz
Sucrose
Salt
a
b
c
d
e
f
R2
ɛ′
85.56
-0.2528
-0.4164
0.0040
0
-0.0045
0.988
ɛ ″
9.186
-0.0564
0.0979
-0.0030
0.0010
0.0015
0.984
ɛ′
84.78
-0.2559
-0.0225
0.0001
0.0002
4.11E-05
0.997
ɛ ″
10.44
-0.1185
0.1300
0.0036
0.0015
-9.8E-05
0.999
101
3.3 Effect of salt content on the dielectric properties
Fig. 5.5 shows the combined effect of frequency and temperature on the dielectric properties
of gellan gel with 200 mM salt. The dielectric constant of the gellan gel followed similar trend as
that of the ones without salt addition as shown in Fig. 5.1a. Much higher loss factor values were
found as comparing with the gellan gel with no salt addition. Due to the predominant role of
ionic loss for gellan gel with the high ion concentrations, the loss factor decreased almost
linearly throughout the tested frequency range (Tang, 2005). Deviation from the linear log-log
curve was noticed at frequencies higher than 1000 MHz at lower temperatures where the dipole
loss started to show its importance.
1000
80
22 ⁰C
22 ⁰C
30 ⁰C
30 ⁰C
Dielectric loss factor
Dielectric constant
85
40 ⁰C
75
50 ⁰C
70
60
70 ⁰C
80 ⁰C
90 ⁰C
(a)
50 ⁰C
60 ⁰C
100
60 ⁰C
65
40 ⁰C
80 ⁰C
90 ⁰C
100 ⁰C
(b)
100 ⁰C
55
70 ⁰C
10
300
Frequency (100 MHz)
3000
300
Frequency (100 MHz)
3000
Fig. 5.5 Effect of frequency and temperature on the dielectric constant (a) and loss factor (b) of
1% gellan gel with 200 mM salt addition
The effect of salt addition of up to 300 mM on the dielectric properties of gellan gel is
shown in Fig. 5.6. Adding salt reduced the dielectric constant, but significant differences were
not found among samples with different salt contents. Mudgett (1986) attributed the effect of salt
on dielectric constants to the reduced polarization of water by dissolved ions. The dielectric loss
102
factor value increased significantly with increasing salt content. The loss factor of gellan gel with
no salt addition did not change linearly with frequency. For samples with 100 to 300 mM salt
addition, the loss factor-frequency curves were linear at frequencies lower than 900 MHz, due to
the major effect of ionic loss by high salt concentration.
500
82
Dielectric loss factor
Dielectric constant
80
78
0 mM
76
100 mM
200 mM
74
72
(a)
300
300 mM
100 mM
200 mM
300 mM
50
5
Frequency (100 MHz)
3000
0 mM
(b)
300
Frequency (100 MHz)
3000
Fig. 5.6 Effect of salt content on the dielectric constant (a) and loss factor (b) of gellan gel at
22 °C between 300 and 3000 MHz
Fig. 5.7 shows the effect of salt addition on the dielectric properties of gellan gel at 915
MHz at 22 to 100 °C. The dielectric constants decreased with temperature and showed no
significant difference among samples with different salt contents (p>0.05). But adding salt
significantly increased the dielectric loss factor of the gellan gel samples. For example, the room
temperature loss factor value increased from 8.38 for sample without salt to 27.3, 47.1, and 62.8
for samples with 100, 200, and 300 mM salt contents, respectively. The difference was even
larger at higher temperatures. At 100 °C, the loss factor values increased from 12.7 for gellan gel
with no salt to 60.5, 108.2, and 150.4 for samples with 100, 200, and 300 mM salt addition,
respectively. Similar results were found for samples at 2450 MHz (data not shown). Regression
103
equations were also obtained to predict the relationship between dielectric properties, salt
content, and temperature as shown in Equation 11, whereas S represents salt (0-300 mM) instead
of sucrose content. The regression results were also summarized in Table 2.
Dielectric constant
80
0 mM
160
0 mM
100 mM
140
100 mM
200 mM
75
300 mM
70
65
60
55
Dielectric loss factor
85
(a)
10
20
120
40
50
60
70
80
90 100 110
300 mM
80
60
40
20
0
30
200 mM
100
(b)
10 20 30 40 50 60 70 80 90 100 110
Temperature (ºC)
Temperature (ºC)
Fig. 5.7 Effect of salt content on the dielectric constant (a) and loss factor (b) of gellan gel at 915
MHz between 22 and 100 °C
3.4 Effects of sucrose and salt contents on penetration depth
The penetration depths of gellan gel with sucrose addition at 915 and 2450 MHz are shown
in Fig. 5.8. The penetration depth values of microwave at 915 MHz were higher than those at
2450 MHz, which agreed with many research results that penetration depths decreased with
frequency (Wang et al., 2003; Guan et al., 2004). Due to the decrease of dielectric constant with
temperature for all samples at 915 MHz, the pattern of penetration depth with temperature was
almost an opposite image of the pattern of loss factor shown in Fig. 5.4b. The effect of sucrose
104
content and temperature on penetration depth at 2450 MHz was much simpler, where increases
of penetration depth with temperature were found for all samples.
