Application of Untargeted Flavoromic Analysis to Characterize Chemical

Home , Canned coffee, Coffee bean storage, Coffee cup, Coffee cupping, Keurig, Technivorm

Application of Untargeted Flavoromic Analysis to Characterize

Chemical Drivers of Coffee Quality

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

Sichaya Sittipod, M.S.

Graduate Program in Food Science and Technology

The Ohio State University

2019

Dissertation Committee

Devin G. Peterson, Advisor

Luis E. Rodriguez-Saona

Christopher T. Simons

Emmanouil Chatzakis

Copyrighted by

Sichaya Sittipod

2019

Abstract

Increasing global demand for premium coffee beverages has challenged coffee producers to provide higher quality products. The overall aim of this project was to characterize the chemical composition of coffee brews that contribute to different flavor quality and ultimately to provide a chemical basis for the optimization of coffee production.

During the initial phase of this study, current industrial methods for green bean selection were evaluated in regard to their ability to predict coffee brew sensory properties. Physico- chemical changes observed in the green coffee beans stored under different conditions were not indicative of coffee brew flavor changes in this study. Overall, these results supported the need to develop new methods for coffee quality prediction. In the second phase of this project, an untargeted Liquid Chromatography/Mass Spectrometry (LC/MS) Flavoromics approach was used to define chemical markers predictive of coffee brew quality. Eighteen coffees ranging in qualities from below specialty to excellent specialty quality, were chemically profiled and modeled against the official Specialty Coffee Association cup score (obtained by Q-graders) using Orthogonal Partial Least Square (OPLS). A successful model was generated and showed high prediction ability (R2Y=0.99, Q2=0.97). A total of

10 highly predictive compounds were selected, of which six were positively and four were negatively correlated to cup score. Their sensory impact was evaluated using a recombination study performed by five Q-graders. Addition of the compounds individually

ii and in combination to a coffee brew base caused significant changes in cup scores. Three compounds predictive of high cup score: 3-O-caffeoyl-1-O-3-methylbutanoylquinic acid

(m/z 437), 3-O-caffeoyl-1-O-3-methylbutanoyl-1, 5-quinide (m/z 419) and a chlorogenic acid derivative (m/z 671) significantly increased the quality of a below specialty grade coffee brew to excellent specialty grade. Four compounds predictive of low cup score: ent-

16α,17-dihydroxy-kauran-19-diglucoside (m/z 659), its aglycone form ent-16α,17- dihydroxy-kauran-19-oic acid (m/z 335), unknown compounds m/z 351 and m/z 9.48_333 significantly decreased the coffee quality score of a high cup score coffee. All the identified compounds had no or slight flavor activity when evaluated in water at the levels present in coffee. Yet they were able to significantly modulate the flavor of coffee, likely through the existence of perceptive interactions. The presence of these seven markers of coffee brew quality in the green bean was further investigated. Markers 3-O-caffeoyl-1-O-3- methylbutanoylquinic acid and ent-16α,17-dihydroxy-kauran-19-diglucoside were both detected in green coffee beans at levels correlated to the coffee brew cup scores. Overall, this research showed a successful application of a newer flavor analysis method,

Flavoromics, that allowed for the discovery of seven flavor compounds predictive of the coffee cup quality. These compounds can be used as markers of quality for raw material selection and process optimization to achieve desirable coffee brew flavor.

iii Dedication

To my mother and sister who have sacrificed so much for my education and growth

iv Acknowledgments

First, I would like to express sincere gratitude towards my advisor Dr. Devin G.

Peterson for giving me the opportunity to work under his mentorship at FREC. He always believed in me and helped guide me, as I struggled to overcome various challenges during these past 4 years. Thank you for pushing me to become my best. These valuable experiences and lessons will stay with me as I continue on to my professional career and life. I would also like to thank all of my committee members, Dr. Rodriguez, Dr. Simons,

Dr. Hatzakis, and Dr. Alonso for their inputs and suggestions these 2 years at OSU.

I owe so much gratitude towards Dr. Laurianne Paravisini for her mentorship and friendship over the years. I will forever be thankful for her kindness and all the things she has taught me. It has been a crazy, long journey and she was there supporting me every single step of the way. I would like to acknowledge Dr. Eric Schwartz, Dr. Sagar

Desphande, and Dr. Smaro Kokkinidou for their advice and help towards my research.

Special thanks to Julie Peterson for her guidance, and her kind, encouraging words. I would also like to thank Laurie Serianni and the coffee team at Keurig Green Mountain Inc. for their valuable assistance with sample sourcing and sensory evaluation for this research.

Many thanks to my current lab mates at OSU and former lab mates and staff in

Minnesota, whom have each supported me in their own special ways. Thanks for the good times that makes up for all those the long days and nights at the lab.

v Lastly, I would like to thank the very important people in my life. I would like to thank my family and friends, here and a far, whom have sent their love and cheered me on throughout my pursuit for a PhD. And very special thanks to Kevin Wong, for being so kind and understanding through the difficult times and always being there for me.

vi Vita

2006- 2010………………………B.S. Food Science and Technology Kasetsart University, Thailand 2012-2014………………………M.S. Grain Science and Industry, Kansas State University, USA 2014-2016………………………Graduate Research Associate, Flavor Research and Education Center, Department of Food Science and Nutrition, University of Minnesota, USA

Publications

Sittipod, Sichaya and Shi, Yong-Cheng. Changes of starch during parboiling of rice kernel. Journal of Cereal Science. 2016. 69, 238-244.

Sittipod, Sichaya and Shi, Yong-Cheng. Changes in physicochemical properties of rice starch during steeping in the parboiling process. Journal of Cereal Science. 2016. 69, 398-405.

Bian, Qi, Sittipod, Sichaya, Garg, Anubha, Ambrose, Kingsly. Bulk flow properties of hard and soft wheat flours. Journal of Cereal Science. 2015. 63, 88-94.

Fields of Study

Major Field: Food Science and Technology

vii

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments ...... v

Vita ...... vii

List of Tables ...... xi

List of Figures ...... xii

Chapter 1. Introduction ...... 1

Research objectives ...... 2

Chapter 2. Literature Review ...... 4

1. Coffee ...... 5

1.1 Green coffee bean composition ...... 5

1.2 Coffee flavor generation ...... 8

1.3 Coffee brew flavor ...... 9

2. Coffee quality ...... 12

2.1 Physical evaluation ...... 13

2.2 Sensory evaluation ...... 13

2.3 Chemometrics: combine sensory and chemistry ...... 17 viii 3. Factors impacting quality ...... 18

4. Methods to uncover coffee flavor ...... 22

4.1 Targeted flavor analysis ...... 22

4.2 Untargeted analysis (Flavoromics) ...... 24

5. Summary ...... 30

Chapter 3. Investigation of physico-chemical properties of green coffee beans as predictors of sensory properties of coffee brew ...... 31

1. Introduction ...... 33

2. Materials and Methods ...... 35

3. Results ...... 37

4. Discussion ...... 46

5. Conclusion ...... 48

Chapter 4. Identification of chemical markers that positively impact coffee quality ...... 49

1. Introduction ...... 51

2. Materials and Methods ...... 53

3. Results and Discussion ...... 60

4. Conclusion ...... 70

Chapter 5. Characterizing markers of low-quality coffee using an untargeted flavoromic analytical approach ...... 78

1. Introduction ...... 80 ix 2. Materials and Methods ...... 82

3. Results and Discussion ...... 89

4. Conclusions ...... 98

Chapter 6. General discussion and conclusion ...... 105

General discussion ...... 106

Conclusion ...... 111

Bibliography ...... 113

Appendix A. Quality classifications of 18 coffee samples based on the average cupping score from five certified Q-graders ...... 136

x List of Tables

Table 1 Main components of green coffee beans (w/w % dry weight) ...... 6

Table 2 SCA cupping score attributes and descriptions ...... 15

Table 3 Total Score Quality Classification based on SCA classification ...... 16

Table 4 Results of triangle test comparing Robusta coffee brew made from fresh (2 oC) and aged (35 oC) green coffee beans...... 43

Table 5 Results of triangle test comparing Arabica coffee brew made from fresh (2 oC) and aged (35 oC) green coffee beans ...... 44

Table 6 Concentration (mg/L) of select chemical markers in coffee. Different letters indicate significant difference according to Tukey post-hoc multiple comparison

(α=0.05). Multiple reaction monitoring was performed using electrospray ionization negative mode (ESI-)...... 72

Table 7 1H and 13C spectroscopic data of compound m/z 437 (MeOD, 700 MHz) ...... 75

Table 8 Multiple ion monitoring (MRM) parameters of markers of interest and concentration ± standard deviation (mg/L) of each marker in coffee. Values connected by same better are not significantly different according to post hoc Tukey test (α= 0.01).

