Kinetics of Volatile Generation During Coffee Roasting and Analysis Using Selected Ion Flow Tube-Mass Spectrometry

Kinetics of volatile generation during coffee roasting and analysis using Selected Ion Flow Tube-Mass Spectrometry

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Sangeetha Krishnaswamy

Graduate Program in Food Science and Technology

The Ohio State University

2017

Master's Examination Committee:

Dennis R. Heldman, Advisor

Sudhir Sastry

Christopher Simons

Copyrighted by

Sangeetha Krishnaswamy

2017

Abstract

Roasting is a critical step comprising of a series of complex reactions that are responsible for flavor generation in coffee. This study presents a real-time analytical technique that predicts the mechanism of volatile generations during different roasting conditions which could be ultimately used for online process control to deliver a more consistent target roast profile. The objectives of this research were to 1) To monitor the volatile compound generation during coffee roasting in real-time using online SIFT-MS 2) To investigate the influence of the time-temperature process during coffee roasting on the kinetics of volatiles generated and develop predictive models to determine kinetic parameters of volatile compounds and 3) predict temperature distribution histories within the coffee bean at different roasting conditions.

Colombian Arabica coffee beans were roasted in a horizontal drum roaster at 210, 220 and 230 °C for 10, 15 and 20 minutes respectively. The concentrations of 7 volatile organic compounds (VOC’s), with impact on coffee flavor, were measured in the gas stream at the exit from the roaster using online Selected Ion Flow Mass Spectrometry

(SIFT-MS) and were compared to the amounts retained in the final coffee extract.

Modified Gompertz and Logistic models were used to describe the rate of volatile generation and estimate the kinetic parameters for the Volatile Organic Compounds

(VOC’s) during different roasting conditions. The activation energy coefficients were calculated using the Arrhenius relationship. A transient heat conduction model for ii unsteady state heat transfer was used to determine the temperature distribution within the coffee bean.

A synergy existed between the VOC release pattern in the roaster gas and the VOC formation/retention trend in the coffee extract. Excessive roasting (230 °C beyond 15 minutes), led to lower VOC concentrations in the roaster gas and the coffee extract.

The modified Logistic models provided good statistical fit to the experimental data and precise estimation of kinetic parameters based on low NRMSE and SEE%. The changes in the temperature influenced the kinetics of volatile generation during the roasting process.

The rate constant and peak concentration increased significantly with increase in temperature for all VOC’s. The range of activation energy coefficient for volatile compounds indicates a very high temperature sensitivity for the volatile compounds.

The mass average temperature was predicted considering the mass contribution of different regions within the coffee bean using transient heat conduction model. Model validation was performed by combining the reference kinetics parameters of volatile compounds with the predicted mass average temperature. A correlation between the rate of volatile generation and the mass average temperature within the coffee bean was evident. The results showed that the predicted VOC concentration was in good agreement with the experimental values. The simulation can be useful for predicting VOC release for different roaster types and optimizing the roasting conditions.

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Acknowledgments

I would like to sincerely express my gratitude to my advisor, Dr. Dennis R. Heldman and to my committee members Dr. Sudhir Sastry and Dr. Christopher Simons. I would like to thank David Phinney for helping me with the experimental setup. Finally, I would like to thank my parents and my husband for their support and encouragement throughout my education.

Vita

2006-2010 ...... B Tech. Chemical Engineering, Anna

University

2015-present ...... M.S. Department of Food Science and

Technology, The Ohio State University

Fields of Study

Major Field: Food Science and Technology

Table of Contents

Abstract ...... ii

Acknowledgments...... iv

Vita ...... v

List of Tables ...... xi

List of Figures ...... xiii

Chapter 1: Introduction ...... 1

References ...... 5

Chapter 2: Literature Review ...... 6

2.1 Green bean constituents ...... 6

2.2 Coffee Roasting ...... 7

2.2.1 Background ...... 7

2.2.2 Roasting Process ...... 7

2.2.3 Roasting Equipment ...... 8

2.2.4 Heat and Mass transfer inside the coffee bean during roasting ...... 8

2.2.5 Physical changes during roasting ...... 10

2.2.6 Volatiles of Roasted Coffee ...... 11

2.2.7 Mechanism of Volatile generation ...... 16

2.2.8 Changes in coffee aroma profile during storage ...... 17

2.3 Analytical measuring techniques ...... 19

2.3.1 Gas Chromatography ...... 19

2.3.2 Proton Transfer Reaction- Mass Spectrometry (PTR-MS) ...... 19

2.3.3 Selective Ion Flow Tube-Mass Spectrometry ...... 20

2.4 Non-linear curve fitting models ...... 25

References ...... 27

Chapter 3: Real-time analysis of volatiles during coffee roasting using Selected Ion Flow

Tube-Mass Spectrometry ...... 33

Abstract ...... 33

3.1 Introduction ...... 35

3.2 Materials and Methods ...... 40

3.2.1 Roasting Procedure ...... 40

3.2.2 SIFT-MS method development ...... 40

3.2.3 SIFT-MS Roaster Gas Analysis ...... 41

3.2.4 SIFT-MS coffee brew analysis ...... 43

3.2.5 Gaussian function ...... 44

vii

3.2.6 Statistical analysis...... 44

3.3 Results and Discussion ...... 45

3.3.1 VOC release from the roaster ...... 45

3.3.2 VOC formation in coffee extract ...... 47

3.3.3 Comparison of VOC generation during roasting and from coffee extract ...... 49

3.3.4 Gaussian function ...... 50

3.3.5 Comparison of VOC parameters ...... 53

3.4 Conclusion ...... 55

References ...... 56

Chapter 4: Kinetic parameters for volatiles generated during coffee roasting ...... 58

Abstract ...... 58

4.1 Introduction ...... 59

4.2 Materials and Methods ...... 63

4.2.1 Roasting Procedure ...... 63

4.2.2 SIFT-MS Analysis ...... 64

4.2.3 Kinetic Models ...... 64

4.2.4 Statistical Analysis ...... 65

4.3 Result and Discussion ...... 66

4.3.1 Comparison between modified Gompertz and Logistic model ...... 68

viii

4.3.2 Influence of temperature on kinetics parameters ...... 69

4.3.3 Determination of Activation Energy Coefficient ...... 71

4.3.4 Comparison of dynamics of Volatile compounds ...... 73

4.4 Conclusion ...... 74

References ...... 76

Chapter 5: Effect of temperature distribution within the coffee bean on the rate of VOC generation during coffee roasting ...... 78

Abstract ...... 78

5.1 Introduction ...... 79

5.2 Materials and Methods ...... 82

5.2.1 Moisture Analysis ...... 83

5.2.2 Thermo-Physical Properties of Coffee Bean ...... 83

5.2.3 Governing equations ...... 84

5.3 Result and Discussion ...... 89

5.3.1 Moisture loss during roasting ...... 89

5.3.2 Thermo-physical Properties ...... 91

5.3.3 Bean Center Temperature ...... 93

5.3.4 Temperature distributions within coffee bean and mass average temperature . 94

5.3.5 Integration of VOC kinetics with temperature distribution ...... 96

5.4 Conclusion ...... 98

References ...... 101

Chapter 6: Conclusions and Future Directions ...... 103

Bibliography ...... 106

Appendix A: VOC Pattern Description ...... 118

Appendix B: VOC release in the roaster gas stream...... 119

Appendix C: VOC release in the coffee extract analysis ...... 121

Appendix D: Gaussian function shape parameter ...... 122

Appendix E: VOC release kinetics ...... 124

Appendix F: Kinetic Parameters of VOC’s ...... 127

Appendix G: Arrhenius Plot of VOC’s (Cumulative Concentration Method) ...... 130

Appendix H: MATLAB code to determine the cumulative area of VOC’s ...... 131

Appendix I: Activation Energy Coefficient (Cumulative Area Method) ...... 132

Appendix J: Experimental Vs Predicted Bean Center Temperature (Convection Oven) 133

Appendix K: Coffee Bean Center Temperature Distribution ...... 134

Appendix L: Coffee Bean Temperature Distribution at different locations ...... 135

Appendix M: Equation to determine thermo-physical properties of coffee bean ...... 136

Appendix N: Thermal Properties Chart ...... 138

List of Tables

Table 3.1. Details on the volatile organic compounds analyzed by SIFT-MS ...... 41

Table 3.2. Statistical Comparison of peak concentration (C∞) of VOC in roaster gas and extract estimated using Gaussian function. (Value ± Standard Error) ...... 52

Table 4.1. Result of kinetic study- modified Logistic and Gompertz model ...... 69

Table 4.2. Activation coefficient of VOC’ using modified Gompertz and Logistic models

...... 73

Table 5.1. Composition of Coffee bean ...... 83

Table A.1. VOC’s release pattern in the roaster gas ...... 118

Table D.1. The Shape factor (w) VOC roaster gas and extract estimated using Gaussian function...... 122

Table D. 2. Statistical Comparison of VOC mid time (tm) in roaster gas and extract estimated using Gaussian function...... 123

Table F.1. Peak Cumulative concentration of VOC estimated from modified Gompertz and modified Logistic model. (Value ± SEE %) ...... 127

Table F.2. Rate Constant of VOC estimated from modified Gompertz and modified

Logistic model. (Value ± SEE %) ...... 128

Table F.3. Lag Time of VOC estimated from modified Gompertz and modified Logistic model. (Value ± SEE %) ...... 129

Table I.1. Activation coefficient of VOC’ using modified Gompertz and Logistic models based on Cumulative Area Method ...... 132

Table M.1. Equations to calculate coffee component density ...... 136

Table M.2. Equations to calculate coffee component specific heat ...... 136

Table M.3. Equations to calculate coffee component thermal conductivity ...... 137

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List of Figures

Figure 2.1. Scheme showing the main classes of volatile compounds formed from non- volatile precursors in the green beans during roasting (Yeretzian et al. 2002) ...... 18

Figure 3.1. a) Experimental setup showing online analysis of VOC’s during the roasting process using SIFT-MS. b) Schematic representation of experimental setup ...... 42

Figure 3.2. Coffee extract head space VOC analysis using Selective Ion Flow Tube Mass

Spectrometry ...... 43

Figure 4.1. Cumulative volatile generation- experimental and modified Logistic model 67

Figure 4.2. Cumulative volatile generation- experimental and modified Gompertz model

...... 67

Figure 4.3. Influence of Temperature on the peak cumulative concentration of Furfuryl mercaptan estimated from modified Logistic model along with statistical comparison .. 70

Figure 4.4. Arrhenius plot of Furfuryl using Modified Gompertz and Logistic model .... 72

Figure 5.1. Experimental moisture drying curve during roasting at 210, 220, 230 °C ..... 89

Figure 5. 2. Mass average temperature and release of Furfuryl mercaptan during roasting at 210 °C...... 90 xiii

Figure 5.3. Moisture curve and evolution of Furfuryl mercaptan during roasting at 210 °C

...... 91

Figure 5.4. Density of the coffee bean during roasting at 220 °C ...... 92

Figure 5.5. Specific heat capacity of the coffee bean during roasting at 220 °C ...... 92

Figure 5.6. Thermal Conductivity of the coffee bean during roasting at 220 °C ...... 93

Figure 5.7. Evolution of bean’s center temperature during roasting at 220 °C ...... 94

Figure 5.8. Evolution of bean’s temperature at different locations during roasting at 220

°C ...... 95

Figure 5.9. Mass average temperature of the coffee bean during roasting at 210, 220 and

230 °C ...... 95

Figure 5.10. Experimental Vs Predicted cumulative concentration of Furfuryl mercaptan from integrated model during roasting at 220 °C ...... 97

Figure 5.11. Experimental Vs Predicted cumulative concentration of Dimethyl Pyrazine from integrated model during roasting at 230 °C ...... 97

Figure B.1. Release of VOC from the roaster gas at three roasting temperatures (210 °C,

220 °C & 230 °C) fitted with Gaussian function ...... 119

Figure B.2. Release of VOC from the roaster gas at three roasting temperatures (210 °C,

220 °C & 230 °C) fitted with Gaussian function...... 120

Figure C.1. Formation of VOC in the coffee extract at the three roasting temperatures

(210 °C, 220 °C & 230 °C) fitted with Gaussian function...... 121

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Figure E.1. Figure Cumulative volatile generation of Furaneol +Sotolon and

Homofuraneol- experimental and modified Gompertz and Logistic model ...... 124

Figure E.2. Figure Cumulative volatile generation of Vanillin and Vinyl Guaicol- experimental and modified Gompertz and Logistic model ...... 125

Figure E.3. Figure Cumulative volatile generation of Dimethyl Pyrazine and Acetic acid experimental and modified Gompertz and Logistic model ...... 126

Figure G.1. Arrhenius Plot of VOC’s based on cumulative concentration method ...... 130

Figure J.1. Experimental Vs Predicted (transient conduction equation) bean center temperature at process temperature 210℃ in a convection oven ...... 133

Figure K.1. Bean Center vs Air Temperature at 210°C ...... 134

Figure K.2 Bean Center vs Air Temperature at 230 °C ...... 134

Figure L.1. Temperature distribution at different bean location at 210 °C ...... 135

Figure L.2. Temperature distribution at different bean location at 230 °C ...... 135

Figure N.1. Heating rate constant fh Vs Biot number (Pflug et al. 1965) ...... 138

Figure N.2. Lag constant jc Vs Biot number (Pflug et al. 1965) ...... 138

Chapter 1: Introduction

Coffee is one of the most valuable exported commodity. Nevertheless, the effort to improve and refine the flavor of coffee continues. Coffee production is the industrial process of converting the raw fruit of the coffee plant into the finished coffee. The flavor of coffee is affected by several factors like variety of plant, weather, soil chemistry and the way the cherries are picked and further processed. Yet, the green bean is nearly flavorless. Roasting is a very critical step responsible for aroma and flavor generation in coffee where various physical and chemical changes takes place by which volatiles and other flavor components are generated. Roasting is a process in which beans are subjected to heat treatment at high temperatures ranging from 200- 260 °C for a certain period. Several physical and chemical changes occur during roasting leading to the development of characteristic flavor and aroma in coffee. Green coffee bean has a moisture content between 8-12% on dry basis but after the roasting process, the moisture content dramatically drops to about 2% percent depending upon the roast degree (Eggers and Pietsch 2001).

More than 800 volatile compounds have been identified in roasted coffee, of which around 30 compounds are responsible for the main impression of coffee aroma

(Baggenstoss et al. 2008). The chemistry of flavor formation during coffee roasting is highly complex and not completely understood. Series of chemical reaction responsible 1 for flavor generation during the roasting process includes Maillard reaction between the reducing sugar and amino acid which generates volatiles like Furfuryl mercapatan and pyrazines contributing to the flavor and aroma of coffee, caramelization of sugar involves dehydration of sugar resulting in formation of double bonds leading to complex mixture of unsaturated polymeric compounds (Bemiller and Huber 2008),degradation of protein, trigonelline and chlorigenic acid etc. Maillard reactions are highly temperature dependent with activation energies ranging from 10 to 160 kJ/mol (Sikorski et al. 2008). Different time-temperature conditions during roasting affect dehydration and the chemical reaction conditions in the bean which control volatiles formation, browning and flavor development.