25
Penetration depth (mm)
Penetration depth (mm)
60
50
40
0
10%
30
20
30%
50%
(a)
10 20 30 40 50 60 70 80 90 100 110
20
15
0
10
10%
30%
5
0
50%
(b)
10 20 30 40 50 60 70 80 90 100 110
Temperature (°C)
Temperature (°C)
Fig. 5.8 Penetration depth of gellan gel with sucrose addition at (a) 915 MHz and (b) 2450 MHz
between 22 and 100 °C
The effect of salt content on the penetration depth of gellan gel at 915 and 2450 MHz is
shown in Fig. 5.9.
At 915 MHz, the penetration depth of all samples decreased with
temperature. The increase of salt content significantly reduced the penetration depth of gellan gel
from 55.7 mm to 17.0, 10.1 and 7.8 mm at 100, 200, and 300 mM salt levels at 22 °C,
respectively. The result could be attributed to the significant increase of loss factor with
increasing salt content. The decrease of penetration depth with temperature was caused by the
increase of loss factor while decrease of dielectric constant. Similarly, the penetration depth of
gellan gel also decreased with salt content at 2450 MHz. However, an increase of penetration
depth with temperature was found for gellan gels with no salt, while decreasing trends were
found for all other samples (Fig. 5.9b).
105
0
25
100 mM
50
200 mM
300 mM
40
30
20
10
0
(a)
Penetration depth (mm)
Penetration depth (mm)
60
20
0
100 mM
15
200 mM
300 mM
10
5
(b)
0
10
10 20 30 40 50 60 70 80 90 100 110
20
30
40
50
60
70
80
90 100 110
Temperature (°C)
Temperature (°C)
Fig. 5.9 Penetration depth of gellan gel with salt addition at (a) 915 MHz and (b) 2450 MHz
between 22 and 100 °C
3.5 Combined effects of sucrose and salt contents on gel strength
The combined effect of sucrose and salt content on gel strength of 1% gellan gel in terms of
shear stress and shear strain at failure is shown in Fig. 5.10. The gel strength decreased with salt
content at all sucrose levels except the gel with 10 mM salt and 30% sucrose, which presented
the highest gel shear stress value. For samples with the same salt content, the shear stress of all
samples increased with increasing sucrose content of up to 30%, while the addition of 50%
sucrose significantly decreased the gel strength. Many researches have been done to illustrate the
importance of cations on the mechanism of gellan gel formation (Tang et al., 1994 & 1995;
Gibson & Sanderson, 1997). Tang et al. (2001) also reported the stabilizing effect of sucrose on
gellan gel formation for packing the double gellan helices in the same way as cations. Morris et
al. (2012) summarized in their review that high levels of sucrose promoted the association of
gellan gel polymer chains by replacing most of the water in the gel-water mixture. The gel
106
strength of 0.30, 0.75, and 1.2 wt % gellan gel was reported to increase with sucrose addition of
up to 25% (Bayarri et al. 2002). However, when both cations and sucrose are presented in the gel
network, optimum levels of both cations and sucrose are required for maximum gel strength. At
cation concentration levels lower than the concentration to naturalize the negative charges of
gellan polymer chain, the increase of sucrose strengthens the gel. However, when the cation level
is high enough for gel formation and stabilization, the excess amount of sucrose will hinder the
aggregation of gel network and thus weakens instead of strengthening the gel (Morris et al.,
2012). Similar results were also found in the studies on 1% gellan gel with various Ca2+ (5 to 40
mM) and sucrose contents (15 to 35%) (Tang et al., 2001), and on 0.5% gellan gel with Ca2+ and
20, 40, and 60% sucrose (Sworn and Kasapis, 1998).
The result of maximum shear strain at failure which reflects the deformation of gellan gel at
the point of fracture is shown in Fig. 5.10b. For samples with 0 or 10% sucrose, the shear strain
of samples showed no significant difference regardless of salt content (p>0.05). However, for
gellan gels with 30% sucrose, salt addition of 0 or 300 mM resulted in significantly lower shear
strain values (p<0.05). The gels with 50% sucrose showed the highest shear strain values among
all samples, indicating the highest gel extensities during deformation. The shear strain of the
samples with 50% sucrose decreased with salt content, which might be caused by the weakening
effect on the gel network by excess salt.
107
14
(a)
Shear stress (KPa)
12
10
0 mM
100 mM
8
200 mM
6
300 mM
4
2
0
0
10
30
50
Sucrose content (%)
0.7
(b)
0.6
Shear strain
0.5
0.4
0 mM
0.3
100 mM
200 mM
0.2
300 mM
0.1
0
0
10
30
50
Sucrose content (%)
Fig. 5.10 Shear stress and shear strain at failure of gellan gel with various sucrose and salt
contents at 22 °C
108
3.6 Combined effect of sucrose and salt content on water holding capacity
The combined effect of sucrose and salt addition on the water holding capacities of gellan
gel is shown in Fig. 5.11. The water holding capacity of gellan gel with only 6 mM Ca2+ after
centrifuge at 2000 rpm for 5 min was 0.54, which agreed with the result reported by Mao et al.