*Transitions was performed in electrospray ionization positive mode...... 100

xi List of Figures

Figure 1 Major diterpenes in coffee ...... 8

Figure 2 Chlorogenic acid ...... 8

Figure 3 Structures of 3-O-caffeoyl-1,5-quinide (a), and 4-O- caffeoyl-1,5-quinide ...... 11

Figure 4 Diterpene bitter tasting mozambioside ...... 12

Figure 5 General schematic of flavoromics; evaluating chemistry with relevance to sensory perception ...... 25

Figure 6 An example procedure of variable selection using multi-criteria assessment

(MCA)...... 29

Figure 7 Bean roasting profile: temperature (oF) and time of roast (mins) ...... 38

Figure 8 Color coordinate-L (brightness scale) of Robusta green coffee beans after five months at 35 oC. Different letters indicate significant difference according to Tukey post- hoc multiple comparison (α=0.05) ...... 39

Figure 9 Color coordinate-a (red-green) of Robusta green coffee beans after five months at 35 oC. Different letters indicate significant difference according to Tukey post-hoc multiple comparison (α=0.05) ...... 39

Figure 10 Color coordinate-a (red-green) of Arabica green coffee beans over 9 months at

35oC. Different letters indicate significant difference according to Tukey post-hoc multiple comparison (α=0.05)...... 40

xii Figure 11 Color coordinate-b (yellow-blue) of Arabica green coffee beans over 9 months at 35oC. Different letters indicate significant difference according to Tukey post-hoc multiple comparison (α=0.05) ...... 41

Figure 12 Moisture content of green coffee beans stored at (a) 2 oC in glass jars and (b) at ambient temperature in a burlap bag for six months ...... 42

Figure 13 LAB coordinates of a) green coffee beans b) roasted with different moisture content upon roasting. * indicates significant difference (p<0.001)...... 45

Figure 14 (a) Orthogonal partial least square scatter plot of coffee brew chemical fingerprint modeled against cup score and (b) S-plot of chemical features. Each dot represents a chemical feature, red colored dots represent selected features of interest .... 71

Figure 15 Total cup score of control and recombination [control + compound(s)].

* indicate significant difference from control according to Dunnett (p<0.05),

** (p<0.005). Area highlighted in yellow represents the range of cup score for coffee that are Below Specialty quality grade (<80 points). Area in blue represents range of cup score of coffee that are considered Specialty grade (>80 points)...... 73

Figure 16 MS/MS fragmentation of compounds (a) m/z 437, (b) m/z 419, and (c) m/z 671

...... 74

Figure 17 Compound m/z 437 (MeOD, 700 MHz) with designated carbon number (right) and corresponding COSY (blue line) and key HMBC correlation (arrows) (left)...... 76

Figure 18 Relative concentration in green and roasted coffee beans of compound m/z 437.

Relative concentration is calculated as compound peak area/internal standard peak area.

Pearson correlation (r) = of 0.76, p< 0.0001. Eclipse highlight samples with cup flavor defect...... 77

xiii Figure 19 (a) Predictive plot of predicted cup score vs true cup score created from

Orthogonal partial least square model (OPLS) and (b)S-plot. Each dot represents chemical feature and red color dots indicate selected features of interest...... 99

Figure 20 Mean total cup score of control and recombination [control+ compound(s)].

*indicate significant difference from control according to student t-test (α=0.05),

** (α=0.005) ...... 101

Figure 21 MS fragmentation of m/z 659, and its formic acid adduct m/z 705 ...... 102

Figure 22 Relative concentration in green bean (green) and in roasted beans (grey) of compound (a) m/z 659 and (b) m/z 335 ...... 103

Figure 23 Relative concentration in green bean and in roasted beans of compound (a) m/z

335 and (b) m/z 351 ...... 104

xiv Chapter 1. Introduction

Coffee is a beverage enjoyed by people all over the world for its characteristic bold flavors and psychophysical stimulation. It is one of the most valuable commodities traded today (Fridell, 2014). Total coffee exports reached a new record in the 2017/18 year at

121.86 million bags (International Coffee Organization, 2018a). As with many other commodities, coffee was originally bred for yield, focusing on criteria like disease resistance (Herbert van der Vossen, Bertrand, & Charrier, 2015). In recent years however, the demand for “specialty” coffee by consumers has stressed the importance of high yield, high quality coffee. The search for climate resilient, sustainably produced, disease resistant varieties that also produce desirable flavors continues today. In order to accomplish these goals, an accurate and consistent method to objectively determine brew flavor quality is needed.

Over the past few decades, evaluating coffee using targeted methodologies has successfully identified hundreds of chemical compounds responsible for coffee aroma as well as many bitter compounds in coffee. Several compounds identified in the literature have exhibited good correlation with specific sensory descriptors and have been used as chemical markers for particular attributes, however, evaluation of coffee brew based on taste or aroma perception separately is rather limiting. Coffee stimuli should be evaluated

1 together, in their original matrix, where all human senses can integrate to achieve the perception of a food product as a whole (Small & Prescott, 2005).

In terms of coffee flavor quality, there is lack of chemical and sensorial agreement which has created inconsistency and ambiguity to the term quality in literature. Despite many extensive studies, the key to highly desirable or undesirable characteristics of coffee are still not fully understood. Several challenges are due to the complexity of the coffee in the analytical and sensory aspect, to holistically capture the term quality. Many papers lack experimental design that includes comprehensive chemical and sensory evaluation. The causative relationship between coffee chemistry and the quality of coffee has rarely been studied.

This current research aims to characterize compounds causative of the unique flavor of coffee brews from different quality classes by comprehensively evaluating coffee chemistry and sensory evaluation using untargeted flavoromic analysis. The identification of compounds correlated with particular quality classes of coffee can help establish objective criteria to better define the term quality. The ability to differentiate subpar quality to superior quality coffee has large monetary values for all relevant parties. Furthermore, understanding the chemistry behind coffee quality will allow researchers and breeders to create high quality producing coffee varieties, and improve agricultural practices and post- harvest processing to maximize flavor quality.

Research objectives

1. Evaluate current industrial practices for coffee bean quality assessment

2. Apply untargeted flavoromic analysis to understand coffee quality 2 2.1. Select and classify green coffee beans based on the sensory evaluation scores

(cupping scores) of their respective coffee brews

2.2. Profile the comprehensive chemical profile of coffee brews using UPLC/MS-Tof

2.3. Correlate chemical features with coffee brew cupping scores and perform variable

selection using multivariate statistical analysis to identify potential markers of

interest

2.4. Isolate and purify the chemical features of interest via mass guided multi-

dimension preparative HPLC fractionation

2.5. Perform sensory validation using certified Q-graders to validate the sensory impact

of markers selected

2.6. Elucidate the structures of markers with sensory quality relevance

3. Evaluate the presence of coffee brew predictive markers in green coffee beans and

identify the potential source of marker generation

The following chapters present findings regarding the investigation to characterize drivers of coffee flavor quality. Chapter 2 provides an introduction to coffee and summarizes past literature on coffee quality and untargeted flavoromic analysis. Chapter 3 presents the evaluation of current industrial methods to determine green coffee bean quality. Chapters 4 and 5 present an untargeted liquid chromatography mass spectrometry

(LC/MS/Tof) flavoromics approach to uncover markers of coffee quality. Chapter 6 includes a general discussion and conclusion, along with suggested future work for this research.