Currently, the roasters often use sensory and physical indicators to control the roasting process. Color change and weight loss are used as a measure of the degree of roast

(Sivetz 1991). But both color and weight loss measurements are indirect indicators of the flavor profile as both these parameters depend significantly on the green beans quality.

Variation in the green bean quality (color and weight) will lead to variability in the final roast color and weight which may not represent the true measure of degree of roast. Also, these parameters hardly consider that the flavor generation depends on the complete time- temperature conditions of roasting. More importantly, they are determined only after the completion of the roasting process and do not allow control during the actual process to achieve consistent result.

The effect of time-temperature combinations of roasting processes on the kinetics of aroma formation in coffee was investigated (Baggenstoss et al 2008). They monitored the

2 volatile generation of 16 aroma compounds at high temperature-short time and low temperature-long time conditions. They found that compared to low temperature-long time roasting, high temperature-short time roasting resulted in considerable differences in the physical properties and kinetics of aroma formation. Schenker et al. (2002) monitored the flavor formation of coffee at different time-temperature combination by sampling at regular intervals during the roasting process and analyzed the coffee volatile compounds with gas chromatography-mass spectrometry. The result from their studies shows that the trend in volatile generation is a clear function of time-temperature conditions in the roaster. However, in these studies, the volatiles were not monitored online in real time and therefore do not provide understanding on the continuous formation of volatiles during the roasting process.

To deliver consistent flavor profile, it is important to have precise control of the roasting process. To do so, the impact of different roasting conditions (different time-temperature combination) and the heat transfer within the coffee bean, on the rate of volatile generation needs to be understood. Since series of complex reactions takes place during roasting, it is important to monitor the volatiles in real time as they are generated.

Objective of the Study

The overall objective of this investigation was to use a real-time analysis tool to determine the rate of volatiles generation during different roasting conditions and predict the temperature distribution within the coffee bean and its effect on the volatile generation during different roasting conditions.

Specific Objectives

1) To monitor the volatile compound generation in real-time using online SIFT-MS

during coffee roasting to get insights on the dynamics of volatile organic compound

(VOCs) release in the roaster gas and compare it with the formation dynamics in the

coffee brew.

2) To investigate the influence of the time-temperature process during coffee roasting on

the kinetics of volatiles generated in real time and develop predictive models to

determine kinetic parameters of volatile compounds generated during roasting at

different time-temperature combinations.

3) To predict temperature distribution histories within the coffee bean at different

roasting conditions.

4) To establish correlation between the kinetic parameters and the temperature

distribution within the coffee bean to predict the concentration of volatile release.

References

Baggenstoss J, Poisson L, Kaegi R, Perren R, Escher F. (2008). Coffee roasting and aroma formation: application of different time–temperature conditions. J Agric Food Chem 56(14):5836– 5846.

Bemiller, J. N., & Huber, K. C. (2008). Chapter 3: Carbohydrates. In S. Damodaran, K. L. Parkin, & O. R. Fennema (Eds.), Food chemistry (Vol. Fourth, pp. 101–102). Boca Raton: CRC Press.

Eggers R, Pietsch A. (2001). Technology I: roasting. In: Clarke RJ, Vitzhum OG, editors. Coffee: recent developments. 1st ed. Malden, MA: Blackwell Science Ltd. p 90-107.

Schenker S, Heinemann C, Huber M, Pompizzi R, Perren R, Escher F. (2002). Impact of roasting conditions on the formation of aroma compounds in coffee beans. J. Food Sci. 67(1):60-6

Sikorski, Z. E., Pokorny, J., & Damodaran, S. (2008). Chaper 14. Physical and chemical interactions of components in food systems. In S. Damodaran, K. L. Parkin, & O. R. Fennema (Eds.), Food Chemistry (4th ed., p. p. 866). Boca Raton: CRC Press.

Sivetz M. (1991). Growth in use of automated fluid bed roasting of coffee beans. Proceedings of the 14th ASIC Colloquium; San Francisco.

Chapter 2: Literature Review

2.1 Green bean constituents

Coffee belongs from family Rubiaceae and comprises of more than 90 species (Davis

2001). However, the two most important varieties of commercial coffee are Coffea arabica and Coffea canephora usually know as Arabica and robusta respectively. Arabica is considered better quality due to its finer flavor (Valdenebro et al. 1999). Arabica, typically comprise up to 50% more sucrose than Robusta coffees (Wasserman &

Bradbury 2000). The chemical composition of green Arabica and Robusta coffee bean consist of water, carbohydrates, fiber, proteins, free amino acids, mineral, lipids, organic acid, chlorogenic acid, caffeine and trigonelline

Arabica coffee plant is about 4-6 meters tall while Robusta coffee plant is about 8-12 meters tall. The ripe cherries are harvested either by hand or harvested using machine by stripping off the whole branches. Hand picking allows selective picking of ripe beans for superior quality, although it is time consuming process. Post harvesting, the coffee fruits are separated from the pulp, by either dry or wet processing (Clarke & Macrae 1987).

The dry process, in which the coffee beans are dried in the sun followed by mechanical separation of hull. While in wet process, the fruit covering the seed is removed before they are dried. In the wet processing, the beans are covered with water and allowed to

6 ferment naturally. Wet processing allows the quality of green bean to be well persevered and produces homogeneous beans (Bee and others 2005).

2.2 Coffee Roasting

2.2.1 Background

Roasting is a process in which beans are subjected to heat treatment at high temperatures up to 260°C. Several physical and chemical changes occur during roasting leading to the development of characteristic flavor and aroma in coffee. More than 800 volatile compounds have been identified in roasted coffee, whereof around 30 compounds are responsible for the main impression of coffee aroma (Baggenstoss et al. 2008). Many chemical reactions like Maillard and Strecker reactions, degradation of proteins, polysaccharides, trigonelline and chlorogenic acids occurs during roasting that makes the roasting chemistry very complex.

2.2.2 Roasting Process

During the roasting process, dry heat is applied to the green beans at high temperature ranging from 200°-260°C for a certain period. Typically, the roasting process can be characterized in by two important phases. The first endothermic phase during which the water content drops from 8-12% to a few percent. This can be perceived by the popping sound, called the first crack at about 175-180°C (Raemy & Lambelet 1982). The second phase of roasting is accompanied by exothermic phase during which pyrolysis reaction takes place. If the beans are further heated at temperature above 200°C, second crack can

7 be heard. Majority of chemical changes and flavor development takes place between the first and second crack and consequently the bean swells double its original size. After the second phase of roasting, the beans must be rapidly cooled (using water or air) to stop the reactions and prevent over-roasting which can alter the quality of the product (Raemy &

Lambelet 1982; Schwartzberg 2002).

2.2.3 Roasting Equipment

The two most commonly used equipment’s for coffee roasting includes drum roaster and fluidized bed roaster. In the drum roasters, can either be indirectly heated drums where the heating elements is under the drum or direct-fired roasters in which the flame contacts the bean inside the drum and the drum rotates to ensure proper mixing of the beans for uniform heat transfer. Fluidized bed roasters force heated air through a screen under the coffee beans with a force sufficient to lift the beans. Heat is transferred to the beans as they tumble and circulate within the fluidized bed. Operating the drum roaster and the fluidizing bed roaster were so-called temperature profile mode, that is, along the identical development of coffee bean temperature over roasting time, the kinetics of aroma generation were similar in both processes (Baggenstoss et al. 2008).

2.2.4 Heat and Mass transfer inside the coffee bean during roasting

8 gradients of heat and mass transfer inside the bean (Eggers and Pietsch 2001; Bonnlander and others 2005). The continuous changes in physical parameters like moisture loss, bean swelling and internal cavities causes continuous changes in the heat transfer properties which creates complexities in modelling the heat transfer within the coffee bean. Various research to predict heat transfer properties in coffee during roasting have been performed

(Sivetz and Derousier 1979; Raemy and Lambelet 1982; Nagaraju and others 1997).

Raemy and Lambelet (1982) used heat flow calorimetry technique to determine the specific heat of coffee and chicory products and study their thermal behavior at 30°C.

The specific heat of green Arabica coffee was found to be 1.85 J g -1 °C -1 in beans with

7.5% humidity. The specific heat of roasted coffee bean was found to be 1.46 J g -1 °C -1 at 2.5% humidity. Schwartzberg developed a semi-physical model to evaluate coffee bean temperature and moisture content during the roasting in batch system. Hernandez et al. also found that the semi-physical model was effective to predict bean temperature and moisture content during roasting. However, there were some limitations associated with the model. Bean temperature and moisture content were considered uniform. But due to a large external heat transfer coefficient, a gradient of temperature and moisture exist. All these models had dealt with simplified geometry, particularly with spheres or semi- ellipsoids.

2.2.5 Physical changes during roasting

2.2.5.1 Moisture loss and color changes

Initial green bean contains 8-12% moisture. During roasting, both free and chemically bound water decrease significantly to about 2% depending on the degree of roast and time of water quenching. Water content reduces more rapidly during the first stage of roasting (Eggers and Pietsch 2001).

The color development in the coffee bean during roasting is the result of Millard reaction between the amino acids and reducing sugar. It has been reported that Maillard reactions mainly contribute to the color of roasted beans due to melanoidin, a colored polymer formation (Vural Go¨kmen, Hamide Z 2005). Color is used as an indicator to monitor the progress of roasting (Parliament 2000). Agtron is the most commonly used scale in the coffee industry to describe the coffee degree of roast based on color, measured using

Agtron spectrophotometers.

2.2.5.2 Structure of the bean

During roasting, as the coffee beans absorbs heat, the color shifts from yellow to an increasingly darker shade of brown and oil starts to appear on the bean surface during the later stages of roasting (dark roast). The flavor and color formation during roasting is accompanied by loss of water and organic mass. As a result, this causes development of internal pressure which causes changes in bean volume and porosity. Typically, coffee bean nearly increases 50% of their original volume. The roasting conditions have a significant impact on the microstructure of the coffee bean (Ortola and others 1998;

Schenker and others 2000). Higher increase in bean volume is caused under fast roasting conditions called as High Temperature Short Time (HTST) as compared to Lower

Temperature Long Time (LTLT) (Schenker and others 2000). Roasting also causes change in the bean density. The green bean density is typically in the range of 550-700 g/L while for roasted beans, it drops to 300-450 g/L (Bonnlander and others 2005).

2.2.6 Volatiles of Roasted Coffee

The research on coffee volatiles dates from 1920-30 (Reichstein and Staudinger 1926).

They identified 30 components including alkyl pyrazines, α-diketones and Furfuryl mercaptan even before the invention of gas chromatography & mass spectroscopy.

Flament and Grosch (2001) concluded that 2-furfurylthiol was a most important odorant in coffee. Using more sophisticated techniques, such as GC/MS, the number of characterized compounds in the roast coffee has increased to 800. A new concept developed in the 80’s showed a new approach to aroma characterization that is to identify the sensory active key aroma compounds, which clearly contribute to the aroma and flavor of the food (Schmid and Grosch, 1986). Using AEDA, only 14 compounds were selected to reproduce coffee aroma based on odor activity values (OAV), i.e. the ratio of concentration to odor threshold (Semmelroch et al. 1995). They quantified these potent odorants in ground roasted Arabica and Robusta coffee and in the corresponding brews and found that a change in aroma takes place from roasted to brewed coffee due to a change in the concentration of the odorants.

The two main varieties of coffee C. arabica & C. canephora greatly differ in their aroma profile and taste. Arabica beans tend to have a sweeter, softer taste like sugar, fruit, and berries. Robusta, however, has a stronger, harsher taste, with a grain-like overtone and peanut aftertaste. In a study comparing medium roasted robusta coffee from Indonesia and Arabica coffee from Colombia, concentrations of guaiacol, 4-ethylguaiacol and 4- vinylguaiacol and pyrazines were significantly higher in robustas while furanones in arabicas (Semmelroch and others 1995).

2.2.6.1 Furfuryl mercaptan

The flavor of the coffee beans is greatly influenced by the roasting conditions.

Sulphurous/roasty, earthy and smoky notes increase from light to dark roast. Study of key odorants indicated that the stronger sulphurous/roasty and smoky notes in the dark roast might be caused by 110% increase in Furfuryl mercaptan and three times increase in guaiacol (Mayer and others 1999). Compared to other roasted foods, sulfur constituents and phenols are formed in high amounts in coffee roasting, contributing to desirable coffee flavor (Tressl 1989).

Reichstein and Staudinger (1926) characterized Furfuryl mercaptan (2-furfurylthiol) as an important aroma constituent of roasted coffee which is a character impact component

(Ohloff and Flament 1978). Furfuryl mercaptan has a threshold of 0.005 ppb in water determined by application of Teflon sniffing tubes and odorless water (Guadagni and

Buttery, 1966). In concentrations of 0.01-0.5 ppb it was perceived like freshly roasted coffee, therefore, Furfuryl mercaptan may be considered as an impact component.

Omission experiments were conducted by Grosch (2002) to identify which of the potent odorants of coffee impact the aroma greatly and they also found that the absence of drastically impacted the roasty flavor of coffee. This important role established in these omission experiments for Furfuryl mercaptan confirmed the assumption of Reichstein and Staudinger, 1955 that it was a key component of coffee flavor.

Pentoses were shown to be significantly more effective than hexoses as precursors of

Furfuryl mercaptan. Model experiments indicated that Furfuryl mercaptan is produced from arabinogalactan, which is present in large amounts in green coffee. It was proposed that arabinose from the side chain of arabinogalactan is the likely precursor for furfural, a dehydration product of pentoses, and the likely immediate precursor to the formation of

Furfuryl mercaptan.

2.2.6.2 Pyrazines

Pyrazines have been identified in the headspace of many roasted beans and nuts and are a vital flavor component in thermally processed foods. Pyrazines contribute to characteristic of a sweet roasted nutty flavor in low concentration (Leunissen and others

1996). Pyrazines are formed through Maillard reaction and between a reducing sugar and amino acid. Pyrazines have been reported to contribute to roasted aromas and burnt flavor in coffee (Agresti et al. 2008). 2,3,5,6-tetraethylpyrazine, 2,3,5-trimethylpyrazine, 2- methylpyrazine, 2,3-dimethylpyrazine, 2,5- dimethylpyrazine and 2,6-dimethylpyrazine were identified in the head space of Arabica coffee beans using solid phase micro

13 extraction technique (Schiffman and Leffingwell 1981; Korhonova et al. 2009; Makri et al. 2011).

Koehler et al. (1970) used glucose and C labeled amino acids to investigate the origin of carbon atoms in methylpyrazine and dimethylpyrazine. They found that glucose was the exclusive carbon source and amino acids provided only the nitrogen in the amino acids.

Recently Weenen et al. (1994) used C-labeled sugars to explain incorporation and location of the carbon atoms in the resulting pyrazine and explain the mechanism of pyrazine formation using MS and NMR techniques. They found that the reaction of asparagines with hexoses, trioses or glycolaldehydes generates methylpyrazines, with 2,

5-methylpyrazine predominating. Alternatively, Amrani-Hemaimi and others (1995) used the labeled amino acids (glycine and alanine) model reaction with sugar to determine their contribution to pyrazine formation. They found that glycine and alanine not only act as the nitrogen source, but also contribute to the alkyl side chains of some alkyl pyrazine.