(2001). The water holding capacities of gellan gels at all tested salt content levels increased with
sucrose content, resulting in the highest water holding capacity values between 0.95 and 0.98 for
samples with 50% sucrose. For the gellan gels with 0 or 10% sucrose addition, the water holding
capacity values decreased with salt content. The results agreed with that reported by Mao et al.
(2001) that the water holding capacity of 1% gellan gel decreased with the increase of Ca2+
concentration of up to 80 mM. However, the trend changed for samples with 30% sucrose where
the water holding capacity first decreased for samples with 100 mM salt addition, and then
increased with increasing salt contents. At 50% sucrose content level, no significant difference
was found among the samples with different salt contents (p>0.05).
1
Water holding capacity
0.8
0.6
0 mM
100 mM
0.4
200 mM
300 mM
0.2
0
0
10
30
50
Sucrose content (%)
Fig. 5.11 Water holding capacity of gellan gel with various sucrose and salt addition at 22 °C
109
4. Conclusions
The dielectric constant of 1% gellan gel (with 6 mM Ca2+) increased with temperature while
slightly decreased with frequency. The loss factor increased with temperature while decreased
with frequency at frequencies lower than 500 MHz. The trend shifted to opposite at frequencies
higher than 2000 MHz. The addition of sucrose and salt both affected the dielectric properties of
gellan gel. Adding sucrose significantly decreased the dielectric constant, while adding salt was
more effective in the adjustment of dielectric loss factor values. Regression equations relating the
dielectric properties with sucrose/salt content and temperature were developed, which can be
used to calculate the specific formulation of a 1% gellan gel (including 6 mM Ca2+) to obtain
certain dielectric properties to match with the real foods to be processed in the 915 MHz MAP
system. When using gellan gels as model foods for heating pattern determination, chemical
markers will be added to the gel solution while it is cooled to a temperature close to the gelation
temperature. The cold-set gellan gels will be packed and color change will take place following
the temperature change at different locations during the MAP processes. The gels will then be
cut into thin layers and the images will be taken and analyzed using the computer vision system.
Results showed that the gel strength of gellan gel with sucrose addition of up to 30% was
relatively high, indicating the gels strong enough for post process handling. Adding 50% sucrose
resulted in much softer while highly deformable gels with the highest water holding capacity.
This study well demonstrated the possibility of using gellan gel as a model food for microwave
pasteurization processes. Moreover, due to the increasing use of gellan as a food additive
worldwide, the knowledge on the physical properties of gellan gel can be useful for the relevant
industrial applications.
110
Acknowledgements
This project was supported by the Agriculture and Food Research Initiative of the USDA
National Institute of Food and Agriculture, grant number #2011-68003-20096. The authors also
thank the Chinese Scholarship Council for providing a scholarship to Wenjia Zhang for her
Ph.D. studies at Washington State University.
111
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115
CHAPTER SIX
HEATING PATTERN DETERMINATION USING A GELLAN GEL
MODEL FOR MICROWAVE PASTEURIZATION PROCESSES
Wenjia Zhang, Juming Tang, Fang Liu, Stewart Bohnet
Department of Biological Systems Engineering, Washington State University,
Pullman, WA 99164-6120
Abstract
A gellan gel model system with incorporated chemical marker precursors, D-ribose and Llysine, was designed as a direct method to determine the heating patterns of microwave assisted
pasteurization (MAP) processes. This provided an assessment of heating uniformity during
pasteurization with an empirical correlation developed between the color changes resulting from
Maillard browning. The color change could be detected by the computer vision system for
heating pattern determination of heating intensity and treatment time and correlated with
absorbance at 268 nm. Solutions of ribose (1%) and lysine (0.5%) heated at 60 to 90 ºC for 10 to
40 min exhibited UV absorbance peaks at 268 and 315 nm. With an increase of heating
temperature, the peak at 315 nm decreased while the one at 268 nm increased. UV absorbance at
268 nm increased linearly with time at all treatment temperatures. Semiquantitative HPLC
analysis (268 nm) was used to measure peak area of the unidentified substance(s) and also 4hydroxy-5-methyl-3(2H)-furanone (M2) (285 nm), a well defined product from the Maillard
116
reaction between ribose and lysine. The unidentified substance(s) exhibited greater stability
during storage at 4 ºC than M2 for one month. Heating patterns determined using the gellan gel
model with ribose and lysine was similar to those obtained from an egg white gel model system
following the same MAP process. Gellan has an advantage in that it is translucent making color
changes easier to observe and, in some cases, easier to handle than egg white gels.
Keywords: Gellan gel, chemical marker, heating pattern, microwave assisted pasteurization.