Chapter 2. Literature Review

Flavor is a multimodal sensation that involves the integration of aroma (olfaction), taste (gustation) and somesthetic stimuli perceived during food consumption. Aroma is usually initiated by volatile compounds that reach the olfactory receptors in the nasal cavity via orthonasal or retronasal routes. Taste is perceived through taste buds on the surface of the tongue and oral cavity and involves interaction of non-volatile tastants with taste receptor cells (Smith & Margolskee, 2006). Somatosensation involves cooling-heating sensations and textural cues such as mouthfeel, body, and astringency (Green & Nachtigal,

2012; Guinard & Mazzucchelli, 1996). It is the integration of all these complex chemical stimuli that lead to the individualized experience for humans during consumption of a food product.

This literature review focuses on the flavors of coffee beverages. The chapter presents a review of coffee flavor research as related to coffee quality. First, a background on coffee, green coffee bean composition, flavor generation during roasting, and composition of coffee brew will be covered. This will be followed by a review of the definitions of the term coffee quality and factors that are known to influence quality. Lastly, methodologies to study coffee flavor will be reviewed.

1. Coffee

Coffee plants are grown in tropical regions of the world. The coffee cherries are harvested upon maturation and processed to remove the pulp and mucilage outer layers.

The raw green coffee beans are then dried down for storage and shipped to importing countries. In processing facilities, the raw green coffee beans are roasted, ground and packaged or prepared into various coffee products for consumers (Clarke & Macrae, 1985;

M. N. Clifford, 1985).

1.1 Green coffee bean composition

Significant amount of work has been carried out to study the composition of green coffee beans which contain all the precursors involved in the formation of the characteristic color and flavor of coffee. Green coffee bean composition includes polysaccharides, lipids, proteins, chlorogenic acids and other minor constituents like minerals (Wei & Tanokura,

2015) (Table 1).

Table 1 Main components of green coffee beans (w/w % dry weight)

(Wei & Tanokura, 2015)

Nearly half the dried weight of the raw bean is composed of non-soluble polysaccharides that together makes up the thick cell wall which gives beans its unique structural integrity and shape (Schenker, Handschin, Frey, Perren, & Escher, 2000). Low molecular weight soluble carbohydrates including mono, di and oligosaccharides which makes up about 5-9% by weight of the bean with sucrose as the primary source of reducing sugar. The conversion of polysaccharides and sugars during roasting contributes significantly to flavor compounds.

The main nitrogenous compounds in coffee including alkaloids: caffeine and trigonelline, free amino acids, peptides and proteins. These compounds are essential for the flavor and color generation of coffee. Proteins and peptides are severely degraded during roasting and are found in small amounts in the coffee brew. Caffeine and trigonelline are important for their physiological properties and bitter character (Ramalakshmi &

Raghavan, 1999).

The major components of the coffee lipid fraction are triglycerides (75%), mainly palmitic, steric, oleic, and linoleic (Speer & Kölling-Speer, 2006). The rest of the fraction contains sterols, sterol esters, diterpenes, wax, pigments, vitamins and hydrocarbons (Speer

& Kölling-Speer, 2006). Diterpenes in coffee are mainly pentacyclic alcohols from the ent- kaurene family, typically present as free and monoesters of long chain aliphatic acids. 16-

O-Methylcafestol, cafestol and kahweol, are the main diterpenes naturally present in the lipid fraction (Kurzrock & Speer, 2001) and their positive and negative physiology properties have been of interest for many years (Bak & Grobbee, 1989) (Figure 1).

Green coffee beans also contain a large amount of chlorogenic acids which are esters of quinic acid and trans-cinnamic acids (Figure 2). Caffeic and ferulic acid are the most common substituted moiety on quinic acid. Chlorogenic acid accounts for approximately 10% of the green coffee bean and present a multitude of regio- and stereoisomeres, making characterization challenging. Currently about 45 derivatives of chlorogenic acids have been identified in Arabica beans (Jaiswal et al. 2012). Chlorogenic acids are largely involved in the development of flavor and brown pigments in coffee.

Figure 1 Major diterpenes in coffee

Figure 2 Chlorogenic acid

1.2 Coffee flavor generation

Green coffee beans lack the desirable, highly aromatic and distinct flavor of coffee.

The raw beans have green, cardboard aroma that is transformed through roasting (Michael

Czerny & Grosch, 2000; Wilhardi Holscher & Steinhart, 1995). Heat treatment of green coffee beans via roasting is responsible for the development of the flavor that is recognized as coffee. Upon application of high heat to raw beans, typically beyond 200oC, the bean expands due to loss of moisture and gas which build up in each cell (Schenker et al., 2000).

The compact green beans become bigger, brittle and dark brown in color. These pores act as “mini-reactors” for the chemical reactions occurring simultaneously with the physical changes (Clarke & Macrae, 1985).

The impact of roasting temperature and time on the coffee brew flavor profile has been a popular topic for several decades. Roasting is a time and temperature dependent process (Baggenstoss, Poisson, Kaegi, Perren, & Escher, 2008a; Buffo & Cardelli-Freire,

2004). Roasting conditions must be optimized for each particular bean batch to obtain the highest flavor potential. Due to the general connection between roasting conditions and flavor profile, many methods have been proposed to determine the optimum degree of roast for a desirable flavor profile (Moon & Shibamoto, 2009).

Complex chemical reactions such as polymerization, caramelization, and Maillard reactions take place during the course of roasting (Buffo & Cardelli‐Freire, 2004). Key reactions generating compounds with flavor relevance include the Maillard reaction between reducing sugars and amino acids, Strecker degradation, and the degradation of sugars, trigonelline, and chlorogenic acids (M. N. Clifford, 1985; Dart & Nursten, 1985).

Under these reactions, green coffee bean components are transformed into coffee flavor compounds and the large molecular weight brown pigment, melanoidin (Clarke & Macrae,

1985). Thousands of aroma and taste compounds are generated such as aldehydes, pyridines, pyrazines, furans, and pyrroles and may further act as intermediates to generate other classes of flavor compounds.

1.3 Coffee brew flavor

From a chemical perspective, coffee brew is a complex mixture of organic compounds. Efforts in elucidating all the compounds making up the rich and distinctive flavor of coffee have been ongoing for several decades.

1.3.1 Volatile compounds

About 1000 volatile compounds have been identified in coffee brew.

Approximately 3% of all compounds identified are considered important to the overall aroma of coffee (Mayer, Czerny, & Grosch, 2000; Peter Semmelroch & Grosch, 1995,

1996). The groups of key aroma compounds identified through gas chromatography- olfactory (GC-O) are reported to include roasted, meaty thiols; boiled potato-like sulfides; sweet, buttery aldehydes; nutty, roasted, earthy pyrazines; fruity esters; spicy, burnt phenols, and sweet, caramel-like furanones (Kerler et al.; Blank et al. 1992; Grosch 1993;

Semmelroch et al. 1995; Czerny et al. 1999; and and Grosch 2000; Mayer et al. 2000b;

Schenker et al. 2000).