2.2.6.3 Furanones (Furaneol, Homofuraneol and Sotolon)

Furaneol and Sotolon has similar chemical structure despite of their diverse chemical origin. The physicochemical and olfactory properties of these two compounds is also similar which makes their determination advisable together. Furaneol and Homofuraneol were detected in Maillard model reactions between pentoses and different amino acids using GC-Olfactory technique. The Strecker aldehydes of glycine and alanine were actively involved in the formation of furaneol and Homofuraneol, respectively (Blank and Fay 1996). Both Furaneol and Stolon have sweet aroma profile which is usually

14 categorized as caramel, burnt sugar like and maple notes (Leffinwell J.C 2002). Both furaneol and Stolon has very low odor threshold values of 5 μg l−1in water–ethanol

(Ferreira et al. 2002).

2.2.6.4 Vinyl Guaicol

Green coffee contains high amounts of chlorogenic acids (CGA), including three isomers of caffeoylquinic acid, three of feruloylquinic acid and three of di-feruloylquinic acid.

During roasting, the phenolic volatiles, including guaicol’s is generated by the degradation of CGA. Guaiacol and its corresponding derivative 4-vinyl guaiacol have low odor thresholds and are potent flavor contributing components in roasted coffee

(Tressl 1989). Experiments showed that 4- feruloylquinic acid is the likely precursor for guaiacol, 4 vinylguaiacol. Sucrose, reducing sugars, free and peptide amino acids are important precursors to aroma compounds in coffee.

2.2.6.5 Acetic acid

Letner and Deathrage (1959) showed that the acetic acid content present in the green bean increased by a factor of 3 during roasting from 300°F (150°C) to 425°F (220°C).

They observed that the formation of acetic acid present in trace amount in the green bean depends largely on the roasting conditions. The acetic acid content decreased with a high roasting depending upon the roasting conditions.

Findings from a sensory study indicated that 2-Furfuryl mercaptan, 4-vinylguaiacol, several alkyl pyrazines, furanones, acetaldehyde, propanal, methylpropanal, and 2- and 3-

15 methylbutanal had the greatest impact on the coffee flavor. In contrast to 2-Furfuryl mercaptan, the other sulfur compounds (e.g., 3-methyl-2-buten-1-thiol, 3-mercapto-3- methylbutylformate) only have a limited influence on the coffee flavor. (Czerny and others 1999).

2.2.7 Mechanism of Volatile generation

The mechanism of coffee aroma formation is very complex and there is wide range of interaction between the different mechanism routes for a particular volatile generation

(Semmelroch et al. 1995).

The major mechanism for volatile formation include (Reineccius GA 1995) a) Maillard reaction: - A chemical reaction between amino acids (proteins, amino acids, trigonelline) and reducing sugars (carbohydrates, hydroxyl acids and phenols) to form aminoaldoses and aminoketones by condensation. b) Strecker degradation: - A chemical reaction between an α-dicarbonyl and an amino acid forming an aminoketone that condenses to form nitrogen heterocyclic compounds or reacts with formaldehyde to form oxazoles. c) Breakdown of sulphur amino acids, viz. cystine, cysteine and methionine, that are transformed into mercaptans, as well as thiophenes and thiazoles, after reacting with reducing sugars or intermediate products of the Maillard reaction. d) Breakdown of hydroxy-amino acids, viz. serine and threonine, able to react with sucrose to form mostly alkylpyrazines.

16 e) Breakdown of proline and hydroxyproline, that react with intermediate Maillard products; the former gives pyridines, pyrroles and pyrrolyzines, whereas the latter forms alkyl-, acyl- and furfurylpyrroles. f) Degradation of trigonelline, forming alkyl-pyridines and pyrroles. g) Degradation of the quinic acid moiety, forming phenols. h) Degradation of pigments, largely carotenoids. i) Minor lipid degradation, mainly diterpenes. k) Interaction between intermediate decomposition products that is not known.

2.2.8 Changes in coffee aroma profile during storage

Coffee aroma is not stable during storage. Temperature, Moisture, light and oxygen causes the development of off flavor. A collection of studies has found that losses of specific volatile compounds contribute to most coffee aroma loss. It was found that methanethiol and 2-methylpropanal gave the most intense aroma notes and dissipated two hours after roasting, and that after eight days of storage, methanethiol decreased to about

30% of its original amount (Holscher and Steinhart 1992). Czerny and Scieberle (2001) have also reported these compounds as key molecules lost in staling. One of the characteristic flavors of staling is rancidity, which is created by lipid degradation, the chemical oxidation or pyrolysis of fats and related compounds (Smith and others 2004;

Vila and others 2005). The process of chemical oxidation is accelerated by moisture

(Smith et al. 2004), oxygen (Vila et al. 2005) temperature (Nicoli et al. 1993; Huynh-Ba and others 2001).

Coffee Flavor Precursors

Formic

Caramelization SUGARS Fragmentation Acetic + Sucrose, glucose, fructose Glycolic Furaneol HMF lactic

+ Maillard, Strecker

AMINO ACIDS (Strecker-active AA)

+ TRIGONELLINE Pyridine Diketone Aldehyde Pyrazines Nicotinic derivatives acid CHLOROGENIC

ACIDS Furfuryl mercaptan 3-mercapto3- butylformate

Chlorogenic acid lactones Caffeic + Ferulic acids

+ Quinic acid CO2 ORGANIC Quinic acid Lactones ACIDS

Vanillin Guaicols Figure 2. 1 Scheme showing the main classes of volatile compounds formed from non- volatile precursors in the green beans during roasting (Yeretzian et al. 2002)

2.3 Analytical measuring techniques

2.3.1 Gas Chromatography

Several analytical techniques have been used to profile food products. Traditional methods of volatile analysis include GC-MS. Ewa Nebesny analyzed the impact on the coffee aroma in robusta coffee using different roasting methods. The volatiles generated from the ground coffee by the three roasting methods were analyzed in the headspace using GC-SPME. Coupled method which was subjected to less roasting time and moderately high final temperature enhanced the retention of the coffee volatiles better than the other two methods. Another approach so-called electronic noses have been applied with limited success to the characterization of food products (Gutierrez-Osuna R

2002). Some of the limitations of this technique are (1) low time resolution, (2) difficulty to relate the observed signal to relevant quality criteria in the end cup (3) lack of sensitivity.

2.3.2 Proton Transfer Reaction- Mass Spectrometry (PTR-MS)

PTR-MS alternative technology that allows real time monitoring of volatile organic compounds (VOCs) at high sensitivity up to parts per billion. This technique is based on chemical ionization of the target molecules by proton transfer reactions with

+ H3O primary ions. The protonated molecules are accelerated and followed by detection using an inline MS (Hewitt, C.N.; Hayward, S.; Tani, A 2003). It includes the fact that samples can be easily analyzed as there are no pre-concentration or separation processes. Also, volatile analysis using this technique offers very fast

19 response times and even allows real-time measurement of volatile samples. Lindinger et al. (2008) examined a technique for online detection of the volatiles during roasting using PTR-MS technique. The conditions of PTR-MS were set to simulate the sensory evaluation. The results were obtained by applying normalizing and standardizing procedure to both instrumental and sensory sets to selectively filter out mutual information. The real-time coffee volatile analysis resulted in the first robust model to predict sensory profiles of coffee from analytical data. Flurin Wieland el al. (2012) monitored volatile organic compounds in the off-gas of drum roaster by proton transfer reaction time-of-flight mass spectrometry. They developed predictive model that projects the online monitored VOCs profile of the roaster off-gas in real time. PTR-MS is a selective technique that deals only with a limited number of VOCs since it only detects compounds with a proton affinity higher than that of water. PTR-MS cannot differentiate isomeric and isobaric ions as they are all detected at the same nominal mass.

2.3.3 Selective Ion Flow Tube-Mass Spectrometry

Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) is a relatively new technique that allow detection and quantification of VOC’s in real time. SIFT-MS technique is used in many research areas including clinical diagnosis, environmental research, and food flavor analysis. SIFT-MS has a great potential for trace gas analysis to monitor the food flavor development during processing. Španĕl and Smith (1999) used SIFT-MS technology to identify the aroma compounds in cut onions, crushed garlic, and ripe bananas.

SIFT-MS involves the chemical ionization of trace volatile compounds by selected

+ + + positive precursor ions (H3O , NO and O2 ), coupled with mass spectroscopic detection to swiftly quantify targeted volatile compounds. Reactions between the precursor ions and VOCs create product ions that are detected and counted by a downstream mass spectrometer. Absolute concentration of trace gases can be calculated based on the known reaction rate constant of the precursor ion with the target compound. This allows real-time analysis of complex mixtures of volatile compounds without trapping or pre- concentration (Spanel and Smith 1999). Thus, there is no need of sample preparation and artifacts are not introduced by the pre-concentration steps. The advantages of using SIFT-

MS technique include instantaneous quantitative analysis of air and headspace with very high sensitivity and selectivity, direct analysis of high humidity samples, simplicity of operation, low maintenance and long term stability. One of the critical difference between

SIFT-MS and other chemical ionization (CI) techniques like proton transfer mass spectrometry (PTR-MS), that uses one reagent ion, typical H3O+, is that SIFT-MS

+ + + technique can use all three reagent ions (H3O , NO and O2 ) to analyze the sample. Use of several reagent ions allows positive identification of sample with high accuracy and allows a wide range of compounds to be detected. One example of the usefulness of using multiple precursor ions is illustrated in a study focusing on esters from banana emissions using H3O+ and NO+ precursor ions simultaneously. Since H30+ and NO+ ester chemistry is different, they found that the main ester emitted by ripe banana is ethyl acetate rather than its isomer methyl propionate (Španĕl & Smith 1999d)

Disadvantages are that the reaction kinetics if not already published must be calculated in order to precisely quantify the volatiles. Also, many volatile compounds produce the same masses after reaction with the reagent ions. If this happens, either the mass can be discarded or the result at that mass can be reported as a mixture.

2.3.3.1 SIFT-MS Working Principle

+ + + SIFT-MS utilizes three precursor ions (H3O , NO and O2 ) as they are not known to react significantly with the major components of air (nitrogen, oxygen, etc.), but can react with many of the very low level (trace) gases (Spanel and Smith 1999). Precursor ions are generated by a microwave discharge source. The selected precursor ions are introduced into the carrier gas (usually helium) through an Venturi orifice inlet. The trace gases from the sample enter the reaction chamber at a controlled rate and react with the precursor ions and undergo chemical ionization, depending on their chemical properties, such as their proton affinity or ionization energy to form product ions. The newly formed

"product ions" flow into the mass spectrometer chamber, which contains a second quadrupole mass filter, and an electron multiplier detector, detects the ions at the selected mass and measures the count rate of the ion in the desired m/z range (Smith and Španĕl

2005).

Determination of individual reaction rate coefficients are necessary for quantification of

VOCs. Kinetic studies of ion reactions using selected ion flow tubes have been done for many years (Adams and Smith 1976). The carrier gas was introduced through a mass flow meter at a controlled rate to determine the rate coefficient for the reaction of the

22 injected ions (Smith and Španĕl 2005). Based on the reduction in the precursor ion current and the increase of the product ion count rates, the rate coefficient for the reaction was calculated. These experiments provided information on how rapidly a precursor ion reacts with the analyte ion that provided large kinetic databases from thousands of ion neural reactions (Smith and Španĕl 2005). An ion-molecule reaction can result in more than one one product ion but can be overcome by determining the branching ratios (Smith and Adams 1987).

2.3.3.2 Scanning modes

Full Mass scans: Mass scans allows identification of unknown compounds in the sample and estimation of the concentration of the compounds by providing data on product masses for each reagent ion (Harper and others 2010). To obtain a complete mass scan, the sample is introduced into the carrier gas at a steady flow rate and the detection quadrupole ion is swept over a selected mass-charge ratio (m/z). The count rates are displayed by the on-line computer (Smith and Španĕl 2005).

SIM Scan: SIM scan is used to target specific compounds for quantitative analysis

(Harper and others 2010). In a SIM scan, the count rates of precursor ion and only selected product ion is monitored. SIM scans are achieved by rapidly switching the downstream mass spectrometer between the masses of all the primary ions and the selected product measured for a programmed time interval (Smith and Španĕl 2005). This scan mode allows more accurate quantification of the compounds compared to mass scan due to the fast flow rates of the carrier gas and the sample gas (Smith and Španĕl 2005).

2.3.3.3 Sampling with SIFT-MS

SIFT-MS analysis of ambient air is achieved by simply opening the sampling port to allow the air to enter the carrier gas and the detection mass spectrometer is operated in the Full Scan or SIM scan mode in order to sample the headspace for volatile analysis, liquid/solid sample is placed in fixed volume container closed by a septum that is punctured by a needle connected directly to the entry port of the SIFT-MS instrument

(Smith and Španĕl 2005).

2.3.3.4 Calibration and Validation of SIFT-MS

In contrast to the conventional analytical techniques such as GC-MS, where calibration is carried out by using set of dilutions of a reference substance of known concentration,

SIFT-MS is capable of automatic validation. Online validation is completed with dilution of the gas mixtures benzene, ethylbenzene, toluene, m-xylene, o-xylene and p-xylene within 15 min. Validation studies were conducted by determining the headspace concentrations of acetaldehyde, ethanol, and acetone above aqueous solutions of known concentrations (Španĕl and others 2002). Since the sample quantification in SIFT-MS is based on the flow rate of sample gas, count rates of precursor and analyte ion(s) and rate constant for precursor-analyte reaction, calibration involves obtaining rate constants and product ratios and is only necessary once for a model of SIFT-MS.

2.3.3.5 SIFT-MS application in Foods

SIFT-MS technique is used in many research areas including clinical diagnosis, environmental research. This analytical technique is extended for food flavor research.

+ + + The reactions using three ion precursors H3O , NO and O2 allows quantification of wide range of compounds in a complex food mixture. Multiple precursor ion allowed to detect differences between Trans-2- and Cis-3-hexenal and benzaldehyde and vanillin

+ + due to difference in their reactions with H3O and NO (Spanel and Smith 1999). Real time analysis of tomato volatile was performed to evaluate the flavor release during chewing and to study the effect of temperature on lipid-related volatile production (Xu and Barringer 2009). Online analysis of cocoa volatiles produced during roasting by

Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) was explored by (Huang &

Barringer 2011). They reported that the concentrations of alkylpyrazines and Strecker aldehydes increased as the roasting temperature increased from 120 to 170 °C. Also, the time to peak concentration of decreased from 13.5 to 7.4 min for pyrazines, and from

12.7 to 7.4 min for aldehydes as the roasting temperature increased from 120 to 170 °C for unalkalized Don Homero beans.