1. Introduction
Volumetric heating provides a faster heating rate and often yields foods with higher product
quality during processing, leading to a high level of interest in microwave assisted thermal food
processing technologies over the past decade (Gupta & Wong, 2007). The microwave assisted
pasteurization (MAP) processes operating at less than l00 ºC can inactivate vegetative
pathogenic cells and viruses. To develop appropriate thermal processes that both assure food
safety and maintain good product quality, it is essential to know the heating pattern and heating
uniformity for a given product. Experimental data from heating model foods is used to validate
computer simulation results (Kim, et al., 1996; Lau et al., 2003; Wang et al., 2004; Pandit et al.,
2006). The development of a model food-chemical marker method with computer vision system
correlates brown color with concentrations of browning products (e.g. chemical markers) that
have well characterized reaction kinetics following heat treatment allowing for a visual 3dimenisional image of heating to be developed and the location of cold and hot spots determined
(Pandit et al., 2006 & 2007).
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Chemical markers are either intrinsic (e.g. vitamin C, thiamine, lysine) or extrinsic with their
precursors (e.g. lysine plus sugars such as ribose) added to the food matrix prior to heating. The
concentration of a chemical marker will change with temperature and degree of heating in a
predictable manner. A change of the amounts of a chemical marker can be used to predict the
sterility of a heating process. Mulley et al. (1975) stated the need and importance of a chemical
index for indicating the effect of heating in the food and pharmaceutical industries. Since then, a
number of efforts have been made to identify suitable chemical reactants. One of the most
significant findings of chemical markers used for thermal food processing was reported by
researchers at the US Army Natick Research Center who identified chemical markers for aseptic
processing (at sterilization temperatures of 121 °C) of particulate foods (Kim and Taub, 1993).
These markers were produced from the Maillard reaction between reducing sugars and amino
acids. The Maillard reaction has been studied extensively (Hodge, 1953). Fig. 6.1 shows the
normal pathways of the Maillard reaction. The reaction starts when the amino groups of the
amino acids or proteins react with a glycosidic hydroxyl of reducing sugar, and stops when
brown nitrogenous polymers or melanoidins are formed (Ellis, 1959). The reaction pathways and
products can be different depending on the types of amino acids and carbohydrates, and can also
be affected by factors such as temperature, pH, oxygen, metals, phosphates, sulfuric dioxide, etc.
In Kim and Taub’s study (1993), the carbohydrate profile changes in heated foods were
monitored using anion exclusion chromatographic (AEC) and photodiode array (PDA) detection
together with gas chromatographic mass spectroscopy (GC-MS). Three intrinsic chemical
markers were separated and structurally identified, namely, M-1 (2, 3-dehydro-3,5-dihydroxy-6methyl-4(H)-pyran-4-one),
M-2
(4-hydroxy-5-methyl-3(2H)-furanone),
and
M-3
(5-
hydroxymethylfurfural). M1 is produced from D-glucose or D-fructose and amines through 2,3-
118
enolization under weak acidic or neutral conditions at sterilization temperatures. M2 is formed
from D-ribose or D-ribose-phosphate under similar conditions. M3, also known as 5-hydroxymethylfurfural (5-HMF), is a major degradation product of D-fructose. The formation of these
three markers have been studied and used to determine the heating uniformity of ohmic heating
(Kim et al., 1996), aseptic processing (Ramaswamy et al., 1996), radio frequency processing
(Wang et al., 2004), and microwave-assisted thermal sterilization (Lau et al., 2003; Wang et al.,
2004; Pandit et al., 2006).
Fig. 6.1 Scheme of Maillard reaction pathway (adapted from Martins et al., 2001)
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Egg white, whole egg, and low acyl gellan gels have been developed as model foods which
would serve as carriers of the chemical markers for MAP processes (Zhang et al., 2013 & 2014),
while the identification and formation of possible chemical markers integrated into the model
foods need to be investigated. Due to the lower operation temperature of MAP process (< 100
°C) than that of MATS process (>121 °C), the three identified chemical compounds, M-1, M-2
and M-3 may not be produced in a sufficient amount to be detected using the computer vision
method, and thus the reaction kinetics for these compounds needs to be investigated. For the egg
white model food, the chemical marker M-2 was produced in a sufficient quantity during the
MAP processes and the color change of the model food matched the sensitivity of computer
vision system making it possible to determine the heating pattern for MAP processes (Zhang et
al., 2014). However, it is necessary to investigate the possible marker(s) in the gellan gel model
food system so that the correlation between color change and marker yield can be used to
indicate the heat intensity at different locations inside the food package during MAP processes.
2. Materials and methods
2.1 Model solution preparation
The gellan gels used as model foods consisted mainly of free water (~99%), it is thus
appropriate to use a water solution to study the marker formations in gellan gel. The model
solutions were prepared with analytical-reagent grade D-ribose and L-lysine (Sigma Chemical
Co., St Louis, MO) added as the Maillard browning precursors with concentrations of 1% and
0.5%, respectively. Ten mL solution was placed into a 15 mL plastic centrifuge tube. The heat
treatments were conducted in water bath set at 60, 70, 80, 85, and 90 °C for 10, 20, 30, and 40
min. For each treatment, a 0.1 mm diameter type-T (copper-constantan) thermo couple (Omega
120
Engineering, Stamford, CT) was inserted into a sample tube sealed with aluminum foil to
monitor the center temperature during heating. The come up time (CUT), which was defined as
the time for the sample temperature to reach 0.5 °C below the set-up temperature, was around 3
to 4 min. Timing was started after the CUT was reached. After each treatment, tubes were taken
out of the water bath and cooled immediately in ice water and tested immediately. Stability of
the formed browning compounds was determined by holding treated samples at 4 and 22 °C for
one month.