1.3.2 Taste compounds

Taste compounds in coffee have been far less studied. Aliphatic acids such as citric and malic are responsible for the sour taste in coffee beverages (Ginz, Balzer, Bradbury, &

Maier, 2000). Chlorogenic acids have been attributed to the astringency of coffee (M N

Clifford & Ohiokpehai, 1983). Caffeine and trigonelline account for up to 30% of the total bitterness intensity in coffee (Chen, W. C. 2007). Several other thermally generated compounds associated with coffee bitterness are reported in the literature. Chlorogenic acids are involved in the generation of chlorogenic derived bitter compounds which include bitter chlorogenic acid lactones such as 5-O-caffeoyl-muco-γ- quinide , 3-O-caffeoyl-γ- quinide, 4-O-caffeoyl-muco-γ- quinide, 5-O-caffeoyl-epi-δ-quinide , and 4-O-caffeoyl- γ- quinidea as well as the quinides of O-feruloylquinic acid, and O-dicaffeoylqunic acids generated by epimerization and lactonization reactions of different derivatives of chlorogenic acids (Frank et al., 2006a) (Figure 3).

a) b)

Figure 3 Structures of 3-O-caffeoyl-1,5-quinide (a), and 4-O- caffeoyl-1,5-quinide

Proline-based diketopiperazines (DKPs) have been identified in roasted coffees

(Ginz & Engelhardt, 2000) and are associated with bitterness and astringency in thermally treated foods such as beer (Gautschi et al., 1997) and cocoa (Pickenhagen et al., 1975). In coffee, 4-Vinylcatechol oligomers such as 1,3-bis(3′, 4′-dihydroxyphenyl)butane, trans-

1,3-bis(3′, 4′-dihydroxyphenyl)-1-butene, and hydroxylated phenylindanes with thresholds ranging from 23 to 178 μmol/L have been associated with long-lasting and harsh bitter taste (Frank, Blumberg, Kunert, Zehentbauer, & Hofmann, 2007). More recently, (furan-

2-yl) methylated benzene diols and triols have been reported as a new bitter taste compound class in coffee (Kreppenhofer, Frank, & Hofmann, 2011). Mozambioside, a furokurane glucoside, has been identified in Arabica coffee and reported as bitter tasting as well

(Figure 4). This compound was suggested to be a marker for Arabica coffee as only trace amounts were detected in Robusta beans (Lang, Klade, Beusch, Dunkel, & Hofmann,

2015). In caffeine-free coffee, mascaroside, a diterpene glycoside, is present and also provides bitterness (Ducruix, Pascard-Billy, Hamonniere, & Poisson, 1975).

Figure 4 Diterpene bitter tasting mozambioside

1.3.3 Interactions between coffee volatiles and non-volatiles

Many desirable roasted aromas are lost as soon as coffee is cooled down after brewing. Sensory attributes such as loss of “freshness” and “staling” have been attributed to the loss of low boiling potent volatiles such as methanethiol (Wilhardi Holscher &

Steinhart, 1992). Large molecular weight coffee melanoidins (> 3000 Da) can trap odor active thiols (2-furfurylthiol), usually perceived as sulfury or roasty, by covalent binding and reduce thiol concentration in coffee brew upon cooling time (Hofmann & Schieberle,

2002). The higher the melanoidin content in the matrix, the higher the interaction. In addition to melanoidins, polyphenol degradation compounds also have the ability to bind with thiol volatile compounds. Protein, on the other hand, played a minor role in volatile compound binding and large polysaccharides appeared to not interact with volatile compounds (Charles-Bernard, Kraehenbuehl, Rytz, & Roberts, 2005).

2. Coffee quality

As the demand for coffee continues to grow, determining chemical markers and sensory attributes related to quality has high economical values (Mintel, 2018). The

sensory quality of coffee is directly related to the chemical composition of the raw green bean and the processing conditions from farm to cup (Sunarharum et al. 2014). This section summarizes how coffee quality is currently determined and the influences of species/cultivars, growing conditions, processing, roasting and storage on coffee quality.

2.1 Physical evaluation

Green coffee beans can be classified based on their physical properties. There is a large diversity in the grading and classification system used around the world. Some criteria may include green bean growing altitude, region, botanical variety, moisture content, processing method (wet vs dry), bean size, bean shape and color, and defects. The grading system around the world is constantly evolving with heightened requirements from green bean buyers (International Coffee Organization, 2018b). In the United States, the most commonly used grading systems are the Specialty Coffee Association of American

(SCAA) grading green coffee method, the Brazil/ New York method and commercial grade coffee system. The SCAA method grades beans based on visual defects and cup quality

(section 2.2) while the latter grading systems rely primarily on visual defects and foreign objects contamination (FAO, 2000).

2.2 Sensory evaluation

The method utilized for coffee brew evaluation is dependent on the research objectives. In general, the evaluation type can be categorized into 3 main categories; 1) descriptive analysis 2) liking or preference and 3) flavor quality of coffee brew. Descriptive analysis (DA) is a method to evaluate differences in coffee based on specific attributes ex. roasted or floral. After a set of attributes are regarded as important, they are then rated on

an intensity scale (Civille, Carr, & Meilgaard, 2015). DA panels are highly trained to identify specific attributes in a product or group of products within the same category, in a precise manner (Civille et al., 2015). World Coffee Research (WCR) and the Sensory

Analysis Center at Kansas State University developed a coffee lexicon with expanded coffee flavor descriptors by utilizing a DA panel. Over 110 attributes were included, that can be used for aroma and/or flavor (Chambers et al., 2016). Each attribute has references and intensity ratings.

Secondly, consumer testing can be used to measure liking/preference of coffee brew or coffee products (Narain, Paterson, Piggott, Dhawan, & Reid, 2004). In this method a large consumer group is asked to rate like/dislike a product or specific product characteristics. This method is commonly used by food product developers in the industry to understand, for example, consumer liking of a newly developed flavor or a flavor reformulation to an existing product. Consumer liking has been used to understand coffee product preference of certain consumer groups by ethnicity (Dzung, Dzuan, & Tu, 2003), gender (Cristovam et al., 2000), and by enjoyment in different environment settings

(Bangcuyo et al., 2015).

Lastly, coffee can be evaluated for quality based on “cup score” or evaluation of the coffee beverage. In its traditional sense, coffee cupping has been performed for over

400 hundred years by mainly large coffee roasters, exporters, and importers. It wasn’t until 1984 that the Specialty Coffee Association of America (SCAA), now SCA, published the first edition of the Coffee Cupper’s Handbook Guide to help standardize the protocol for cupping (Lingle & Menon, 2017; Lingle, 1986). The method evaluates quality based on the orthonasal (smell) and retronasal (sip and slurp) evaluation of hot water infusion of ground coffee. This method is composed of ten flavor attributes in

which each attribute is rated on a 10-point scale. These attributes include fragrance/aroma, flavor, acidity, aftertaste, body, overall impression, balance, sweetness, clean cup, and uniformity (SCAA, 2009) (Table 2). The total score is summed out of 100 and points are deducted if flavor defects are found present in the brew. The final score is then used to classify coffee into different grades: specialty or non-specialty (Table. 3).

Evaluators are required to go through intensive training and testing to become licensed evaluators (Q-graders or cuppers), as certified by the Coffee Quality Institute (CQI).

Typically, several Q-graders are utilized by the industry during cupping evaluations to evaluate quality and set price points or processing methods for coffee.

Table 2 SCA cupping score attributes and descriptions Attribute Description Aromatic aspects of dry ground Fragrance/Aroma coffee/ Aromatic aspects of ground coffee when infused with hot water Acidity Brightness and/or sourness of coffee The mouthfeel or heaviness Body perceived on the surface of the tongue Defined as taste and aroma, mid Flavor tone of coffee Sweetness Subtle pleasant sweetness in coffee Transparency in the cup, should be Clean cup free of off flavor and defects Overall rating of coffee, no one Balance parameter should be dominate Consistency of flavor of the Uniformity different cups tasted Duration of positive flavor attributes Aftertaste in coffee Overall Overall rating of coffee

Table 3 Total Score Quality Classification based on SCA classification

Total Score Quality Classification

90-100 Outstanding

85-89.99 Excellent Specialty

80-84.99 Very good

< 80.0 Below Specialty Not Specialty

Pereira et al. (2017) evaluated the SCA Q-grader process. Factors such as the cuppers, the coffee, and their judgement based on interactions with each other, were reviewed. The authors found that there was consistency in scores and notes given by cuppers. Interestingly, significant differences occurred between two testing sessions if cuppers were allowed talk to each other between sets, especially regarding the terms balance, overall, and body. Defects were detected by only a few cuppers and not in all evaluation sessions suggesting that physical-chemical analysis should be used to confirm defects. Another study found consistency in the average total cupping score between three coffee centers evaluating the same coffee, suggesting cupping is reliable for quality grading

(Worku, Duchateau, & Boeckx, 2016).