2.4 Non-linear curve fitting models

The Gompertz equation was modified by Zwietering et al. (1990) to describe growth parameters (peak concentration, growth rate and lag time) instead of mathematical parameters. Modified Gompertz has been widely used to model bacterial growth curve

(Zwietering et al. 1990). It has also been used to describe the progress of substrate

25 degradation, and soluble metabolite production in a batch fermentative hydrogen production process. Beuvniv et al. (2014) applied modified Gompertz model to study the kinetics of grass silages incubated with buffered ruminal fluid. Ruyan Dai and Loong-

Tak Lim (2014) estimated the release rate of Allyl Isothiocyanate from Mustard Seed

Meal Powder using modified Gompertz model. Gompertz model was used to describe the sigmoidal trend of flavor (acetaldehyde, diacetyl, acetoin, and 2-butanone) generation during lactic acid fermentation of milk base (Maria Tsevdou et al. 2013).

The Logistic model is used to describe Sigmoid shaped curves (Mohamed et al. 2005).

There have been numerous applications of the Logistic curves in biological, technological and economic fields.

Xian-Yang Shi and Han-Qing Yu (2005) used modified Logistic model to describe the cell growth of Rhodopseudomonas capsulata with various levels of acetate, propionate and butyrate. The synthesis-degradation of acrylamide in model systems has been recently described by Logistic model (Corradini MG, Peleg M 2006)

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Chapter 3: Real-time analysis of volatiles during coffee roasting using Selected Ion Flow Tube-Mass Spectrometry

Abstract

The results of this investigation describe a real-time analytical tool for online monitoring of volatile organic compounds (VOC’s) concentrations, released during the coffee roasting process. The overall objective of this research was to investigate the release dynamics of Volatiles Organic Compounds (VOC’s) generated during the roasting process and compare these patterns to VOC’s released from the coffee extract.

Ultimately, these relationships could be used in the development of online process control.

Selected Ion Flow Mass Spectrometry (SIFT-MS) in the roaster gas stream and compared to the amounts released from the coffee extract. An analysis of VOC concentrations in the roaster gas as a function of time revealed a lag time before the volatile concentration increased exponentially. The maximum concentration achieved during the roasting process for all VOC’s increased with roasting temperature. An analysis of VOC’s

33 released from the coffee extract indicated that the amounts of acetic acid decreased as the roasting temperature was increased from 210 °C to 230 °C. The concentrations of other volatile compounds (Furfuryl mercaptan, Furaneol+Sotolon, Pyrazines) released during extraction increased as the roaster temperature and time increased. At 210 and 220 °C

, the concentration of most VOC’s increased during the first 12-15 minutes of roasting before reaching a constant level. For some VOC’s, the concentration decreased at 20 minutes of roasting. A similar profile of volatile concentrations release was observed during extraction. At 230 °C the volatile concentrations increased during the initial 15 minutes of roasting, followed by a significant decrease in concentration. Excessive roasting (230 °C beyond 15 minutes), led to lower VOC concentrations generated in the roaster as well as in the coffee extract.

A Gaussian function was used to describe the profile of VOC concentrations released with time during roasting and from the coffee extract. The results indicated that the time to reach the peak concentration estimated for the VOC’s during roasting and during extraction were statistically similar indicating similar pattern of release during roasting and from the coffee extract, except for Furaneol+Sotolon, Furfuryl mercaptan, the mid- point time were statistically different at all three roasting temperatures (210, 220 & 230

℃).

The results from this investigation provide parameters for comparing the impacts of coffee roasting process on VOC concentration profiles. These same parameters can be used to estimate VOC release during coffee extraction, and provide the basis for online

34 control of the coffee roasting process as needed to achieve optimum profiles of volatile compounds.

3.1 Introduction

Roasting is a very critical step responsible for characteristic aroma and flavor generation in coffee Roasting process is comprised of two important phases. The first endothermic phase during which the water content drops from 8-12% to a few percent. This can be perceived by the popping sound, called the first crack at about 175-180 °C (Raemy &

Lambelet,1982). The second phase of roasting is accompanied by exothermic phase during which pyrolysis reaction takes place. If the beans are further heated at temperature above 200°C, second crack can be heard. Majority of chemical changes and flavor development takes place between the first and second crack and consequently the bean swells double its original size. After the second phase of roasting, the beans must be rapidly cooled (using water or air) to stop the reactions and prevent over-roasting which can alter the quality of the product (Raemy & Lambelet,1982; Schwartzberg, 2002).

Series of physical and chemical changes occurs in the coffee bean during the roasting process. Physical changes include moisture loss, color development, swelling of the bean, increase in bean porosity, decrease in density etc. Currently, color and physical indicators like weight loss are used to monitor the progress of roasting. But both color and weight loss measurements are indirect indicators of the flavor profile and most importantly, they are determined only after the completion of the roasting process and do not allow control during the actual process to achieve consistent result.

Several reaction pathways like Millard reaction, pyrolysis of sugar, degradation of chlorogenic acid, trigonelline etc. lead to the development of flavor compounds in coffee.

More than 800 volatile compounds have been identified in roasted coffee. It is practically very difficult to monitor and determine the Odor Activity Values (OAV) for all the volatiles. Recent Studies have focused on screening by the potent odorant in roasted coffee by charm analysis (Tressl 1989 & Acree 1993) and Aroma Extraction Dilution

Analysis (AEDA) (Ullrich et al. 1987, Grosch 1993). In both procedures, an extract obtained from food is evaluated by gas chromatography/olfactometry (GCO). The extract is then diluted, and each dilution is analyzed again by GCO. This procedure is performed until no odorant is perceivable by GCO. Finally, an aroma model is formulated using the quantitative data and compared with original food product. The first AEDA of roasted coffee was performed by Holscher et al. (1990). They identified Furfuryl mercaptan , 2- methylbutanoic acid , methional, 3-mercapto-3-methylbutylformate, furaneol, 3-isobutyl-

2-methoxypyrazine and b-damascenone as odorants showing the highest FD-factors in an

Arabica coffee from Colombian origin. Blank et al. (1991, 1992) compared the potent odorants of roasted Arabica (Coffea Arabica) and Robusta coffee (Coffea canephora) by

AEDA. Using AEDA, only 14 compounds were selected to reproduce coffee aroma based on odor activity values (OAV), i.e. the ratio of concentration to odor threshold

(Semmelroch et al. 1995). They quantified these potent odorants in ground roasted

Arabica and Robusta coffee and in the corresponding brews and found that a change in aroma takes place from roasted to brewed coffee due to a change in the concentration of the odorants. These studies provide evidence that these potent odorants which impact the

36 coffee flavor can be selectively monitored during the roasting process to improve the quality and consistency of roast profile.

Multiple reaction pathways and numerous volatiles generated during roasting makes the process very complex. Therefore, there is a need to monitor the volatiles online in real- time as they are generated. Maria-L Mateus et al. (2007) modelled the release kinetics of volatile organic compounds from roasted and ground coffee using diffusion and Weibull models. The release kinetics of volatile organic compounds were measured using PTR-

MS. Coffee samples roasted to different roasting degrees and ground to different particle sizes were studied under dry and wet stripping conditions. They concluded that release patterns were very complex and almost specific for each compound analyzed. In the case of prewetted coffee, varying particle size (∼400–1200 μm) had no significant effect on the VOC release rate, whereas for dry coffee, the release was faster for smaller particles.

Though these study focuses on the kinetics of volatile release in real time but this was performed in the end-product (roast and ground coffee). Therefore, this study doesn’t provide much information on the kinetics of volatile generation during the roasting process.

Online monitoring of volatile organic compounds (VOC) in the off-gas of a drum roaster by proton transfer reaction time of-flight mass spectrometry was performed by Flurin et al (2012). They establishment a predictive model that projects the online-monitored VOC profile of the roaster off-gas in real time. A robust model was developed to predict the sensory profile of espresso coffee from instrumental headspace data using PTR-MS. The

37 experimental PTR-MS conditions were designed to simulate those for the sensory evaluation (Christian Lindinger et al. 2008).

Selective Ion Flow Tube-mass spectrometry (SIFT-MS) is a relatively new analytical technique that can be applied for real-time volatile organic compound analysis. SIFT-MS utilizes soft ionization process to generate gas phase ions without extensive fragmentation thus enables accurate analysis of complex mixture of gases. Even PTR-MS works on the principle of soft ionization that uses H3O+ reagent ion but has been shown to give considerably more product ion fragmentation (Buhr et al. 2002). Another key feature of SIFT-MS is the upstream mass quadrupole, which allows the use of multiple precursor ions (H3O+, NO+ and O2+) which allows analysis of wide variety of compounds with higher precision. Selected Ion Flow Tube- mass spectrometry (SIFT-

MS) is widely applied for human breath analysis and disease diagnosis. Recently, this technique has been extended to monitor the volatile organic compounds (VOC’s) in food products. SIFT-MS technique was used for real-time detection and quantification of complex emission which constitute the aromas of some food product like cut onion, crushed garlic and ripe banana (Spanel and Smith 1999). Huang and Barringer (2011) monitored the Cocoa volatiles online during the roasting process using Selected Ion Flow

Tube-mass spectrometry. They reported that most VOC, including pyrazines, aldehydes, alcohols, acids, esters and ketones were generated and reached maximum concentration within 15 minutes of roasting. Currently, there are no published studies on the online analysis of VOC during coffee roasting using SIFT-MS.

Gaussian function is one of the most widely used functions in many domains such as neural network, signal and image processing to describe a normal distribution. Another application of Gaussian function is observed in multidimensional problems like pattern matching and data classifications (Lippman, 1989). In this study, Gaussian function was applied to model the VOC release in the roaster gas and VOC formation in the extract to compare the parameters estimated for the VOC’s in the roaster and parameters estimated for the VOC’s in the extract.

Objective of this experiment

To deliver consistent flavor profile, it is important to have precise control of the roasting process. Since series of complex reactions takes place during roasting, online measurements are the most efficient way to gain insight into the kinetics of coffee flavor generation. The primary objective of this research was to monitor the volatile compound generation in real-time using online SIFT-MS during coffee roasting and evaluate the influence of roaster temperature and time on the concentration of volatile released during the roasting process. to get insights on the dynamics of volatile organic compound

(VOCs) release in the roaster gas and compare it with the formation dynamics in the coffee brew. A second objective was to compare the generation of volatiles during roasting with the volatiles released during brewing of the coffee after roasting. A third objective was to demonstrate online process control during the roasting process to achieve a desired profile of volatiles from coffee during extraction.

3.2 Materials and Methods

Columbian origin Arabica coffee beans (from stauf’s coffee roaster, OH, USA) was used in the study. Selected Ion Flow Tube mass spectrometer was used for volatile analysis during coffee roasting.

3.2.1 Roasting Procedure

The green beans were weighed (100g/batch), fed into a benchtop horizontal drum roaster

(Model-CRB 101 Gene Café, Roast masters, USA) and roasted at 210, 220 and 230 °C for 10, 15 and 20 minutes respectively leading to a range of roasted bean from very light to very dark. Roasting experiments were carried out in three replicates.

3.2.2 SIFT-MS method development

A method was developed for roasted coffee volatiles and imported into the SIFT-MS.

The method consisted of eight volatile compounds which were selected based on careful review of the literature. The absolute concentration of these volatile compounds was

+ + + quantified from their reactions with the precursor ions H3O , NO , or O2 , based on known kinetic parameters (Spanel and Smith 1999). To avoid conflicts amongst the volatiles, the m/z values produced by reaction with 1 of the 3 precursor ions were carefully chosen (different volatiles produce the same m/z value) based on published data

(Table 3.1). The m/z values were chosen such that they are produced exclusively for each compound (Table 3.1). Therefore, the selected compound was uniquely measured

40 with the exceptions of 2,3-, 2,5-, and 2,6-dimethylpyrazine, which were reported as

Dimethyl Pyrazine (DMP) as well as Furaneol and Sotolon reported as

Furaneol+Sotolon. Online validation was performed before the start of each experiment.

Table 3.1. Details on the volatile organic compounds analyzed by SIFT-MS

Volatile Precursor Reaction Branch Mass to Product Organic Ion Rate (k) -ing Charge Compounds (10-9 cm3s- Ratio Ratio 1) (%) (m/z) + + FurfurylMercapt NO 1.6 80 114 C5H6O5 an + 61 CH3COOH2 + + Acetic acid H3O 2.6 100 79 CH3COOH .H2O + 97 CH3COOH2 .2H2 O + + Furaneol NO 2.5 95 128 C6H8O3 +Sotolon + + DMP O2 2.7 100 108 C6N2H8 + + Homofuraneol NO 2.5 100 142 C7H10O3 + + Vinyl Guaicol NO 2.5 100 150 C9H10O2 + + Vanillin NO 3.0 100 108 C8H8O3

3.2.3 SIFT-MS Roaster Gas Analysis

To continuously sample the volatiles generated during roasting, a high temperature resistant fiberglass duct hose (3 inches ID) was connected to the outlet vent of the roaster.

The connection was properly sealed using metal connectors to prevent escape of volatiles. The sampling needle of the SIFT-MS was inserted into the headspace of the fiberglass tube so that the instrument could continuously detect the volatiles

41 concentration in real time as they generated during different roasting process conditions.

Steel mesh (10 micron) was placed in the roaster outlet to prevent chaff from being sucked into the sampling needle (Figure 3.1).

EXHAUST VENT a)

SAMPLING NEEDLE SILICONE TUBE CONNECTED TO THE ROASTER OUTLET

COFFEE ROASTER

SIFT-MS

Figure 3.1. a) Experimental setup showing online analysis of VOC’s during the roasting process using SIFT-MS. b) Schematic representation of experimental setup

3.2.4 SIFT-MS coffee brew analysis

Beans from different roast batch were ground to fine-medium particle size using burr type coffee grinder (Cuisinart 8-oz brushed stainless burr coffee grinder). Coffee extract was prepared in a 500 ml pyrex glass vial from 12 g ground coffee in 200 ml water at 90

°C (Alexia N. Gloess 2014). Coffee powder to water ratio was kept as 0.0625 (1-gram coffee for every16 grams water). The glass vial was immediately closed using a rubber septum and placed for 30 minutes in a water bath at 60 °C for equilibration. The headspace was sampled by inserting SIFT-MS sampling needle into the rubber septum

(Figure 3.2).

Figure 3.2. Coffee extract head space VOC analysis using Selective Ion Flow Tube Mass Spectrometry

3.2.5 Gaussian function

Gaussian function model (Hongwei Guo 2011) was used to estimate peak VOC concentration, time to reach the peak concentration and the shape factor which describes the width of the curve was determined at different roasting temperature based on the experimental results. The equation is given by

(퐓퐢퐦퐞 − 퐭 )ퟐ 푪 = 퐂 퐞퐱퐩(− ( 퐦 )) (1) 퐏 ퟐ ∗ 퐰ퟐ

Where C (ppm) is the VOC concentration, CP (ppm) is the peak VOC concentration, tm

(min) is the time to reach the peak concentration, w (min-1) is the shape factor of the curve.

3.2.6 Statistical analysis

The parameters for each VOC at different roasting conditions was estimated using nonlinear regression analysis using SAS 6.1 enterprise software. Data analysis were evaluated based on three replications. Standard error was estimated for the parameters.