2.2 UV detection
UV spectra of samples prepared above were taken (190 to 900 nm)(Shimadzu UV-2550
spectrophotometer (Shimadzu Co, Kyoto, Japan). Two absorbance peaks at 268 and 315 nm
were observed (Fig. 6.2).
121
Fig. 6.2 Whole wavelength spectrum (190~900 nm) for (a) untreated ribose-lysine solution, (b)
ribose-lysine solution treated at 60 °C for 30 min, and (c) ribose-lysine solution treated at 80 °C
for 30 min
2.3 HPLC measurements for storage stability of the possible marker(s)
In order to prove the accuracy of the heating pattern results during storage after MAP
processes, the storage stability of the markers were evaluated. The ribose-lysine solutions heattreated at 70 °C for 10, 20, 30, and 40 min were used to study the effects of storage time and
temperature on the stability of the possible chemical marker(s). The heat-treated solutions were
122
collected from the centrifuge tubes, filtered through 0.45 μm PTFE syringe filters (Pall
Corporation, Port Washington, NY), and sealed in C4011-1w glass HPLC sample vials (National
Scientific Company, Rockwood, TN). Before and after a two-day storage at 4 and 22 °C, HPLC
measurements were conducted using an Agilent 1100 HPLC system (Agilent Technology, Santa
Clara, CA) equipped with a diode array detector. An injection volume of 0.25 µL automatic
injection system and flowed through a 100×7.8 mm fast acid analysis column (Bio-Rad
Laboratories, Hercules, CA) with 10 mM H2SO4 mobile phase at a rate of 1 mL/min. The
detecting wavelengths were set at 268 and 285 nm (Kim and Taub, 1993) with peak areas of M-2
(at 285 nm) and that of an unidentified substance at 268 nm reported following treatment at 60 to
90 °C for 10 to 40 min and then again after one month storage to further evaluate the stability of
the markers. All measurements were conducted in duplicate.
2.4. Color value measurements
A computer vision system (CVS) was used for the sample color analysis as previously
described (Zhang et al., 2014). Solutions were transferred to plastic petri dishes, and placed on a
white board for taking photographic images CIE Lab color model was used for color value
determination, in which L* stands for luminance or lightness component, while a*and b* are
chromatic parameters which stand for green to red and blue to yellow, respectively (Leόn et al.,
2006). On each sample image, the color parameter values at three different locations were
obtained using the histogram tool in CS6 Photoshop Software (Adobe system, Inc., San Jose,
CA) to represent the color of the entire sample. Correlations between color values, heat treatment
temperature, and the UV absorbance values were analyzed.
123
2.5. Heating pattern validation using microwave process
Gellan gel model containing chemical marker precursors ribose and lysine were used to
validate heating patterns for MAP process predicted by computer modeling (Zhang et al., 2014).
Low acyl gellan gum (KELCOGEL F) provided by CP Kelco Inc. (Atlanta, GA) was added to
DDI water at 22 °C to obtain a final dispersion of 1% (w/v) polymer concentration. The
dispersion was stirred for 1h, and left at 22 °C overnight for better rehydration. It was then gently
heated to 90 °C when the dispersion became a clear solution. CaCl2·2H2O (J.T. Baker, Avantor
Performance Materials, Inc., Phillipsburg, NJ) was added to the hot solution at 90 °C to obtain a
Ca2+ concentration of 6 mM to ensure the formation of a strong gellan gel network as explained
in Chapter Five. The solution was allowed to cool down to around 60 °C at 22 °C and added with
predetermined amounts of D-ribose (1%) and L-lysine (0.5%). The solution was further stirred
for 1 min until the ribose and lysine were dissolved and 8 oz hot solution was quickly poured
into plastic pouches (16 cm×12 cm) and vacuum-sealed. The gel pouches were placed on flat
surfaces at 22 °C for cooling and to allow the gel to set, at which point the gellan gel was then
subjected to MAP treatments.
The pilot plant scale single mode 915 MHz Microwave Assisted Pasteurization (MAP)
system developed at Washington State University was used to heat the gellan gel at
pasteurization temperatures. The MAP system consisted of pre-heating, microwave heating,
holding, and cooling sections. The system was warmed up for 10 min before operation , and then
the temperature of circulating water inside the system was set to the desired process temperature
of 90 °C, which was monitored at the inlet of the microwave heating cavity. The microwave
power of the microwave heating cavities was set as 14 kW. The gellan gel pouches were placed
on a tray/pouch carrier (4 pouches in each carrier) moved by a conveyor belt through pre-heating
124
section for pre-conditioning with warm water (60 °C) for 20 min. The samples were then moved
through the microwave heating cavities where the sample temperature was rapidly increased by
both microwave power and circulating water. The gel models then moved to a holding section
with 90 °C water. The total residence time of the samples in microwave heating and holding
section was around 5 min. A cooling section was followed where the samples were cooled down
using tap water (25 °C) and unloaded from the system. After MAP treatment, the gels were taken
out the pouches and a layer of 5 mm thickness below the horizontal center line was obtained to
minimize the accumulated color from the bottom of the transparent gel. The image of the gel
sample layer was taken and analyzed using the computer vision method (Zhang et al., 2014). The
locations in blue and red color shown on the heating pattern results were assumed as the cold and
hot spots, respectively. Mobile metallic ELLAB sensors were used to monitor the temperatures
at the predetermined cold/hot spots. The ELLAB sensors installed inside a protective metal tube
with 2 mm diameter and 50 mm length were inserted into the horizontal middle layer of the
gellan gel model at the cold and hot spots predetermined for the 90 °C process.