Di Donfrancesco et al. (2014) showed that there is little overlap between the sets of terms generated using DA and Q-graders. Therefore, these methods should not be used in comparison but rather be complimentary. Since the SCA method can be used to evaluate the coffee flavor holistically and not by attribute, the SCA method is most appropriate to evaluate the term quality while descriptive analysis can be used to understand specific attributes of samples within certain quality classes.

2.3 Chemometrics: combine sensory and chemistry

Researchers have developed ways to predict coffee quality using various methodologies that combine chemical and sensory data using advanced statistics such as multivariate data analysis (Wold & Sjöström, 1998). Principal component analysis (PCA) and partial least squares (PLS) regression have been commonly used for that purpose (Cen

& He, 2007). In the following studies, flavor compounds that exhibited a good correlation with specific sensory descriptors are suggested to be chemical markers of a particular sensory attribute or class of coffee samples.

2.3.1 Predicting undesirable attributes

Analytical methods have been developed to confirm the authenticity of high quality coffee that is contaminated by of other coffee species of cheaper value or other non-coffee impurities such as husk, barely and corn (Correia et al., 2018; de Moura Ribeiro, Boralle,

Redigolo Pezza, Pezza, & Toci, 2017; Jumhawan et al., 2013). Significant efforts have also been made to identify and select markers of roast defects which are typically negatively associated with coffee such as 4-ethyl-2-methoxyphenol for scored defect, or 2,5- dimethylfuran for under developed defect (Yang et al. 2016). Volatile compounds such as

2-methylpyrazine and 2-furylmethanol acetate have been identified as markers for black- immature beans, while benzaldehyde and 2, 3, 5, 6-tetramethylpyrazine were found to be markers of defective beans (Toci & Farah, 2008).

2.3.2 Predicting desirable attributes

Kwon (2015) evaluated green coffee beans based on nuclear magnetic resonance

(NMR) fingerprint and bean quality using the Alliance for coffee excellences (ACE) cup score. The authors found more sucrose, and lower amounts of gamma aminobutyric acid

(GABA), quinic acid, choline, acetic acid and fatty acids in high quality coffee beans.

Successful prediction of some attributes of the Ethiopian quality parameters using green coffee bean Near Infrared spectra (NIR) have also been demonstrated (Tolessa,

Rademaker, De Baets, & Boeckx, 2016). For roasted beans, several authors have aimed to monitor and ultimately manipulate roasting conditions in order to control the formation of certain volatile compounds to obtain preferred flavors (Moon et al. 2009). Real time roast monitoring with multivariate statistics were used to monitor the formation of phenolic coffee flavor compounds including 4-vinylguiacol and guaiacols that provide desirable spicy, smoky notes in coffee (Ralph Dorfner et al. 2003). Roasting degree profiles have also been monitored through on-line proton transfer reaction ToF-MS of volatile organic compounds to achieve desirable characteristic flavors of the brew (Lindinger et al., 2005;

Wieland et al., 2012).

3. Factors impacting quality

3.1 Species

The two most important commercialized coffee species are C. arabica (Arabica) and C. canephora (Robusta) and produce coffee brews that are different chemically and in sensory profiles. Several groups have focused on the influence of genomics on coffee quality (M. N. Clifford, 1985; Leroy et al., 2006). Arabica coffee is more acidic, sweeter, more aromatic and less bitter while Robusta is known to be more bitter and astringent and

is typically associated with inferior quality (Ky et al. 2001; Nebesny and Grazyna Budryn

2006).

3.2 Environment

Soil fertility, temperature, and growing conditions, are known to influence coffee bean composition (Joët et al., 2010). Robusta variety is more disease resistant and grown at lower elevation (Bertrand, Guyot, Anthony, & Lasherme, 2003). Arabica, on the other hand, is more susceptible to disease and is grown on mountains or at high elevation. Shade grown coffees tend to exhibit more positive attributes like acidity, larger bean size and better beverage quality (Vaast, Bertrand, Perriot, Guyot, & Génard, 2006). Elevated plant growing temperatures was shown to induce the accumulation of alcohols such as butan-

1,3-diol and butan-2,3-diol in green coffee beans which resulted in less aromatic quality, less acidity and an increase in earthy and green off- flavors in the brew (Bertrand et al.,

2012).

3.3 Processing method

Coffee processed using different drying techniques have different quality and flavor profiles (Silva et al. 2000; Selmar et al. 2006; Gonzalez-Rios et al. 2007, Dong 2017).

Green coffee beans from wet process produce coffee with better aroma quality due to the generation of classes of compounds during roasting associated with sweet, fruity, and floral notes (Gonzalez-Rios et al. 2007). Undesired microbial fermentation due to incomplete mucilage removal can cause off notes (Iamanaka et al., 2014). Others have shown that differences in coffee brew flavor profiles are caused by different green bean metabolic

processes that are the result of post-harvest treatment applied to green coffee beans

(Kleinwächter & Selmar, 2010; D. Selmar et al., 2006; Dirk Selmar, Bytof, & Knopp,

2008).

3.4 Bean defect

Seed quality and the removal of defective beans such as immature beans, sour beans, black beans, and stinker beans which cause off notes upon roasting are also essential for ensuring cup quality. Defective green beans make up about 15-20% of coffee production (Ramalakshmi, Kubra, & Rao, 2007). Research has shown that raw defective beans have different compositions than healthy beans. Healthy beans have higher amounts of lipids and sucrose while histamine was only detected in defective green coffee

(Mendonça, Franca, Oliveira, & Nunes, 2008; Oliveira, Franca, Glória, & Borges, 2005;

Vasconcelos, Franca, Glória, & Mendonça, 2006). More mature beans favor the development of high quality coffee flavor (A Farah, Monteiro, Calado, Franca, & Trugo,

2006) while immature beans are more prone to oxidative changes to chlorogenic acid

(Montavon, Duruz, Rumo, & Pratz, 2003). Higher levels of chlorogenic acids can lead to undesired astringency and poor beverage quality (M N Clifford & Ohiokpehai, 1983;

Ramalakshmi et al., 2007; Toci & Farah, 2008). Raw defective beans, once roasted, have higher numbers and concentration of total volatile compounds such as pyrazines, pyrroles, and phenols compared to the control. It was suggested that enzymatic reactions due to physiological changes or shock, were associated with the changes in volatile content (Toci

& Farah, 2014), however, no sensory evaluation of the coffee was performed to determine the flavor quality of the resulting brew.

3.5 Microbial activity

Certain fungal species can cause musty and earthy notes in the brew called Rio off- flavors, due to the generation of 2,4,6 trichloroanisole (TCA) (Spadone et al.1990).

Infection of Asperfillus luchuensis was shown to produce 1-oct-en-3-ol, and has been suggested to be the cause of negative attributes in brew compared to non-infected brew

(Iamanaka et al. 2014). “Fermented” attributes are typically undesirable and negatively impact overall quality of coffee, however there is interest in using controlled inoculation of microbes to improve flavor quality (Lee, Cheong, Curran, Yu, & Liu, 2015).

3.6 Storage

After harvesting, green coffee beans may be subjected to storage prior to roasting.

Changes in green bean aroma during storage has been attributed to decreases in brew quality. An increase in woody, rubbery notes was observed in coffee brew after 3 months of uncontrolled storage conditions (Peter Bucheli, Meyer, Pittet, Vuataz, & Viani, 1998).