To test the statistical significance between VOC parameters estimated using Gaussian function in the roaster gas and extract for a given temperature, Z-score was calculated based on the modified equation (6) proposed by (Brame et al. 1998) at 95% confidence limit. The null hypothesis assumption is that there is no statistical difference between the

VOC parameters estimated for roaster gas and extract. The alternate hypothesis is that there is a statistical difference between the VOC parameters estimated for roaster gas and extract. If the calculated Z score was greater than or equal to the critical Z score (1.96 at

95% confidence limit), the null hypothesis was rejected resulting in statistical difference between the parameters. If the calculated Z score was less than the critical Z score, failed to reject the null hypothesis resulting in no statistical difference between the parameters.

풁 = (풃ퟏ − 풃ퟐ)/√(푺푬풃ퟏퟐ + 푺푬풃ퟐퟐ) (3)

Where b1, b2 are the regression coefficients and SEb1, SEb2 is the standard error of the regression coefficient.

3.3 Results and Discussion

3.3.1 VOC release from the roaster

The VOC release of Furfuryl mercaptan during the roasting process is shown in Figure

3.3. The release of other volatiles during roasting at different temperatures are presented in Appendix (A). At the beginning of the roasting process, there was a period of lag time before the volatile concentration increased. This lag time could be attributed to the first endothermic phase of roasting during which water evaporation takes place. After about 6 minutes of roasting, the concentration of acetic acid began to increase exponentially. The concentration of other volatiles increased slightly later in the roasting process. After an exponential increase, the concentration of volatiles reached a peak concentration. This peak concentration increased as the roasting temperature increased from 210-230 °C.

Some volatiles like Furfuryl mercaptan, Furaneol+Sotolon, acetic acid followed similar release pattern. The concentration of these volatiles continued to increase for first 12-15 minutes at 210 °C & 220 °C, before reaching peak concentration and followed by a slight decrease. The concentration of other volatile like homofuraneol, 4-vinyl guaicol, vanillin

45 and DMP increased up to about 15 minutes followed by a level off at 210 °C & 220 °C.

At 230 °C, the concentration of all the volatiles increased for first 15 minutes followed by an apparent decrease.

8 Furfuryl Mercaptan

2 Concentration (ppm) Concentration

0 0 5 10 15 20 25 Time (min) Experimental (210 ℃) Experimental (220 ℃) Experimental (230 ℃)

Figure 3.3. Release of Furfuryl Mercaptan during roasting at three temperatures (210, 220

& 230 °C)

Schenkner et al. (2002) studied the impact of roasting conditions on the formation of aroma compounds. These investigators reported a decrease in the concentration of 2- ethyl-3,5-dimethylpyrazine, propyl pyrazine, which indicated that aroma formation was already superimposed by an accelerated degradation of compounds due to more intense processes (220 °C -600 s or 260 °C -160 s). Similar observation was made by Alexia N.

Gloess et al. (2014) who investigated flavor formation during coffee roasting using PTR-

MS. They reported an increase in the intensity of VOC in Columbian coffee roasted at

190-200 °C followed by a more pronounced plateau with longer roasting time. These 46 investigations also indicated that changing the roast profile from HTST (High

Temperature Short Time) to LTLT (Light Temperature Short Time) resulted in pronounced change in the VOC formation profiles. These results indicated that small changes in the roasting temperature and time can impact the coffee flavor profile as due to the formation of different concentration of VOC’s during roasting. These observations provide a basis for creating wide range of flavor profiles in response to consumer preferences. On the other hand, it also provides information regarding the optimum roasting conditions beyond which the key flavor compounds decreases significantly.

3.3.2 VOC formation in coffee extract

To explain the influence of different roasting conditions on the profiles of volatile formation in the coffee extract, the coffee extract from different roasting conditions were analyzed for the same set of seven volatiles as measured during the roasting process using

SIFT-MS. Figure 3.4 shows the release of Furfuryl mercaptan in the coffee extract during different roasting temperatures. The analysis of coffee extract indicated that formation of

Furfuryl mercaptan, Homofuraneol, Dimethylpyrazine and Vanillin was favored at high temperature (230 °C) and resulted in highest concentration of these volatiles. The reverse trend was observed for acetic acid. At 210 °C & 220 °C, the volatile (Furfuryl mercaptan,

4-Vinyl guaicol, Homofuraneol, Dimethyl pyrazine and vanillin) formation increased with roasting time but started to level off after 15 minutes. Furaneol+Sotolon concentration increased throughout the roasting process at 210 °C & 220 °C. At 230 °C, the concentration of the volatiles (Furfuryl mercaptan, Homofuraneol, Dimethylpyrazine,

4-Vinyl guaicol, Furaneol+Sotolon) increased till 15 minutes followed by an apparent decrease.

Furfuryl mercaptan

0.5

0.4

0.3

0.2 Concentration (ppm) Concentration 0.1 0 5 10 15 20 25

Time (min) 210 °C 220 °C 230 °C

Figure 3.4. Release of Furfuryl mercaptan in the extract at the three-roasting temperature (210, 220, 230℃)

Several researchers have reported a similar decrease in the concentration of volatiles during roasting for longer times at higher temperatures. Baggenstoss et al. (2008) investigated the effect of excessive roasting on the formation and degradation of aroma compounds at 232 °C. These investigators reported a decrease in the aroma compounds from the coffee roasted for excessive periods. In addition, a steady decrease in the levels of 4-vinyl guaiacol throughout excessive roasting and decrease in Furfuryl mercaptan concentration beyond 25 minutes of roasting at 232 °C. Since the coffee roasted was with a fluidized bed roaster, the direct comparison of time intensity profile with this study is limited.

Chahan Yeretzian et al. (2002) performed on-line monitoring of volatiles during coffee roasting using PTR-MS. A decrease in the volatile intensity and change in profile for coffee roasted beyond medium roast level was reported and are consistent with the observations in this investigation. Schenker et al. (2002) observed increase in the intensity of some VOC with darker roast and decrease in the concentration of others.

Acetic acid concentration increased for the 15 minutes at 210 °C and 220 °C (slight increase) followed by decrease in concentration. In contrast, the acetic acid concentration continuously decreased throughout the roasting process at 230 °C. Alexia N. Gloess et al.

(2014) also reported a decrease in the acetic acid concentration with increasing roast degree.

3.3.3 Comparison of VOC generation during roasting and from coffee extract

Although the intensity of the VOC’s from roasting and in the extract were different, an interesting analogy between the VOC pattern of release was observed. During roasting, the concentration of most VOC’s increased during the first 15 minutes followed by a steady-state or slight decrease at 210 °C & 220 °C and more obvious decrease at 230 °C

(Figure 3.3). When Compared to patterns from extract, the formation of volatiles increased during the first 15 minutes followed by a plateau at 210 °C & 220 °C and a decrease at 230 °C (Figure 3.4). It should be noted that the extract analysis was completed at only for three times (10, 15 & 20 minutes). This similarity among the VOC release patterns during roasting and from extract provides a promising opportunity for online monitoring of the roasting process. A thorough analysis of VOC release patterns

49 during roasting as compared to the VOC pattern in the extract could enable the prediction of changes (VOC concentration) in the final product (coffee extract) based on the release pattern observed during roasting. A predictive model (Gaussian function) was developed to estimate the parameters to compare the VOC release in the roaster gas and VOC formation in the extract.

3.3.4 Gaussian function

A Gaussian function was used to model the pattern of VOC release during roasting and for the release of VOC in the extract. The peak concentration (C∞), shape factor (w) and the time (tm) to reach the peak concentration were estimated using Gaussian function. The release of Furfuryl mercaptan during roasting and from coffee extract fitted with

Gaussian function is shown in Figure 3.5 and 3.6 respectively. The physical significance of shape factor is difficult to interpret. Therefore, for simplicity we have chosen to provide statistical comparison of peak concentration estimates and time to reach peak

VOC concentration during roasting as well as from extract (Table 3.2 and Figure 3.7).

8 Furfuryl Mercaptan

2 Concentration (ppm) Concentration

0 0 5 10 15 20 25 Time (min) Experimental Predicted

Figure 3.5. Experimental Vs Predicted (Gaussian) release of Furfuryl mercaptan from roaster at 230 °C

Furfuryl mercaptan 0.5

0.4

0.3

0.2 Concentration (ppm) Concentration 0.1 0 5 10 15 20 25

Time (min) Experimental Predicted

Figure 3.6. Experimental Vs Predicted (Gaussian) release of Furfuryl mercaptan from the coffee extract at 230 °C

Table 3.2. Statistical Comparison of peak concentration (CP) of VOC in roaster gas and extract estimated using Gaussian function. (Value ± Standard Error)

Roaster Extract Temperature Volatile Compounds 퐂 퐂 (℃) 퐏 퐏 (ppm) (ppm) 210 1.2007 ± 0.006a 0.362 ± 0.017b Furfuryl mercaptan 220 2.497 ± 0.014A 0.425 ± 0.009B 230 6.718 ± 0.037i 0.460 ± 0.014ii 210 0.732 ± 0.005a 0.327 ± 0.018b Furaneol+Sotolon 220 1.928 ± 0.013A 0.375 ± 0.017B 230 10.255 ± 0.078i 0.369 ± 0.010ii 210 0.295 ± 0.001a 0.066 ± 0.003b Homofuraneol 220 0.765 ± 0.003A 0.081 ± 0.003B 230 2.602 ± 0.017i 0.080 ± 0.002ii 210 0.103 ± 0.0005a 0.308 ± 0.015b Dimethyl Pyrazine 220 0.629 ± 0.004A 0.372 ± 0.022B 230 0.315 ± 0.030i 0.360 ± 0.010ii 210 19.216 ± 0.144a 5.211 ± 0.337b Acetic acid 220 35.463 ± 0.243A 4.872 ± 0.191B 210 0.064 ± 0.0002a 0.053 ± 0.002b 4-Vinyl Guaicol 220 0.333 ± 0.001A 0.065 ± 0.001B 230 1.709 ± 0.013i 0.063 ± 0.002ii 210 0.093 ± 0.0003a 0.069 ± 0.003b Vanillin 220 0.468 ± 0.002A 0.099 ± 0.004B 230 1.878 ± 0.013i 0.111 ± 0.002ii

(Lowercase Alphabets used for comparison at 210 °C (a, b), Capital Alphabets used for 220 °C (A, B), Roman numerals used for 230 °C (i, ii). Same alphabets and numbers within a temperature indicate no statistical difference between the roaster and extract parameter at 95% confidence limits).

3.3.5 Comparison of VOC parameters

The comparison between the time to reach the peak concentration (tm) during the roasting and from extract is presented in Figure 3.7. The result shows that the time to reach peak concentration during roasting and from the extract was statistically similar for the volatile compounds. The exceptions were Furaneol+Sotolon, Furfuryl mercaptan at 210, 220 &

230 °C, for which the time to reach the peak concentration during roasting and the extract was statically different.

Beyond the time to reach peak concentration, the VOC concentration starts to decrease or remain at steady state. This similarity between the VOC’s generation trend in the roaster and the extract demonstrates the possibility of monitoring the VOC’s in the roaster gas and predicting the change in the VOC’s pattern in the extract. In summary, the roasting conditions needed to retain the maximum flavor could be predicted.

For a given temperature, the shape factor (w) and peak concentration (CP) estimated for the VOC’s in the roaster and the extract were statistically different (Z > 1.96). Despite of the difference, the value of these parameters at a given temperature and time is unique for each volatile compound in the roaster and the extract. Therefore, onetime estimation of correlation between these parameters in the roaster and the extract for all volatile compounds at different process temperature, the VOC parameters in the roaster could be used to predict parameters in the extract.

Furfuryl mercaptan Homofuraneol 20 b B 25 A 2 a 1 20 b A A 15 a 1 1 15

10 (min)

(min) 10

m t

5 t 5 0 0 210℃ 220℃ 230℃ 210℃ 220℃ 230℃ Temperature ℃ Temperature ℃ Roaster Extract Roaster Extract

Furaneol+Sotolon Pyrazine 40 b 25 20 a a A A 30 A 2 1 1 A 1 15

20 a

(min) (min)

m 10

m t 10 t 5 0 0 210℃ 220℃ 230℃ 210℃ 220℃ 230℃ Temperature ℃ Temperature ℃ Roaster Extract Roaster Extract

Vinyl Guaicol Vanillin 25 25 A A A A a 1 20 a a 1 1 20 a 1

15 15 (min)

(min) 10 10

t t 5 5 0 0 210℃ 220℃ 230℃ 210℃ 220℃ 230℃ Temperature ℃ Temperature ℃ Roaster Extract Roaster Extract

Figure 3.7. Statistical comparison of mid-point time of VOC’s between Roaster and extract

3.4 Conclusion

On-line measurement of volatile compounds using SIFT-MS accompanied with off-line analysis has been demonstrated. Different time-temperature conditions of roasting led to varied composition of volatile compounds in the roaster and the extract. A decrease in the concentration of volatile compounds occurred at extended roasting times and/or temperatures.

Gaussian function was used to describe the pattern of volatile generation in the roaster and the coffee extract in good agreement with the experimental data. On-line and off-line analysis of volatile compounds allowed establishing correlation between the roaster and the extract parameters that revealed evident similarities in the VOC release trend which demonstrates the potential for on-line process control during the roasting process. On-line process control could be achieved by monitoring the characteristic pattern in the roaster and regulating the time-temperature of roasting accordingly to obtain a desired profile in a cup of coffee.

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Chapter 4: Kinetic parameters for volatiles generated during coffee roasting

Abstract

The influence of time and temperature during roasting of coffee beans has been investigated using real-time sensing of volatile compounds during the roasting process.

The patterns of volatile concentrations generated as a function of time have been analyzed using a modified Gompertz model and a modified Logistic model. The experimental data for seven Volatile Organic Compounds (VOC’s) were measured within the roaster at three roasting temperature (210 °C, 220 °C & 230 °C), for up to 20 minutes using online Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS). The kinetic parameters were estimated based on cumulative volatile concentrations as a function of time. Activation energy coefficients were estimated using rate constant from the models.

The increase in volatile concentration within the roaster followed a sigmoid pattern.

Modified Logistic model described the volatile generation pattern with good statistical fit and estimated kinetic parameters values with low NRMSE (0.004-0.04) and SEE%. The rate constant and maximum cumulative concentration increased significantly with an increase in temperature for all VOC’s. The activation energy coefficients for the volatiles calculated using modified Gompertz and Logistic models were in the range of 158.772 to

356.67 kJ/mol and 158.67 to 361.027 kJ/mol, respectively. The range of activation

58 energy coefficients for the volatile compounds indicates reactions generating the volatile compounds are very sensitive to temperature.

4.1 Introduction

Several factors affect the flavor of coffee like green bean variety, weather conditions, soil chemistry, the way the cherries are picked and further processed etc. Yet, roasting is considered one of the most important step that is responsible for flavor generation in coffee (Schenker et al, 2002; Baggenstoss et al. 2008). Series of physical and chemical reactions make the roasting process highly complex. Therefore, achieving consistency during the roasting process is not simple. This requires detailed understanding of the dynamics of the volatile organic compound (VOC) generation which eventually determines the flavor of coffee. Kinetic study is one of the most efficient method to get insights on the rate of chemical reaction and the effect of different experimental conditions on the reaction mechanism as well as the construction of mathematical models to describe the characteristics of chemical reaction.