The heating pattern results were compared with those obtained by using the egg white gel
model system with ribose and lysine treated by the same MAP processing conditions. The
preparation of the egg white model and preparation of pouches were as previously described by
Zhang et al. (2014).
3. Results and discussion
3.1 UV absorbance
125
Fig. 6.3 shows the UV spectra for ribose-lysine solutions treated at 60, 80, 100, and 120 °C
for 30 min. Two peaks at 268 and 315 nm were found. With the increasing of heat treatment
temperature, the absorbance at 315 nm decreased while the one at 268 nm increased, indicating
the possible transformation of the unidentified substance(s) with maximum UV absorbance at
315 nm (S315) to other unknown substance(s) which had the maximum absorbance at 268 nm
(S268) upon heating, or to the next stage of Maillard reaction to form final browning products.
Fig. 6.3 Typical wavelength ultraviolet scan (190 to 900 nm) for ribose- lysine model solution
after heat treatment at 60 (orange line), 80 (yellow line), 100 (blue line), and 120 °C (pink line)
for 30 min
126
The absorbance of UV at 268 and 315 nm for samples treated at different time-temperature
combinations is shown in Fig. 6.4a and 6.4b, respectively. The absorbance at both wavelengths
increased linearly with treatment time at all temperatures. The coefficients of determination of
the linear relationship (R2) are also shown in the figure varying from 0.96 to 0.99. van Boekel
(2001) reported that the formation of the relatively more stable intermediate Maillard reaction
products (MRP) followed zero-order kinetics. It was also reported in Zhang et al. (2014) for the
formation of M2 in egg white gel at 75–95 °C. This could be due to the relatively low amounts
of these MRPs compared to the concentrations of the reaction precursors. The absorbance at 315
nm showed higher R2 values at lower temperatures of 60 and 70 °C, while higher R2 values at
268 nm were found at temperatures higher than 80 °C. Combining with the decease of the peak
at 315 nm, 268 nm was used as a detecting wavelength for further HPLC analysis.
127
Absorbance at 268 nm
60
(a)
y = 1.35x + 3.07
R² = 0.992
60 ºC
50
70 ºC
40
80 ºC
y = 1.01x + 0.33
R² = 0.995
85 ºC
30
y = 0.54x + 1.04
R² = 0.976
90 ºC
20
y = 0.21x - 0.02
R² = 0.979
10
y = 0.05x + 0.05
R² = 0.986
0
0
10
20
30
40
50
Heating time (min)
60
(b)
Absorbance at 315 nm
50
y = 1.20 + 4.66
R² = 0.962
60 ºC
70 ºC
y = 0.95x + 1.05
R² = 0.976
80 ºC
40
85 ºC
30
90 ºC
y = 0.49x + 1.45
R² = 0.966
20
y = 0.22x + 0.12
R² = 0.995
10
y = 0.06x + 0.06
R² = 0.989
0
0
10
20
30
40
50
Heating time (min)
Fig. 6.4 UV absorbance of ribose-lysine solutions after various heat treatments at (a) 268 nm and
(b) 315 nm (results shown were mean value of three replicates)
128
3.2 HPLC analysis of S268 and M-2
Based on the UV absorbance results shown above, the HPLC measurements of the heattreated model solution samples were set at 268 nm for the unidentified substance(s) S268. The
peak area at 285 nm was also recorded to detect possible M-2 formation in these solutions. Fig.
6.5 shows the HPLC chromatogram of the model solution after treatment at 70 °C for 20 min at
268 and 315 nm, respectively. The M-2 peak at both wavelengths appeared at around 6.5 min. At
268 nm, the highest peak was found at elution time of around 2.4 min in a well-defined shape.
Lysine elutes at approximately 1.5 min and ribose at around 3.1 min.
Fig. 6.5 HPLC spectrogram at 268 nm and 285 nm of ribose-lysine solutions after heat treatment
at 70 °C for 20 min
129
3.3 Storage stability of S268 and M2
Fig. 6.6 shows the change of peak area of S268 and M-2 in the model solutions treated at 70
°C for 30 min after storage at 4 and 22 °C for two days. In Fig. 6.6a, the peak area of S268
increased with heating time. Storage at 4 °C for two days did not significantly affect the peak
area, while after storage at 22 °C, the peak area increased significantly for samples treated after
all treatment times. The results indicated the further formation of the S268 compound at 22 °C .