Damp and warm conditions support microbial growth which resulted in an increased generation of esters, in particular methyl esters of 2 and 3- methylbutanoic acid, which was suggested to influence the aroma of the coffee beverages (Scheidig, Czerny, & Schieberle,

2007). Increase in moisture and temperature during storage can also activate lipase and undesired germination (Patui et al., 2014; Speer & Kölling-Speer, 2006). It can also cause increased oxidative stress and decrease the amounts of natural antioxidant compounds glutathione and ascorbic acid in the beans (Dussert et al., 2006). Increase of oxidative processes and the loss of seed viability can cause changes to bean cellular structure. This results in the liberation of free fatty acids in the green coffee beans. Oxidation of unsaturated free fatty acids can generate volatiles and ultimately cause loss of positive

attributes and generate “stale flavor” or “rested coffee flavor” which is associated with negative coffee quality in the brew (Rendón, De Jesus Garcia Salva, & Bragagnolo, 2014).

The generation of sour taste and negative bitter quality has also been observed during roasted coffee storage (Ross et al. 2006). Improved packaging technology like vacuum packs can slow down the oxidation process. However, flavor changes still become prevalent after 9 months of storage. An increase in “stale” aroma associated with moldy and old coffee was observed along with increase in sour and bitterness (Kreuml,

Majchrzak, Ploederl, & Koenig, 2013).

4. Methods to uncover coffee flavor

The major challenges for understanding the chemical drivers of coffee quality comes down to 3 main aspects: the ambiguity of the sensory term of quality, the complexity of coffee brew chemistry, and the selection of the proper analytical and sensory methods.

The following section addresses two very different approaches to understand coffee quality.

4.1 Targeted flavor analysis

Sensory guided analysis has been used for screening key aroma and taste compounds in foods for decades. For coffee aroma analysis, Gas Chromatography-

Olfactometry (GC-O), a technique which employs humans as detectors, has been one of the key tools to screen for aroma compounds in foods that are odor active. Through a sniffing port, panelists then record the odor they perceive as the compounds elute out the column in real time. GC-O provides valuable information about the sample and the

chemical compounds to focus attention and resources. GC-O studies have greatly contributed to our knowledge of the thousands of different chemical compounds that make up coffee aroma today (Acree & Teranishi, 1993; I. Blank, Sen, & Grosch, 1991; Grosch,

1993, 1994).

Taste compound analysis, on the other hand, has been far less advanced, as aroma was thought to be the primary contributor to flavor for several decades. More recently however, research shows evidence show that taste is also a critical part of understanding flavor perception (Small & Prescott, 2005). Starting in the early 2000s, methods to identify key tastants responsible for specific taste attributes were developed. In a similar concept as

GC-O, taste-guided fractionation uses liquid chromatography (LC) and employs humans as taste detectors, along with chemical detectors such as Ultraviolet (UV) or Mass

Spectrometry (MS). Food samples are deconstructed into smaller portions, removed of solvent and taste evaluation is performed on each portion separately. This method has been shown to be highly effective in determining compounds with specific attributes such as bitterness in whole wheat (Bin & Peterson, 2016), umami in mushroom, and astringency in coca (Rotzoll, Dunkel, & Hofmann, 2005; Stark, Bareuther, & Hofmann, 2005). This method has been used to identify certain taste active compounds responsible for the bitter taste in coffee (Frank et al. 2006).

The term quality is composed of several different sensations triggered by aroma, taste, and somesthetic stimuli at the same time. Therefore, using targeted methodologies would be limiting and would likely not be sufficient to describe the flavor quality of different coffee samples. This, along with the challenges in using non-coffee quality expert to detecting slight flavor change as associated with coffee quality strongly suggests that a sensory activity guided approach is not suitable.

4.2 Untargeted analysis (Flavoromics)

Reineccius (2008) first introduced a comprehensive untargeted approach termed flavoromics, to elucidate chemical compounds that drive sensory properties of a food product by analyzing the all the chemical compounds of a food as a whole, instead of focusing on major compounds known to influence the flavor. The term -omics refers to the interdisciplinary field of research and technologies that analyzes the entirety (or near entirety) of a large family of molecules in a biological system (-omes). This data driven approach, using a top-down strategy, does not rely on previous specific knowledge or hypothesis about a biological system, but instead relies on the data itself. When the large amount of data is analyzed with multivariate statistical analysis, markers of interest can be selected and correlated to the subject of interest, and a hypothesis generated (Fiehn, 2002).

This approach has been applied for early detection of diseases, pharmaceutical, and nutrition for the purpose of discovering biomarkers or the traits of interest.

Flavoromics applies this concept to finding the compounds causative of a sensory perception of food. Considering all low molecular weight compounds, Charve et al. (2011) first demonstrated the use of flavoromics to understand flavor differences in orange juice.

After collecting chemical information on the GC and UHPLC using orange juice of different commercial brands, classification models were obtained which allowed the categorization of the samples by brand and then used to identify potential compounds related to each sample. Using this untargeted approach opens the concept of identifying new flavor contributors that may be missed using targeted methods since more compounds are considered potential candidates.

Further novelty of flavoromics is illustrated by successful determination of compounds causative of ill-defined flavor attributes relating to food storage, or ageing, and

cooked, ferment flavors (Andujar-Ortiz, Peppard, & Reineccius, 2015; I. G. Ronningen &

Peterson, 2018a). A novel flavor modulator of citrus ageing flavors was also recently identified using flavoromics (Ronningen et al. 2018b). This compound would have likely not been discovered using traditional taste-guided fractionation methods as this compound did not have flavor activity on its own. Chemical compounds responsible for attributes such as ageing or cooked may differ from food product to food product, therefore no one compound can be used to define these attributes. Quality is another ill-defined term that is product dependent and is challenging to chemically define. Flavoromics can be utilized to help better understand the complex sensory perception of foods and in this case coffee.

Figure 5 General schematic of flavoromics; evaluating chemistry with relevance to sensory perception

In brief, the general work flow for flavoromics follows the schematic in Figure 5

1. Material selection and sample preparation

Untargeted fingerprinting aims to collect as much chemical information as possible within the food system. Sample preparation and separation and data collection techniques are highly influential to the quality and accuracy in representing a sample (Fiehn, 2002).

Sample preparation should be performed using a quick and simple method in order to prevent loss or changes to the sample, as well as to minimize variance. Coffee, for example, can be prepared as coffee brew to represent the true beverage. Solid phase extraction (SPE) or other methods deemed appropriate, should be used to remove compounds such as melanoidins, lipids, and polysaccharides, along with salts and minerals in coffee, which can interfere with chemical analysis.

2. Comprehensive chemical data collection

High mass accuracy and sensitivity is needed for untargeted, global profiling of samples, and especially for compound identification. Mass spectrometry is a common detection method based on the detection of compounds by their mass/charge ratio which provides some structural information necessary for identification of compounds. Direct infusion of samples into the MS is fast and simple. However, signal suppression can occur, and data may be harder to interpret (Dettmer, Aronov, & Hammock, 2007). Food matrixes such as coffee are complex and contain large molecular weight carbohydrates, lipids, and proteins, therefore, chromatography-MS methods are often used. Separation methods like liquid chromatography (LC) or gas chromatography (GC) adds an additional separation aspect which helps reduce complexity as the compounds are introduced into the detector.

LC examines mainly low and non-volatiles metabolites while GC examines highly volatile metabolites. On the LC/MS platform, column chemistry, column diameter, length, mobile phase, and additives must be considered to optimize resolving power for the particular food of interest.