The flavor of roasted coffee depends upon the degree of roast which is by the light reflectance of the ground beans in the industry or based on visual inspection of the color of the beans. Kinetics studied have been performed on the color formation and weight loss during coffee roasting. Xiuju Wang & Loong-Tak Lim (2014) performed kinetic modelling study to describe the color development and roast loss during coffee roasting.

They demonstrated that the lightness of the roasted coffee and roast loss followed two- stage processes. Lightness of roasted coffee followed pseudo first-order reaction models for the giving activation energies of 59.7 and 170.2 kJ/mol for the first and second stages,

59 respectively. Roast loss data followed zero-order reaction kinetics, with activation energies of 52.9 and 181.3 kJ/mol for the first and second stage, respectively. But this technique of measuring the degree of roast based on color of the beans using light reflectance or visual color inspection of the bean color has been demonstrated ineffectual

(Feldman, Ryder & Kung, 1969; Purdon & McCamey, 1987), since the beans with different chemical compositions, and hence different aroma and flavor, could have the same average readings in light reflectance measuring instruments. Therefore, both color and weight loss measurements are indirect indicators of the flavor profile. Also, these parameters hardly consider that the flavor generation depends on the complete time- temperature conditions of roasting. More importantly, they are determined only after the completion of the roasting process and do not allow control during the actual process to achieve consistent result. Consequently, the volatile compounds which are responsible for the flavor of coffee needs to be monitored during the roasting process for consistent flavor delivery.

The effect of time-temperature combinations of roasting processes on the kinetics of aroma formation in coffee was investigated (Baggenstoss et al 2008). They monitored the volatile generation of 16 aroma compounds at high temperature-short time and low temperature-long time conditions. They found that compared to low temperature-long time roasting, high temperature-short time roasting resulted in considerable differences in the physical properties and kinetics of aroma formation. Schenker et al. (2002) monitored the flavor formation of coffee at different time-temperature combination by sampling at regular intervals during the roasting process and analyzed the coffee volatile compounds

60 with gas chromatography-mass spectrometry. The result from their studies shows that the trend in volatile generation is a clear function of time-temperature conditions in the roaster. Silwar and Lüllmann (1993) investigated the influence of roasting temperature on flavor generation in Robusta coffee. Coffee samples were roasted at different temperatures to various degrees of roast. They reported an increase in the total number of volatiles with increasing temperature up to 250 °Cfollowed by decrease in volatile release beyond this temperature. The authors concluded from tasting cups of brewed coffee that aroma formation starts at around 170 °C, when a peanut-like roasted flavor can be perceived. The coffee like flavor starts to develop from 180-190 °C while the “real” flavor of roasted coffee was perceived only at 220 to 230 °C. Beyond this point, the flavor was refereed to be slightly over roasted (240 °C) and typically over-roasted (250 to

260 °C). However, in these studies, the volatiles were not monitored online in real time and therefore do not provide understanding on the continuous formation of volatiles during the roasting process.

Sigmoid models, such as Gompertz (Gibson et al., 1987; Gompertz, 1825; Zwietering et al., 1990) and Logistic (Gibson, Bratchel, & Roberts, 1987; Neter, Knuter, Nachtsheim,

& Wasserman, 1996; Zwietering, Jongenburger, Rombouts, & Riet, 1990) models have been widely applied in the field of predictive microbiology. Most of the equations describing a sigmoidal growth curve contain mathematical parameters. Zwietering et al.

(1990) reparametrized the original Gompertz and Logistic models to a modified model

(modified Gompertz and modified Logistic) containing parameters with biological meaning for microbial growth. There are indeed several analogies between volatile

61 release during coffee roasting and bacterial growth. The initial lag phase observed in the bacterial growth curve when the bacteria adapt themselves to growth conditions. Similar lag phase was observed during the roasting process which can be attributed to the drying phase during which primarily water evaporation takes place. The exponential bacterial growth phase can be compared with the volatile release during the exothermic phase of roasting. The stationary and death phase of the bacteria due to lack of nutrients and environmental temperature can be compared to the steady state or decrease in the volatile concentration during excessive roasting.

Modified Gompertz model has also been applied in the field of chemical kinetics to model the color changes during caramelization reaction (Mafalda Quintas et al. 2007).

Kloek et al. (2000) used a modified Gompertz equation to describe the crystallization kinetics of fully hydrogenated palm oil in sunflower oil solutions. Ruyan Dai and Loong-

Tak Lim (2014) estimated the release rate of Allyl Isothiocyanate from Mustard Seed

Ramaswamy (2002) studied the color and texture changes in ripening bananas. They reported that the time dependent L(Lightness), color difference and puncture force values followed a Logistic model. Xian-Yang Shi and Han-Qing Yu (2005) used modified

Logistic model to describe the cell growth of Rhodopseudomonas capsulata with various levels of acetate, propionate and butyrate. The synthesis-degradation of acrylamide in

62 model systems has been recently described by Logistic model (Corradini MG, Peleg M,

2006).

The volatile organic compounds (VOC’s) are responsible for the flavor in coffee and different temperature conditions can alter the formation dynamics of the VOC’s.

Although, the effects of roasting conditions on the kinetics of VOC’s formation have been reported in literature, systematic investigation to quantify the kinetic parameters is currently lacking.

Objective of this experiment

The objectives of the research were to identify appropriate models to describe the concentrations of volatile compounds released during roasting of coffee beans, and to estimate the kinetic parameters to describe the influence of time and temperature on release of the volatile compounds.

4.2 Materials and Methods

Columbian origin Arabica coffee beans (from stauf’s coffee roaster, OH, USA) was used in the study. Selected Ion Flow Tube mass spectrometer was used for VOC analysis in the roaster gas stream.

4.2.1 Roasting Procedure

The green beans were weighed (100g/batch), fed into a benchtop horizontal drum roaster (Model-CRB 101 Gene Café, Roast masters, USA). Three roasting temperature

(210 °C, 220 °C and 230 °C) and roasting time of 20 minutes was considered for the kinetic study. Roasting experiments were carried out in three replicates.

4.2.2 SIFT-MS Analysis

The SIFT-MS method development and analysis has been discussed in detail in chapter

3 (Section 3.2.2 & 3.2.3). The release kinetics of 7 volatile compounds was monitored in the roaster gas using online SIFT-MS.

4.2.3 Kinetic Models

In this study, the kinetics of VOC release in the roaster gas has been presented based on

Cumulative Concentration Release of volatile compounds as a function of roasting time.

4.2.3.1 Kinetics based on Cumulative Concentration Release

Modified Gompertz and Logistic models (Zwietering et al. 1990) have been applied to describe the volatile organic compounds (VOC’s) generation kinetics during coffee roasting. The modified Gompertz and Logistic describes cumulative VOC release during the coffee roasting process. Using these models, the maximum cumulative VOC concentration, rate of VOC concentration release and the lag time was determined at different roasting temperature based on the experimental results. The equation is given by

e Modified Gompertz model C = ( C∞)exp {− exp [(k ∗ ) (tl − t) + 1]} (1) ((C∞)

k Modified Logistic model C = ( C∞)/{1 + exp [(4 ∗ ) (tl − t) + 2]} (2) ((C∞)

(Where ‘C’ is the VOC cumulative concentration (ppm) at the roasting time ‘t’ minutes;

-1 ‘C∞’ is peak cumulative concentration (ppm) of VOC release; ‘k’ is rate (min ) of VOC concentration release; ‘e’ is exp(1) = 2.718; tl (min) is the duration of the lag phase).

4.2.3.2 Modelling temperature effect on VOC release rate

Temperature is known to have significant effect on food reaction kinetics. This effect is often translated into a dependence of kinetic parameters on temperature generally expressed with an Arrhenius type equation (3)

E 1 ln( k) = − ( a) ( ) + ln(A ) (3) R T o

(Where k is the rate constant; Ea is the activation energy in Joules; T is the absolute temperature in kelvin; Aois the pre-exponential factor).

4.2.4 Statistical Analysis

The kinetics parameters for each VOC at different roasting conditions was estimated using nonlinear regression analysis using SAS 6.1 enterprise guide software. Data analysis were evaluated based on three replications. The Normalized Root Mean Standard

Error (NRMSE) was calculated for predicting the goodness of fit for the models with the experimental data. Standard error (%) was estimated for the kinetics parameters to compare the two models (modified Gompertz and Logistic).

To test the statistical difference between the kinetic parameters estimated for each VOC at different roasting temperature, Z-score was calculated based on the modified equation

(4) proposed by (Brame et al. 1998) at 95% confidence limit. The null hypothesis 65 assumption is that there is no statistical difference between the kinetic parameters estimated at different temperature. The alternate hypothesis is that there is a statistical difference between the kinetic parameters estimated at different temperature. If the calculated Z score was greater than or equal to the critical Z score (1.96 at 95% confidence limit), the null hypothesis was rejected resulting in statistical difference between the kinetic parameters. If the calculated Z score was less than the critical Z score, failed to reject the null hypothesis resulting in no statistical difference between the kinetic parameters.

Z = (b1 − b2)/√(SEb12 + SEb22) (4)

Where b1, b2 are the regression coefficients and SEb1, SEb2 is the standard error of the regression coefficient.

4.3 Result and Discussion

The concentration of volatiles released during coffee roasting increased according to a sigmoid pattern. Modified Gompertz and Logistic model were used to describe the cumulative VOC generated during roasting. The cumulative concentration of Furfuryl mercaptan with roast time at the three roasting temperatures (210 °C, 220 °C & 230 °C) as described by the modified Logistic and modified Gompertz models is presented in

Figure 4.1 and 4.2 respectively. The cumulative concentration release kinetics of other volatile compounds is presented in the Appendix (E).

Furfuryl mercaptan

1400 1200 1000 800 600 400 200 0

0 5 10 15 20 25 Cumulative Cumulative Concentration(ppm) Time (min)

Figure 4.1. Cumulative volatile generation- experimental and modified Logistic model

Furfuryl mercaptan

1400 1200 1000 800 600 400 200 0 0 5 10 15 20 25

Time (min) Cumulative Cumulative Concentration(ppm)

Figure 4.2. Cumulative volatile generation- experimental and modified Gompertz model

4.3.1 Comparison between modified Gompertz and Logistic model

The estimated kinetic parameters from modified Gompertz and Logistic models for

Furfuryl mercaptan and Furaneol+Sotolon are given in Table 4.1. The NRMSE values of the volatile compounds ranged between 0.004- 0.04 for modified Logistic model and ranged between 0.001- 0.01 for modified Gompertz model. The NRMSE range of both the models is indicative of a good fit. Therefore, the second factor to be considered is the accuracy of estimation of kinetic parameters from both the models based on SEE %. The values of rate constant and lag time estimated from both the models were comparable.

However, the peak cumulative concentration estimated from modified Gompertz model was consistently high compared to the experimental values for all volatile compounds

(Table 4.1) with high SEE% (0.004-5.68) while the peak cumulative concentration estimated from modified Logistic was comparable to the experimental values with low

SEE% (0.001-0.02). Therefore, it can be concluded that modified Logistic model provided a better statistical fit and estimation of kinetic parameters than modified

Gompertz model based on low NRMSE values and low SEE% for the kinetic parameters.

Table 4.1. Result of kinetic study- modified Logistic and Gompertz model

Modified Logistic Model Modified Gompertz Model

Cumulative Cumulative Cumulative Rate Lag VOC Lag Volatile VOC VOC Rate Temperature C∞ (C∞) Constant Time C∞ Constant Time Compound NRMSE (ppm) NRMSE (°C) (ppm) (ppm) k (tl) (k) (tl) (min-1) (min) (min-1) (min) Observed Predicted Predicted 210 318.7 37.6 5 10.0 0.016 384.9 33.9 9.5 0.005 Furfuryl 311.24 Mercapt- 220 667.01 714.3 78.8 10.5 0.018 929.7 70.84 9.9 0.006 an 230 1367.35 1383.4 213 11.3 0.008 1590.2 191.4 10.9 0.008 210 189.2 23.3 9.90 0.024 219 21.3 9.4 0.01 Furaneol 190 + 220 497 544.3 58.4 10.98 0.042 761.2 52.65 10.4 0.028 Sotolon 230 1608 1603.6 323 12.34 0.009 1740.5 300.7 12.0 0.016

4.3.2 Influence of temperature on kinetics parameters

The influence of temperature on the kinetic parameters (k, C∞, tl) was analyzed statistically using a Z score with 95% confidence limits. The estimated kinetic parameters for Furfuryl mercaptan and Furaneol+Sotolon based on modified Gompertz and Logistic model is given in Table 4.1. Increased temperature of roasting caused the rate constant

(k) and peak cumulative concentrations (C∞) The influence of temperature on the cumulative peak concentration and the rate constant for Furfuryl mercaptan is presented in Figure 4.3. The result indicated a very significant increase in the peak cumulative concentration (C∞) release and the rate of VOC (k) concentration release with an increase in temperature for all the volatile compounds, indicating that the peak cumulative concentration and rate constant was highly temperature dependent. Similar increase in

VOC’s concentration with increase in temperature has been reported by researchers also.

Silwar and Lullmann (1993) reported continuous increase in the VOC concentration with increasing temperature up to 250 °C. Baggenstoss et al. (2008) who studied the impact of roasting conditions on the aroma formation in coffee beans also reported increase in the peak concentration of VOC with temperature. Alexia Gloess et al. (2014) reported higher rate of VOC release at High Temperature Short Time (HTST) compared to Medium

Temperature Medium Time (MTMT) and Low Temperature Long Time (LTLT) with roast time for Colombian coffee.

Furfuryl mercaptan

2000 C 1600

1200 B

800 A

400

0 210 220 230

Peak Cumulative Concentration (ppm) Concentration Cumulative Peak Temperature ℃ Peak Concentration (Cumulative)

Figure 4.3. Influence of Temperature on the peak cumulative concentration of Furfuryl mercaptan estimated from modified Logistic model along with statistical comparison

There was no clear trend in the lag time (tl) of the volatile compounds with an increase in temperature from 210 to 230 °C. For some VOC’s like Furfuryl mercaptan, Furaneol +

Sotolon and vanillin, the lag increased with increase in roasting temperature while for other VOC’s like pyrazine and Vinyl Guaicol, the lag time decreased as the roasting

70 temperature was increased. However, the differences in the lag time of a VOC between different roasting temperature was less.