Fig. 6.6b shows the change of peak area of chemical maker M-2 detected at 285 nm after storage.
The storage at 4 and 22 °C for two days both significantly decreased the peak area of M-2,
indicating significant degradation or transformation of M-2 into other compounds. The results
illustrated the better storage stability of S268 than M-2 during storage.
130
1200
(a)
Peak area (mAU*s)
1000
fresh
4 °C, 2 days
800
22 °C, 2 days
600
400
200
0
10
20
30
40
Heating time (min)
800
Peak area (mAU*s)
700
600
500
(b)
fresh
4 °C, 2 days
22 °C, 2 days
400
300
200
100
0
10
20
30
40
Heating time (min)
Fig. 6.6 Effect of storage at 4 and 22 °C for two days on (a) the peak area of unidentified
substance(s) S268 detected at 268 nm and (b) the peak area of chemical marker M-2 detected at
285 nm
The stability of the compound at S268 and M-2 in the heated ribose-lysine solutions were
analyzed after storage at 4 °C for one month (Fig. 6.7). In Fig. 6.7a, the peak area of the
unknown substance(s) S268 detected at 268 nm exhibited a linear relationship with treatment time
131
at all temperatures. M-2 levels were low and not reliable indicating that S268 may be a more
suitable chemical marker than M-2 in gellan gel for MAP applications.
6000
5000
Peak area (mAU*s)
y = 140.2x + 155.5
R² = 0.992
(a)
60 ºC
y = 100.3x - 165.5
R² = 0.985
70 ºC
4000
80 ºC
3000
85 ºC
y = 46.95x + 64.83
R² = 0.976
90 ºC
2000
y = 20.86x - 55.92
R² = 0.973
1000
y = 3.05x - 5.01
R² = 0.927
0
0
10
20
30
40
50
Heating time (min)
300
(b)
Peak area (mAU*s)
250
60 ºC
70 ºC
200
80 ºC
85 ºC
150
90 ºC
100
50
0
0
10
20
30
40
50
Heating time (min)
Fig. 6.7 Peak area of unidentified substance(s) S268 detected at 268 nm and chemical marker M-2
detected at 285 nm after heat treatment in ribose-lysine solutions? at different time temperature
combinations after storage at 4 °C for one month
132
3.4 Correlation between color values, UV absorbance, and heat treatment temperature
Fig. 6.8 shows photographic images of the ribose-lysine solutions after treatment at 60 to 90
°C for up to 40 min. Observable color changes occurred during the heat treatments, clearly
differentiating heat treatment times and temperatures. The color parameters L*, a*, and b* values
were obtained using Microsoft Photoshop software and the significant correlations (p<0.05)
between color parameters, temperature, and the UV absorbance at 268 nm were analyzed and are
shown in Table 1. Significant correlations were found between temperature with lightness,
redness of the ribose-lysine solutions, and with the UV absorbance of the sample at 268 nm.
These results indicate the possibility of using S268 as a marker to predict the temperature of the
reaction and using color values L* and a* as an indication for heating pattern determination in
the gellan gel during pasteurization processing.
(a)
(b)
(c)
(d)
(e)
Fig. 6.8 Images of ribose-lysine solutions in centrifuge tubes after heat treatments at (a) 60, (b)
70, (c) 80, (d) 85, and (e) 90 °C for 10, 20, 30 and 40 min.
133
Table 6. 1 Correlation between heat treatment temperature and color and UV absorbance of
ribose-lysine solutions
Parameter
Coefficient of determination (R2)
Significance (p)
L* (lightness)
-0.983
0.003
a* (redness)
0.960
0.010
UV absorbance at 268 nm
-0.992
0.010
3.5 Validation of the model system using MAP process
Earlier research has shown that egg white gel with the added chemical marker precursors
ribose and lysine can be used to predict heating patterns for MAP processes in conjunction with a
computer vision system. The same technique was applied for gellan gel. Fig. 6.9 shows the
heating pattern results of four gellan gel and four egg white gels in two separate tray carriers
after two replicate runs in the MAP process at 90 °C. The heating patterns obtained from the two
different systems were similar, with the cold spots generally at the center with the hot spots
around the edge of the model foods.
134
Fig. 6.9 Heating pattern results of obtained by using gellan gel and egg white gel systems in
pouches in two replicated runs of MAP treatments at 90 °C
To further validate the cold and hot spots obtained from the computer vision analysis, Ellab
sensor were inserted to the gellan gel to measure the time-temperature profile at cold spot in
pouch at location 1 in carrier 1 (Fig. 6.10 up) and at hot spot in pouch location 1 in carrier 2 (Fig.
6.10 down). The time-temperature history at those two locations were recorded and shown in
Fig. 6.11. The temperature at the cold spot during microwave heating was much lower than at the
hot spot location.
135
Fig. 6.10 Heating pattern results of gellan gel in pouches in two replicated runs of MAP
treatments at 90 °C with Ellab senor at predicted cold spot of Pouch 1 in Carrier 1 and at the
predicted hot spot of Pouch 1 in Carrier 2
136
Fig. 6.11 Time-temperature profile at cold spot of Pouch 1 in Carrier 1 (blue line) and hot spot of
Pouch 1 in Carrier 2 (red line) in MAP.