3. Statistical model generation

Data handling including data transformation and data reduction which is necessary to generate quality data for interpretation (Alonso, Marsal, & JuliÃ, 2015). Chemical data can then be coupled with sensory data to extract out information to help answer research questions of interest using multivariate statistical analysis. Principle component analysis

(PCA) is the most widely used multivariate statistical analysis method in metabolomics

(Bro & Smilde, 2014). It is a linear reduction method of all the variables into a few latent variables used to capture all the variance in the data. PCA provides a first look into the dataset and the relationship or natural clustering between the samples based on chemical data. It may also be used to determine outliners. This type of visualization however, could showcase artifacts, instrumental variations, or sample variation that does not answer the more specific research question of interest. Partial least square (PLSR) and Orthogonal partial least squares (OPLS) methods are tailored towards finding relevant information of interest by finding the relationship between two matrices X, and Y, or chemical data with sensory data. OPLS can analyze data with strongly collinearity (correlated but not relevant to question), noisy, large number of X-variables, and can simultaneously model several response variables (Y) (Trygg & Wold, 2002; Wold, Sjostrom, Eriksson, & Sweden˚˚,

2001). This supervised method allows the generation of models that focuses on the research question of interest when extra prior information about the samples in the form of setting

samples by classes (samples of same treatment or group) or by another set of data with specific response values (Y) is provided, such as sensory information. Different metrices are then used to evaluate model quality. R2X and R2Y explains the goodness of fit and Q2 explains the predictive ability of the model. Values higher than 0.75 are generally deemed as acceptable quality in the literature.

4. Variable selection

Variable selection is performed to determine which variables/ features are most relevant to the sensory related question. Several strategies can be used for variable selection

(Figure 6). Setting the variable of important (VIP) value > 1 maybe used as the first criteria to remove non-significant variables (to research question). However when high multicollinearity is present, setting a general cut off at 1 may not be adequate and using multiple assessment methods should be opted (Chong & Jun, 2005). A second layer of filtering can be to set p(corr) and p[1] value cutoff as determined from the S-plot. The S- plot visualizes the covariance (p[1]) and correlation (p(corr1]) of each variable (features) to the model’s construct (by classes or y-variable(s) (Wiklund et al. 2008). Markers with high correlation and covariance are of interest. Loose criteria may result in higher numbers of features for further examination which may or may not be relevant, while too strict criteria may risk not capturing enough information. ANOVA analysis may also be performed. Testing a set of independent samples (validation test set) should then be used as a final assessment of the selected variable prior to sensory testing (Mehmood, Liland,

Snipen, & Sæbø, 2012).

Non-significant All features features

VIP >1, VIP rank < Features 20, of interest S-plot p(corr[1]), p[1] cut off Most significant Test for sensory ANOVA relevance

Figure 6 An example procedure of variable selection using multi-criteria assessment (MCA).

5. Sensory validation

Collection of sensory evaluation data is as equally important to chemical data and is the core of flavoromics (Charve et al., 2011; I. G. Ronningen & Peterson, 2018a; I.

Ronningen, Miller, Xia, & Peterson, 2018b). In essence, samples with varying sensory properties are investigated for causative chemical drivers of those differences. The type of sensory evaluation utilized will largely depend on the research objective and is performed in parallel with chemical profiling (Figure 4). Upon selection of compounds of interest based on statistical analysis, sensory analysis is utilized again to validate the sensory impact of those compounds. Typically, in the form of recombination models, sensory evaluation is performed on a control sample that is recombined with a compound or set of compounds that is hypothesized to be the cause of certain flavors or sensory qualities.

Compounds that cause significant impact to research questions of interest can then be

further characterized and identified, potentially leading to the discovery of novel compounds.

6. Structural identification of sensory relevant compound

The last step of flavoromics involves the characterization of compounds that cause sensory impact. Often times, LC based compounds in particular, cannot be easily identified due to limited online databases. Other methods for compound elucidation are often needed.

For example nuclear magnetic resonance (NMR) is a structural elucidation technique that provides molecular information about the elements involved, stereochemistry of the compound along with functional groups and their connectivity (Pavia, 2008).

5. Summary

As summarized in this literature review, understanding coffee quality is challenged by the complexity of coffee chemistry, the lack of objective measurements for the sensory term quality, and suitable analytical evaluation methodologies. Despite many extensive studies, the chemistry behind coffee quality is not fully understood. Previous research has often lacked an experimental design which includes comprehensive chemical and sensory evaluation and validation. This literature review demonstrates the need for comprehensive chemical and sensory information to identify the chemical causes of quality differentiation of coffee flavor. The following chapters will demonstrate the application of untargeted

Flavoromic analysis to characterize compounds causative of the unique flavor of coffee brews of different quality classes.

Chapter 3. Investigation of physico-chemical properties of green coffee beans as

predictors of sensory properties of coffee brew

Abstract

Prolonged storage of raw coffee beans is believed by the coffee industry to cause deterioration that subsequently reduces the quality of the resultant coffee beverage. The objective of this work was to assess current industrial practices for green bean quality evaluation by determining chemical changes in green beans stored at various conditions and the impact on sensory properties of the brew. After nine months of storage at 35 oC, significant green color loss was observed in aged green beans, however, no significant changes in brew flavor were detected via sensory evaluation. Storage time, temperature, and packaging type did not induce any sensory change in the brew (α=0.05). Overall, the results suggested that green bean color and moisture content are not reliable predictors for coffee brew quality.

1. Introduction

Coffee brew is a complex mixture of compounds that are responsible for multiple stimuli such as aroma, taste and chemesthetic sensations which altogether contribute to the flavor experience. Bean variety, growing environment, drying conditions, roasting process and brewing methods are a few factors known to influence coffee flavor (Sunarharum et al., 2014). The green coffee beans contain all the precursors involved in coffee flavor development that occurs during roasting. Understanding the chemical drivers of quality in the green bean is needed for raw material selection and breeding strategies, and to ultimately improve brew quality.

The majority of green coffee beans used for commercial production are imported from regions such as South America, Africa, and Asia. Under inadequate shipping and storage conditions green coffee beans can be exposed to various temperature and humidity profiles leading to unpredictable physical and chemical changes (P. Bucheli & Taniwaki,

2002). Raw green beans are typically dried to below 14% moisture content to prevent microbial growth which may cause excess fermentation and microbial toxin generation during long transportation times (Iamanaka et al., 2014). Detrimental flavor changes caused by chemical changes can also occur within the beans and are more challenging to prevent. For instance, uncontrolled storage of green coffee beans for three months has been found to cause undesirable attributes such as an increase in woody rubbery off-flavors in coffee brew (Bucheli et al., 1998). Such conditions can also increase the generation of esters such as methyl esters of 2 and 3-methylbutanoic acid in the green bean due to microbial growth and cause off-flavor in the brew (Scheidig et al., 2007). Storage

temperature also has a strong influence on free fatty acid content by enhancing lipase activity which can promote the generation of undesirable lipid oxidation compounds (Speer and Kölling-Speer, 2006).

Others researches have shown that post-harvest treatment can also affect metabolic processes and induce changes in coffee brew flavor profiles (Kleinwächter & Selmar,

2010). Increases of oxidative processes, loss of seed viability and changes in cellular structure have been correlated with the loss of quality and an increase in undesirable flavors

(Rendón et al. 2014). Additionally, green coffee beans are susceptible to volatile contamination from the surrounding environment which can impact the resulting coffee flavor profile (Borém et al., 2013; Ribeiro et al., 2011).

Current industrial practices use physico-chemical properties like color and moisture of the green beans as indicators of the resultant coffee brew quality. Green-bluish color has been associated with high quality raw material (Van Der Vossen, 2009). Yet, literature suggests that many other external factors such as growing conditions or green bean processing methods are also influential to the green bean and may not be associated with color changes. The objective of this work was to evaluate the effectiveness of using bean color and moisture content as indicators of flavor quality. Green coffee beans were stored under various temperatures, time, and packaging conditions. Physico-chemical and sensory evaluations were performed on the green coffee beans and their respective brews.

2. Materials and Methods

2.1 Green Coffee Beans

Colombian Arabica wash and Vietnamese Robusta natural beans used for this study were provided by an industrial partner. Green coffee beans (1.2 kg) were tightly sealed in glass jars with PTFE lids to maintain moisture content. Jars were stored in the dark at 2 oC

(fresh) and 35 oC (aged) samples. In order to simulate conditions in which beans were susceptible to moisture loss during storage at ambient temperature (25 oC), another set of beans were kept in burlap bags in the dark.