4.3.3 Determination of Activation Energy Coefficient

The influence of temperature on rate constant was accomplished using an Arrhenius analysis. An Arrhenius plot of rate constants for Furfuryl mercaptan is shown in Figure

4.4. The Arrhenius plots for other volatile compounds are included in Appendix (G). The activation energy coefficients were calculated for all volatiles using the rate constants obtained from the modified Gompertz and modified Logistic model are summarized in

Table 4.2. The Arrhenius relationship provided good estimation of activation coefficient with high R2 values ranging from 0.97-0.99 for modified Logistic model and 0.95-0.99 for modified Gompertz model. Both models provided comparable estimation of activation energy coefficient for the volatile compounds. The activation energy coefficient for the volatiles calculated using modified Gompertz and Logistic was in the range of 158.772 to 356.67 kJ/mol and 158.67 to 361.027 kJ/mol respectively. The range of activation energy coefficient for volatile compounds indicates high sensitivity to temperatures of the reactions generating the volatile compounds. Xiuju Wang & Loong-

Tak Lim (2014) performed kinetic modelling study to describe the color development and roast loss during coffee roasting. They demonstrated that the lightness of the roasted coffee and roast loss followed two-stage processes. Lightness of roasted coffee followed pseudo first-order reaction models for the giving activation energies of 59.7 and 170.2 kJ/mol for the first and second stages, respectively. Roast loss data followed zero-order

71 reaction kinetics, with activation energies of 52.9 and 181.3 kJ/mol for the first and second stage, respectively. Similar to the range of activation energy of volatile compounds, the activation energy reported for the second stage of color development and roast loss indicate higher temperature sensitivity. During the roasting process, due to the depletion of low molecular weight carbohydrate, the degradation of polysaccharide become the main source of sugar for the Maillard reaction. Therefore, the degradation of polysaccharides might be the rate-limiting step during the second stage of roasting

(exothermic phase of volatile generation), resulting in higher temperature dependence than initial stage of roasting (Xiuju Wang & Loong-Tak Lim 2014).

Furfuryl Mercaptan volatile release 6 y = -20989x + 46.933 5 R² = 0.9905 4 y = -21017x + 47.096 3 R² = 0.9907

Ln Ln k 2

0 0.00198 0.002 0.00202 0.00204 0.00206 0.00208 1/T (K)

Mod. Logistic model Mod. Gompertz model

Figure 4.4. Arrhenius plot of Furfuryl using Modified Gompertz and Logistic model

Table 4.2. Activation coefficient of VOC’ using modified Gompertz and Logistic models

Ea Ea (kJ/mol) (kJ/mol) Volatile Mod. Mod. R2 R2 Compounds Gompertz Logistic model model Furfuryl mercaptan 174.5 0.98 174.7 0.99 Furaneol+Sotolon 266.64 0.96 264.57 0.96 Homofuraneol 223.47 0.99 222.18 0.99 Dimethyl Pyrazine 356.67 0.99 361.02 0.95 Acetic acid 158.77 0.97 158.67 0.97 4-Vinyl Guaicol 334.09 0.99 337.81 0.97 Vanillin 309.68 0.99 309.39 0.96

4.3.4 Comparison of dynamics of Volatile compounds

The release kinetics of each volatile compound was unique. Different Time-Temperature combination of roasting led to different composition of volatile compounds release in the roaster. Based on the kinetic parameters estimated for each volatile compound, the concentration of acetic acid dominated the volatile release in the roaster. One possible explanation for this is the higher volatility of acetic acid. For most volatile compounds, significant increase in the rate constant and peak concentration was observed at 230 °C indicating that formation of these compounds was favored at higher temperature.

However, extending the roasting time for longer period at higher temperature (230 °C) led to the decrease in the concentration of the volatile compounds which is evident from

73 the curvature observed in the cumulative concentration curve at 230 °C (Figure 4.1&

4.2).

Heterocyclic Compounds such as pyrazines (2,3-, 2,5-, and 2,6-dimethylpyrazine),

Furfuryl mercaptan are the roasting products resulting from Maillard reaction. Vinyl

Guaicol is formed from the thermal decomposition of ferulic acid. For most volatile compounds, higher temperature was needed to initiate the reaction evident from the high activation energy coefficients of the volatile compounds. Amongst the volatile compounds analyzed acetic acid had relatively lower activation energy (158 kJ/mol) compared to other volatile compounds. Compounds such as dimethyl pyrazine (2,3-, 2,5-, and 2,6-dimethylpyrazine), vinyl guaicol and vanillin had relatively higher activation energy values indicating very high temperature sensitivity for these volatile compounds.

To create a target roast profile, roasting temperature will play a very significant role in the formation of these high activation compounds specially, dimethyl pyrazine, vanillin.

4.4 Conclusion

Kinetic parameters to describe VOC generation was obtained using modified Gompertz and Logistic models. The kinetic parameters (peak cumulative concentration, rate of volatile generation and lag time) were estimated for the VOC’s from both the models.

The statistical analysis showed that modified Logistic model was better consisted with the experimental data. This was proved by comparing the NRMSE values of the two models and considering the SEE % of the kinetic parameters (modified Logistic model:

NRMSE range 0.004-0.04; SEE % range for cumulative peak concentration 0.001-0.02,

74 modified Gompertz model: NRMSE range 0.001-0.01; SEE % range for cumulative peak concentration 0.004-5.68). The results from the kinetic study indicated a significant increase in the volatile generation rate and peak cumulative concentration with increase in temperature from 210-230 °C for all volatile compounds. The activation energy coefficient for the volatiles calculated using modified Gompertz and Logistic was within the range of 155 to 365 kJ/mol for both the models. The range of activation energy coefficients for volatile compounds indicates high temperature sensitivity. Different roasting conditions (temperature) led to different volatile compound composition in the roaster gas stream. This demonstrates the scope for creating a wide range of flavor profiles in a cup of coffee by varying the time-temperature of roasting. The information on temperature sensitivity of volatile compound is vital to optimize the roasting temperature-time conditions to obtain a target roast profile.

References

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Chapter 5: Effect of temperature distribution within the coffee bean on the rate of VOC generation during coffee roasting

Abstract

In this study, an analysis of the heat transfer to evaluate the coffee bean’s temperature during the roasting process was performed. A numerical model based on transient heat conduction and on time-dependent properties was applied to describe the temperature distribution history within the coffee bean. The model was referenced specifically for temperatures within a horizontal drum roaster with forced convection due to an inlet air velocity of 1.2 m/s. Columbian Arabica coffee was roasted at three process temperature

(210 °C, 220 °C & 230 °C) for 20 minutes. The temperature of the roasting chamber was measured online using T-type thermocouple. The convective heat transfer coefficient for air at the surface of a coffee bean was predicted using Ranz-Marshall correlation. Biot number was calculated iteratively during the roasting process to account for changes in the characteristic dimension due to bean expansion and changes in thermal conductivity due to the increase in the bean porosity during the roasting process. A transient heat conduction model was used to predict the temperature distribution within the coffee bean.

Sfer model was integrated with the kinetic parameters to predict the generation of volatile compounds during the roasting process using parameters from the modified Logistic model and compared to the experimental VOC concentration. The results indicated that the predicted VOC concentration was in excellent agreement with the experimental 78 values. A correlation between the rate of volatile generation and the mass average temperature within the coffee bean was evident. The simulation can be useful for predicting VOC release for different roaster types and optimizing the roasting conditions.

5.1 Introduction

Roasting is a process in which beans are subjected to high heat treatment. During the roasting process, dry heat is applied to the green beans at high temperature ranging from

200°-260 °C for a certain period. Typically, the roasting process can be characterized in by two important phases. The first endothermic phase during which the water content drops from 8-12% to a few percent. This can be perceived by the popping sound, called the first crack at about 175-180°C (Raemy & Lambelet,1982). The second phase of roasting is accompanied by exothermic phase during which pyrolysis reaction takes place. If the beans are further heated at temperature above 200 °C, second crack can be heard. Majority of chemical changes and flavor development takes place between the first and second crack and consequently the bean swells double its original size. After the second phase of roasting, the beans must be rapidly cooled (using water or air) to stop the reactions and prevent over-roasting which can alter the quality of the product (Raemy &

Lambelet,1982; Schwartzberg, 2002).

79 uniform heat transfer. Fluidized bed roasters force heated air through a screen under the coffee beans with a force sufficient to lift the beans. Heat is transferred to the beans as they tumble and circulate within the fluidized bed. Operating the drum roaster and the fluidizing bed roaster were so-called temperature profile mode, that is, along the identical development of coffee bean temperature over roasting time, the kinetics of aroma generation were similar in both processes (Baggenstoss et al. 2008). In a motionless system (v = 0m/s), heat transfer coefficient reported was around 14 W/m2K. Even with minimum velocity, the heat transfer coefficient increases considerably. The process parameters need to be carefully chosen based on the type of roasting equipment as that will affect the rate of heat transfer to the coffee bean (Pittia and Romani, 2010).

The thermal treatment causes both heat and mass transfer. Heat transfer from the roaster to the beans is primarily by convection and from bean to bean by conduction. As the temperature of the increases, water starts to diffuse to the bean surface. This causes gradients of heat and mass transfer inside the bean (Eggers and Pietsch 2001; Bonnlander and others 2005). The continuous changes in physical parameters like moisture loss, bean swelling and internal cavities causes continuous changes in the heat transfer properties which creates complexities in modelling the heat transfer within the coffee bean. Various research to predict heat transfer properties in coffee during roasting have been performed

(Sivetz and Derousier 1979; Raemy and Lambelet 1982; Nagaraju and others 1997).

Raemy and Lambelet (1982) used heat flow calorimetry technique to determine the specific heat of coffee and chicory products and study their thermal behavior at 30°C.

The specific heat of green Arabica coffee was found to be 1.85 J g -1 °C -1 in beans with

7.5% humidity. The specific heat of roasted coffee bean was found to be 1.46 J g -1 °C -1 at 2.5% humidity.

Schwartzberg developed a semi-physical model to examine the coffee bean temperature and moisture content during the roasting in batch system. The model allows prediction of average temperature of the coffee bean, neglecting the real internal distribution. Hernadez et al. (2007) performed theoretical analysis of the heat and mass transfer to evaluate the coffee bean’s temperature and moisture content during the roasting in batch system using the equation proposed by Schwatzberg (2002) including internal distribution. Hernandez et al. also found that the semi-physical model was effective to predict bean temperature and moisture content during roasting. However, there were some limitations associated with the model. Bean temperature were considered uniform. But due to a large external heat transfer coefficient, a gradient of temperature exists. Fabbri et al. (2011) developed a numerical model using a finite element technique to describe the heat and moisture transfer inside a coffee bean during the roasting process. The model refers to a rotating cylinder roaster in natural convection conditions.

Objective of the Research

The amount of heat transferred to the coffee bean and rate of transfer are the primary factors in the roasting process (Illy and Viani 1995, Schenker et al. 1999). Therefore, numerical model to predict the heat transfer and temperature distribution within the coffee bean can be a useful tool to optimize the roasting process. The aim of this research was to develop a numerical model using a transient heat conduction model to account for physical changes (like change in characteristic dimension due to bean expansion and change in thermal conductivity due to increase in porosity) to describe the temperature distribution history within the coffee bean during the roasting process. In addition, integration of kinetic parameters of VOC generation with temperature distribution history was investigated to predict VOC concentration as a function of roasting conditions.

5.2 Materials and Methods

The green beans were weighed (100g/batch), fed into a benchtop horizontal drum roaster

(Model-CRB 101 Gene Café, Roast masters, USA) and roasted at 210, 220 and 230 °C for 20 minutes respectively leading to a range of roasted bean from very light to very dark. Roasting experiments were carried out in three replicates. The inside chamber temperature roaster was measured online using T-type thermocouple (Omega

Engineering) and recorded using a data logger at the rate of 5 seconds. The inlet air velocity of the roaster was measured by anemometer (Amprobe TMA40-A).

5.2.1 Moisture Analysis

The moisture content of green bean and roasted beans were measured using moisture analyzer (MJ33, Mettler-ToledoInc, Greinfensee, Switzerland) to determine the moisture loss during the roasting process. Coffee beans were ground to fine-medium particle size using burr type coffee grinder (Cuisinart 8-oz brushed stainless burr coffee grinder) and heated in the moisture analyzer instrument at 120 °C in auto mode. The instrument performs thermogravimetric analysis based on loss of mass at a constant temperature for a given time.

5.2.2 Thermo-Physical Properties of Coffee Bean

The composition of coffee bean is presented in Table 5.1 (Adriana Farah 2012)

Table 5.1. Composition of Coffee bean

Component Percentage composition (%) Carbohydrate 52 Protein 18 Fat 16.5 Water 9 Ash 4.5

The basic expression for prediction of thermos-physical property from food composition was proposed by Choi and Okos (1986).

The density, specific heat and thermal conductivity of the coffee bean during roasting was from calculated from equation (1), (2) and (3) respectively and the linear regression model for each component proposed by Phinney et al. (2017). The thermal diffusivity was calculated from equation (4).

ρ = 1/ Σ (mi /ρi) (1)

Cp = Σ mi Cpi (2)

kb = Σ Yi ki (3)

α = k/(ρ. Cp) (4)

5.2.3 Governing equations

5.2.3.1 Heat Transfer Coefficient

During the roasting process, heat is transferred mainly by convection from air to the bean surface and by conduction from surface to the bean. In the numerical model used in the analysis, radiation phenomenon is neglected and only forced convection is considered.

The rotation of the drum is sufficient to effectively mix the beans and therefore, all the beans are assumed to have same temperature. The convective heat transfer coefficient of the air to coffee bean was estimated using Ranz-Marshall correlation, equation (5) (Perry

& Green, 1998, Hernandez et al. 2007).

h.푑 푐 = 2 + 0.6 ∗ (Re)0.5 ∗ (Pr)0.33 (5) 푘푎

5.2.3.2 Biot Number

Biot Number is the ratio of heat transfer at the surface to the heat transfer within the coffee bean.

Bi = h. dc/k (6)

The internal resistance that an object offers to heat transfer is described using Biot

Number. The value of Biot number determines whether there is internal resistance to heat transfer.

There are two cases that needs to be considered in determining whether lumped parameter model or unsteady state heat transfer models to be used for solving transient heat transfer problems (Becker & Frickle 2004).

1. Low Biot Number (Bi ≤ 0.1): Negligible resistance to heat transfer within the

coffee bean. Therefore, during roasting the temperature distribution within the

coffee bean is uniform.

2. High Biot Number (Bi ≥ 0.1): Finite internal and surface resistance to heat

transfer. Therefore, during roasting temperature gradient exists within the sample.

The geometry of coffee bean was considered spherical with characteristic dimension (dc =

0.007 m). During the roasting process, the characteristic dimensions and thermal conductivity changed due to expansion and an increase in porosity. It must be noted that the Biot number includes both characteristic dimension and thermal conductivity in the and both values changed during roasting, the Biot number was calculated iterative in a step-wise manner throughout the roasting process to account for these physical changes.

To predict the temperature distribution within the coffee bean during roasting, the

following assumptions were made:

1) Radiation phenomenon was neglected and only forced convection was considered.

2) The heat produced by exothermic reactions during roasting was neglected.

3) The characteristic dimension was considered constant during the first 5 minutes of

roasting while temperature of the bean increased.

4) The characteristic dimension increased linearly during 5 to 10 minutes of roasting

due to bean expansion. Beyond 10 minutes, the characteristic dimension was

assumed to be constant.

5) Thermal conductivity and density of the coffee bean decreased linearly between 5

to 20 minutes of roasting due to linear increase in bean porosity (Schenker et al.

1999) and bean volume respectively.