4. Conclusion
This study investigated the possibility of using gellan gel incorporating D-ribose and Llysine to show the color change that occurs during heating. An unidentified browning compound
with a UV absorbance peak at 268 nm (S268) increased linearly with an increase of heating time
at all heat treatment temperatures. S268 also showed better stability than marker M-2 which could
be formed, but at lower levels. Visual color, instrumental color and UV absorbance, indicative of
137
browning compound concentration were well correlated and the color intensity or concentration
increased with temperature and length of heating. The heating patterns of gellan gel were similar
to those in egg white gels following MAP processing. The temperature measurements at cold and
hot spots could be accurate predicted. Compared to egg white, gellan has the advantage of a
wider application temperature range of higher than 65 °C and easier for preparation and
handling.
Acknowledgements
This project was supported by the Agriculture and Food Research Initiative of the USDA
National Institute of Food and Agriculture, grant number #2011-68003-20096. The authors also
thank the Chinese Scholarship Council for providing a scholarship to Wenjia Zhang for her
Ph.D. studies at Washington State University.
138
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140
CHAPTER SEVEN
CONCLUSIONS, CONTRIBUTION TO KNOWLEDGE, AND
RECOMMENDATIONS
1. Major Conclusions
Two time-temperature indicators, which consisted of model foods and chemical markers
systems, were developed as direct heating pattern indicators for microwave assisted
pasteurization processes. The model food systems included egg white and whole egg gels, and
the low acyl gellan gel models. The relevant physical properties of the model foods validated
their suitability as model foods for the MAP processes. Possible chemical markers as the
indicators of the heat intensity in each model food system were investigated. The heating
patterns were obtained by analyzing the images of the processed model food-chemical marker
systems using compute vision system. The major findings of this research are summarized
below:
a. Dielectric properties of the egg white and whole egg gel models could be adjusted by
changing their solid and salt contents. Various foods could be modeled by the gels prepared
according to the regression equations. Gelation of the egg whites and whole eggs with 25% solid
concentration occurred at 70 and 80 °C, indicating their applications for MAP processes at
temperatures higher than 70 and 80 °C, respectively. The gel strength and water holding
capacities of both gels are adequate for post-process evaluation.
141
b. The formation of chemical marker M2 due to the addition of 1% D-ribose and 0.5% Llysine in egg white gel models at pasteurization temperatures of 75 to 100 °C increased with both
time and temperature. Since the degradation of M2 during storage, it is recommended to analyze
the gel model samples shortly after process or storage at 4 °C and for accurate heating pattern
results. The heating patterns of the MAP processes at higher than 75 °C could be clearly shown
using the computer vision method.
c. 1% gellan gel (including 6 mM Ca2+) can be formulated with different sucrose and salt
additions following the regression equations developed to match the dielectric properties with the
real foods to be processed. The gellan gels with sucrose addition of up to 30% were strong
enough for post process handling. 50% sucrose addition resulted in much softer while highly
deformable gels with the highest water holding capacity.
d. Unidentified substance(s) obtained from heating 1% D-ribose and 0.5% L-lysine in
solution showed maximum UV absorbance peak at 268 nm. The S268 showed better stability than
chemical marker M2 after storage at 4 °C for two days. The color values L* and a* of the heattreated solutions showed significant correlation with temperature and UV absorbance at 268 nm,
indicating the S268 as a proper marker for gellan gel model food during MAP processes. The
heating patterns of the gellan gel model were proven to be accurate by both temperature
measurement and comparison with the results obtained using egg white system.
2. Contribution to knowledge
Two model food-chemical marker systems were developed in this research to solve the
problem of experimental determination of heating patterns in prepackaged food products during
microwave assisted pasteurization processes. The egg white model food system is more suitable
142
for MAP processes at temperatures higher than 75 °C, while the gellan gel model food system
can be used at a wider temperature range of higher than 65 °C. The two systems were suitable for
mapping the heating patterns with accurate cold and hot spots. The temperatures at cold and hot
spots could thus be monitored for proper process development and for improving the uniformity
of the microwave assisted pasteurization system. This research provides an effective
experimental tool in future research and development of microwave assisted pasteurization
processes for industrial food safety applications. Both model systems can be used for product
and package development as well as process and system designs. The experimental data included
in this study provides support for use and further refinements of the marker systems.
3. Recommendations
a. A three-dimensional image analyzing method should be found or developed so that the
direct three-dimensional heating pattern could be obtained due to the transparency of the gellan
gel.
b. Validation of the model systems should be done using certain real foods. The temperature
at the predetermined cold and hot spots in the model food and real food systems should be
compared.
c. Possibility of forming the gel models in certain shapes (such as in slices, cubes, or round
shapes) instead of in a whole piece to model food items such as vegetable or meat slices or dices
should be studied.
d. The unidentified substance(s) which could be used as an indicator for heat intensity in the
gellan gel models during heating should be further analyzed for its possible chemical structure so
that quantification could be conducted using a standard.
143
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