5.2.3.3 Bean Temperature Distribution

For high Biot number (Bi ≥ 0.1), transient heat conduction for unsteady state heat transfer, a heat curve equation (Ball C.O. 1923, Kopelman et al. 1968) was used to predict the temperature distribution within the coffee bean at different roasting temperature.

t Log[푇푎 − 푇푏] = − + Log [j. 푇푎 − 푇푖] (7) 푓ℎ

The center temperature of the bean was predicted using equation (7). During application, measured values of Ta were used. The fh and j values were estimated for corresponding

Biot number for each temperature increment using a graphical method from the thermal properties chart (Pflug et al. 1965) shown in Appendix (N).

The j value changes with location within the bean. To calculate the temperature distribution at different location in the bean, the j values (spherical geometry) were calculated at four different locations within the bean (Bean center, 25% from center, 50% from center, 75% from center, Surface) from equation (8).

j = 0.63662(푑푐⁄r)sin (πr⁄푑푐) (8)

5.2.3.4 Mass Average Temperature

Mass average temperature (Tm) is the average of temperature distribution within the bean.

Mass of each volume element was computed from the volume based on radial distance and density of the coffee bean.

푚 = 휌×푉 (9)

The mass average temperature of the coffee bean was calculated from the mass fraction and temperature for a given location, followed by summation from equation (10)

1 m T = ∫ T. dm (10) m m 0

5.2.3.4 Development of integrated model

1) The predicted mass average temperature and the reference kinetic parameters (kref,

Ea) of the volatile organic compounds (VOC) estimated from the kinetic models

(Modified Gompertz and Logistic model, Chapter 4-Table 4.1, 4.2) were used to calculate the rate constant using time incremental approach based on Arrhenius relationship given in equation (11).

2) The computation of each increment was accomplished using the appropriate rate constant for the bean temperature at that time during the process. The temperature for a given increment was calculated as the mean of the temperature at the beginning and end of the increment.

3) The rate constant was in turn was used to predict the cumulative concentration increase of VOC at different roasting temperatures using modified logistic model (12) and compared to the respective experimental VOC concentration. The Normalized Root

Mean Standard Error (NRMSE) was calculated for predicting the goodness of fit of the model with the experimental data.

1 1 Arrhenius Relationship k = kref×exp (−퐸푎/R)( − ) (11) 푇푟푒푓 푇푚(푎푣푔)

k Modified Logistic model C = ( C∞)/{1 + exp [(4 ∗ ) (tl − t) + 2]} (12) ((C∞)

5.3 Result and Discussion

5.3.1 Moisture loss during roasting

The initial moisture content of the green bean was measured to be 9.32 ± 0.64%. During the first phase of roasting (drying phase), the moisture content of the bean decreased continuously. The experimental moisture data of coffee beans during roasting at three- process temperature (210, 220 and 230 °C) is shown in Figure 5.1.

Moisture curve 10

(%) 8

Moisture Content Moisture 2

0 0 5 10 15 20 25 210 Time (min) 220 230

Figure 5.1. Experimental moisture drying curve during roasting at 210, 220, 230 °C

Raemy and Lambert (1982) described the initial roasting process to be endothermic but at around 175 °C, the reaction becomes exothermic. In addition, it is anticipated that once the bean reaches about 6% moisture content, the actual roasting process begins and the overall process becomes exothermic during which there is an exponential increase in the generation of VOC’s. From the mass-average temperature curves (Figure 5.9) and

89 moisture curves (Figure 5.1), it is evident that the bean temperature reached 175 °C and the moisture content decreased to 6% at about 5 minutes of roasting. As is evident an exponential increase in the concentration of the volatile compound occurs immediately after 5 minutes of roasting (Figure 5.2 & 5.3). These observations demonstrate the role of temperature distribution within the bean on the concentration of VOC generation during roasting.

Furfuryl mercaptan (210 ℃)-Temperature curve

1.6 240

200 1.2 160

0.8 120

0.4 ℃ Temperature

40 Concentration (ppm) Concentration

0 0 0 5 10 15 20 25 Time (min) Time to reach 175 ℃

Figure 5. 2. Mass average temperature and release of Furfuryl mercaptan during roasting at 210 °C. (Exponential increase in the VOC concentration was observed once the bean temperature reached 175 °C)

Furfuryl mercaptan (210 ℃) - Moisture curve 1.6 10

8 1.2

6 0.8 4

0.4

2 Moisture content (%) content Moisture

Concentration (ppm) Concentration 0 0 0 5 10 15 20 25 Time to reach 6% moisture Time (min)

Figure 5.3. Moisture curve and evolution of Furfuryl mercaptan during roasting at 210 °C. (Exothermic increase in VOC concentration was observed once the bean moisture decreased to around 6%)

5.3.2 Thermo-physical Properties

The thermo-physical properties were estimated from linear regression model proposed by

(Phinney et al. 2017). The predicted density, specific and thermal conductivity of coffee bean during roasting at 220 °C is shown in Figure 5.4, 5.5 & 5.6 respectively. The density of the coffee bean decreased continuously with increase with increase in roasting temperature. The thermal conductivity of the coffee bean gradually increased during the first 5 minutes of roasting as the roaster temperature increased. But as the roasting progresses, the porosity of the coffee bean increases. Schenker et al. (1999) reported a linear increase the pore volume of the coffee bean during the roasting process.

Consequently, the thermal conductivity of the coffee bean decreased linearly during 5 to

20 minutes of roasting (Figure 5.6). Fabbri et al. (2011) measured the thermal conductivity of the coffee bean during roasting at 200 ℃ and they also reported a 91 decrease in thermal conductivity with increase in roasting time. The specific heat of the coffee bean increased gradually with increase in roasting temperature.

Density 250 1200

200 )

900 3 - 150 600 100 300

50 (kgm Density Temperature ℃ Temperature 0 0 0 5 10 15 20 25 Time (min) Roaster Air Temperature Density

Figure 5.4. Density of the coffee bean during roasting at 220 °C

Specific Heat

250 2.4

)) 1

200 2.35 -

℃ 1 2.3 - 150 2.25 100 2.2

50 2.15 Temperature ℃ Temperature

0 2.1 Specific Heat (kJkg Heat Specific 0 5 10 15 20 25 Time (min) Roaster Air Temperature Specific Heat

Figure 5.5. Specific heat capacity of the coffee bean during roasting at 220 °C

Thermal Conductivity 250 0.35 0.3 200 0.25 150 0.2 100 0.15 0.1

Temperature ℃ Temperature 50 0.05 0 0

0 5 10 15 20 Thermal Conductivity (W/m℃) Conductivity Thermal Time (min) Thermal Conductivity Roaster Air Temperature

Figure 5.6. Thermal Conductivity of the coffee bean during roasting at 220 °C

5.3.3 Bean Center Temperature

The convective heat transfer coefficient calculated from Ranz-Marshall correlation ranged between 46-50 Wm-°C -1. The Biot number value ranged from 1.2 to 2.3 during the roasting process. Since, Biot number was greater than 0.1, finite resistance to heat transfer was assumed within the coffee bean and the bean’s temperature distribution was predicted from transient heat conduction model. The bean center temperature obtained by simulation from the model and the air temperature (°C) measured on-line for 20 minutes at 220 °C are reported in Figure 5.7. These curves show the effect of air temperature on the increase in bean’s temperature. An exponential increase in the bean temperature was observed. Schwartzberg (2002) also described exponential behavior of the internal bean temperature below 250 °C. The bean center temperature reached the air temperature at about 7 minutes of roasting (Figure 5.7).

220 ℃ 250

200

150

100

50 Temperature (℃) Temperature

0 0 5 10 15 20 25 Time (min)

Figure 5.7. Evolution of bean’s center temperature during roasting at 220 °C

5.3.4 Temperature distributions within coffee bean and mass average temperature

Based on the j (lag constant) values calculated from Eq (8), the temperature distribution at different locations within the bean were predicted. Figure 5.8 shows the temperature distributions at different bean locations at roasting temperatures 220 °C. The temperature distributions at different bean locations at roasting temperatures of 210 ℃ and 230 °C is given in Appendix (L). The bean surface heated up faster compared to the center and other locations. The temperature at all locations became uniform at around 7 minutes of roasting. The mass average temperatures of the coffee bean at the three roast temperatures is shown in Figure 5.9. Since the model predicted the mass average temperature in response to the temperature changes in the roaster, a curvature was observed in the temperature curves.

220 ℃ 250

200

150

100

Temperature (℃) Temperature 50

0 0 5 10 15 20 25

Time (min)

Figure 5.8. Evolution of bean’s temperature at different locations during roasting at 220 °C

Mass Average Temperature 250

200

C) ° 150

100 Temperature( Temperature( 50

0 0 5 10 15 20 25 210 °C Time (min) 220 °C 230 °C

Figure 5.9. Mass average temperature of the coffee bean during roasting at 210, 220 and 230 °C

5.3.5 Integration of VOC kinetics with temperature distribution

An important outcome from this study was the development of integrated model by combining the kinetic parameters of volatile compounds estimated from modified

Logistic model with the temperature distribution within the coffee bean. During the roasting process the incremental increase in temperature as a function of time, was integrated with the rate constants during the roasting process from equation (11) using the reference kinetic parameters (kref, Ea) for volatile compounds (Chapter 4-Table 4.1& 4.2) and the mass average temperature of the bean predicted from the transient conduction model. The model was then used to predict the increase in cumulative concentration of volatile compounds during the roasting process using modified Logistic model. Figure

5.10 & 5.11 illustrates the experimental and predicted cumulative concentration of

Furfuryl mercaptan and DMP using kinetic parameters from the modified Logistic model during roasting at 210 °C and 230 °C respectively. The predicted VOC cumulative concentration and the experimental values were in good agreement based on the NRMSE range 0.08-0.1. Based on these observations, the transient conduction model can be used to predict the temperature distribution within the bean during the roasting process. These temperature distributions can be integrated with kinetics parameters to predict the volatile generation during the roasting process.

Furfuryl mercaptan 800

600

400

200

Cumulative Concentration (ppm) Concentration Cumulative 0 0 5 10 15 20 25 Time (min) Experimental Predicted

Figure 5.10. Experimental Vs Predicted cumulative concentration of Furfuryl mercaptan from integrated model during roasting at 220 °C. (NRMSE 0.08) Experimental ( ), Predicted ( )

Dimethyl Pyrazine 600

500

400

300

200

100

Cumulative Concentration (ppm) Concentration Cumulative 0 0 5 10 15 20 25 Time (min) Experimental Predicted

Figure 5.11. Experimental Vs Predicted cumulative concentration of Dimethyl Pyrazine from integrated model during roasting at 230 °C. (NRMSE 0.1) Experimental ( ), Predicted ( )

5.4 Conclusion

A transient heat conduction model has been used to predict the temperature distribution history within the coffee bean during the roasting process. Several factors like, porosity, density, thermal conductivity have been incorporated into the model for temperature prediction. The mass average temperature provides roasting times required for release of volatile compounds.

A model for prediction of volatile compounds generated during roasting based on mass average temperature integrated with kinetic parameters of volatile compound has been proposed. The prediction of volatile compound concentrations demonstrated excellent agreement with experimental results. The proposed model can be used to predict the coffee bean temperature for different types of roasters when the Biot number are greater than 0.1 by measuring or estimating the appropriate heat transfer coefficient of the roaster. The simulation clearly demonstrates the role of temperature distribution within the bean on the kinetics of volatile generation during the roasting process. The integration of the volatile generation kinetics with heat transfer provides a better visualization of the contribution of temperature distribution on volatile generation during the roasting.

Nomenclature mi mass fraction of component (kg)

-3 ρi density of component (kgm )

-1 -1 Cpi specific heat capacity of component (kJ kg °C )

-3 Yi volume fraction of component (m )

-1 -1 ki thermal conductivity of component (Wm °C )

α thermal diffusivity (m2s-1)

-1 -1 ka thermal conductivity of air (Wm °C )

-1 -1 kb thermal conductivity of the coffee bean (Wm °C )

-1 -1 Cp specific heat capacity of the coffee bean (kJ kg °C ) h convective heat transfer coefficient at the air-bean surface (Wm-2°C -1) dc characteristic dimension of coffee bean (m) r radial distance from the bean center (m)

Re Reynolds number

Pr Prandtl number

Ta ambient temperature of the roaster (°C),

Tb bean temperature (°C),

Ti initial temperature (°C), j Lag constant

-1 fh heating rate constant (min )

V volume based on radial distance from the center (m3)

T temperature at each volume element within the bean (°C)

Tm mass average temperature (°C) m total mass for a given volume (kg)

-1 k ref reference rate constant of VOC (min )

-1 Ea activation energy of the VOC (Jmol )

Tref process temperature of the roaster (°K)

Tm(avg) mean of mass average temperature at the beginning and end of increment (°K)

C∞ peak cumulative concentration (ppm) of VOC release; ‘

tl duration of the lag phase (min).

100

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102

Chapter 6: Conclusions and Future Directions

To deliver a consistent coffee aroma profile, it is important to have precise control of the coffee roasting process. Since series of complex reactions takes place during roasting, online measurements are the most efficient way to gain insight into the kinetics of coffee flavor generation. On-line measurement of volatile compounds using SIFT-MS accompanied with off-line analysis has been successfully demonstrated. Different roasting conditions (temperature) led to different volatile compound concentration in the roaster gas stream and the coffee extract. Extending roasting times and/or temperatures led to a decrease in the concentration of volatile compounds. The synergy between roaster and extract trend has been confirmed using Gaussian function by comparing the time to reach the peak concentration, thereby providing the opportunity for online process control and automation of coffee roasting process.

The influence of time-temperature condition of roasting on the composition of volatile compounds has been demonstrated using kinetic models. Kinetic parameters were obtained using modified Gompertz and Logistic models to describe VOC generation during the roasting process. The modified Logistic model provided better statistical agreement with the experimental cumulative concentration of VOC’s based on low

NRMSE and SEE % of the kinetic parameters (modified Logistic model: NRMSE range

0.004-0.04; SEE % range for cumulative peak concentration 0.001-0.02, modified 103

Gompertz model: NRMSE range 0.001-0.01; SEE % range for cumulative peak concentration 0.004-5.68). The results from the kinetic study indicated a significant increase in the volatile generation rate and peak cumulative concentration with increase in temperature from 210-230 °C for all volatile compounds. The range of activation energy coefficients indicates high temperature sensitivity for the formation of VOC ‘s during roasting.

A transient heat conduction model has been used to predict the temperature distribution history within the coffee bean during the roasting process. A unique feature of the study was several factors like, porosity, density, thermal conductivity were incorporated into the model for temperature prediction.

Future Directions

Future research could be performed using SIFT-MS with different origin of coffee beans to gain insights on how the volatile generation dynamics would be altered depending on the type of the beans. 104

The simulation presented in this study by integrating the kinetics parameters of the volatile compounds with the bean temperature distribution could be extended for different process parameters like roasting temperature, types of roasting equipment’s etc.

The on-line analysis and kinetics models allowed detailed understanding of the roasting process, however sensory studies should be performed to establish the correlation between the volatile generation dynamics predicted from the kinetics models and the actual flavor perception which can be used as a robust quality monitoring tool in industry.

105

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