2.2 Roasted Coffee Beans

Green coffee beans (0.45 kg) were roasted using a gas roaster (Mill City Roaster,

Saint Paul, MN) equipped with a USB Data log and 4 thermocouples. Gas, drum and fan speed were monitored to control the temperature and time profile. At least two roast replicates were performed and mixed homogenously per storage condition. Beans were roasted to a light roast; approximately 9.30 mins of total roasting time (L-value = 30).

Roasted coffee beans were allowed to degas for three days prior to sensory evaluation.

2.3 Moisture Content

Moisture content of the green beans was determined at each storage time point to evaluate the impact of storage condition on bean moisture content following the ISO

6673:2003 method. Briefly, five grams of green beans were weighed on to pre-weighed aluminum pans and heated at 105 oC for 16 hours. Moisture was calculated based on total mass loss. Moisture adjustment of green coffee beans was performed by calculating the

amount of water (g) needed to adjust the water content to desired moisture concentration.

A spray bottle was used to slowly incorporate the water in the beans. Moisture content was then re-evaluated to confirm appropriate adjustment.

2.4 Bean Color

Color of whole green and roasted beans were measured using a color meter (Konica

CR300, Minolta, USA). CIE values (coordinates L= brightness, a= red-green, b= yellow- blue) were determined directly. Five replicates were performed for each sample.

2.5 Sensory Evaluation of Coffee Brew

Triangle tests were carried out to determine if changes in the brew flavor occurred during green bean storage. Aged samples refer to brew prepared from green coffee beans stored for a certain amount of time at a particular storage condition and temperature. Fresh samples refer to brew prepared from green beans stored at 2 oC. Two triangle test sessions were performed to evaluate the fresh and aged samples. In one session, panelists were asked to evaluate differences based on taste and tactile sensations and thus wore nose clips to eliminate aroma inputs. In a second session, nose clips were not used in order to evaluate the overall flavor profile. Eighteen experienced panelists (aged 23- 50; 9 females, 9 males) were included in the study. Coffee brew (5 % ground/water) was prepared from freshly ground coffee (37.5 g) with 750 ml of water using a drip-coffee maker (Moccamaster

KBT741, Technivorm, Italy). Approximately 40 mL of coffee brew was served in a capped

Styrofoam cup and evaluated at 70 oC. The presentation was balanced for all tests.

Compusense Cloud Software (Compusense Inc., Guelph, Ontario, Canada) was used for collecting sensory data.

2.6 Statistical Analysis

The effect of storage on green bean and roasted bean color was evaluated by one- way ANOVA. When a significant effect was observed (a= 0.05), post-hoc multiple comparison tests were performed. JMP® Pro 13 (SAS Institute Inc. Carry, NC).

3. Results

3.1 Optimization of experimental roasting condition

Coffee brews prepared from coffee of different roasting batches were evaluated to ensure the reproducibility of the roasting conditions for the investigation of the impact of green bean storage on sensory properties of the coffee brew. Panelists were not able to detect differences between brews prepared from two different roasting sessions (same green coffee beans) as evaluated by consensus sensory evaluation (n=13). This result ensured that taste or flavor differences detected in the brew would be from the changes due to the green bean storage conditions. The roasting time/temperature profile utilized for the entire study is shown in Figure 7.

450 400 350 F)

o 300 250 sample 1 200 sample 2 150 100 sample 3

Temperature ( 50 0 0:00 0:33 1:06 1:39 2:12 2:45 3:18 3:51 4:24 4:57 5:30 6:03 6:36 7:09 7:42 8:15 8:48 9:21 Time (mins)

Figure 7 Bean roasting profile: temperature (oF) and time of roast (mins)

3.3 Impact of storage conditions on green bean physico-chemical properties

3.3.1 Time and temperature

Green coffee beans were stored at 2 oC and 35 oC for 5 months in tightly sealed glass jars to evaluate the impact of temperature and time on physico-chemical properties

(moisture and color) and coffee brew quality. Color coordinate-L (brightness) was significantly lower after five months (α=0.05) resulting in less bright and more muted color of the bean (Figure 8). Coordinate-a, representative of red-green color, also significantly decreased (Figure 9) indicating a loss of red color. No significant change in coordinate-b, associated with the yellow color, was observed.

60 a ab 50 b L

- 40

coordinate 20

0 Control 3 months 5 months

10 a 9 8 ab 7 green) - 6 b

a (red 5 - 4 3

coordinate 2 1 0 Control 3 months 5 months

It should be noted that Arabica beans were evaluated longer than five months as a result of the sensory experiments discussed in section 3.4.1. Unlike Robusta beans, the coordinate-L (brightness) of Arabica green beans did not significantly change over time 39

and maintained an average of 50 ± 2.5 units over 9 months (data not shown). Coordinate- a (red-green) changed significantly resulting in beans becoming less red at the 7-month time point and onwards, compared to fresh control (Figure 10). Coordinate-b (yellow- blue), however, changed significantly at the 5-month time point and onwards (Figure 11).

Green beans became more yellow according to measured values which is in agreement with overall visual assessment of the aged beans. In summary, Arabica green beans appeared to be more susceptible to yellow color increase than Robusta beans over time.

6 a 5 a a

green) 4 - b 3

a (red b -

1 coordinate

0 Control 3 months 5 months 7 months 9 months

Figure 10 Color coordinate-a (red-green) of Arabica green coffee beans over 9 months at 35oC. Different letters indicate significant difference according to Tukey post-hoc multiple comparison (α=0.05).

25 a ab bc 20 dc

blue) d - 15 b (yellow - 10

5 coordinate

0 Control 3 months 5 months 7 months 9 months

Figure 11 Color coordinate-b (yellow-blue) of Arabica green coffee beans over 9 months at 35oC. Different letters indicate significant difference according to Tukey post-hoc multiple comparison (α=0.05)

Moisture content was determined at each time point before roasting. The moisture content of green Robusta beans remained around 12 % during the entirety of storage for five months at both temperatures. However, Arabica beans were less stable with a moisture content decreasing slightly from 11.3% in the control to about 10% for the samples stored at 35 oC for 9 months. Both the moisture and color changes indicate that

Arabica beans are less stable during storage compared to Robusta beans.

3.3.2 Packaging condition

The impact of type of packaging was studied by comparing green beans kept in glass jars at 2 oC to beans kept at ambient temperature (25 oC) in a burlap bag for 6 months.

A gradual decline in moisture content from 11% to 6% was observed for the beans kept in the burlap bag (Figure 12). Color evaluation of the green beans showed no significant differences between the two samples (data not shown).

12 a

Moisture content % 4 b

0 0 1 2 3 4 5 6 7 Months of storage

Figure 12 Moisture content of green coffee beans stored at (a) 2 oC in glass jars and (b) at ambient temperature in a burlap bag for six months

3.4 Impact of green bean storage conditions on sensory properties

Sensory evaluation via a triangle test with experienced panelists was used to determine changes in sensory properties of coffee brew prepared from fresh and aged green beans. Panelists were instructed to place samples in-mouth with and without wearing nose clip to evaluate taste and overall flavor, respectively.

3.4.1 Time and temperature

A sensory panel consisting of 13-18 participants performed triangle tests to compare coffee brews from green beans stored in glass jars at 2 oC and 35 oC at 3, 5, 7 and

9 months. Both aged Arabica and Robusta beans were evaluated and compared to their respective control (2 oC sample).

No significant differences were found between the fresh and aged samples for both the Arabic and Robusta beans. Results of the 5-month time point suggested that Arabica samples were closer to reaching a significant difference (p= 0.17) than the Robusta beans

(p=0.33) (Table 4,5). For this reason, the rest of the study was carried out on the Arabica species.

After nine months of storage, panelists were still not able to significantly distinguish between the fresh and aged Arabica beans, (5/14 correct response with 9/14 needed for α=0.05; Table 5). The p-values for both the taste and flavor difference tests did not show any decreasing or increasing trends over time suggesting that samples were not significantly changing throughout the 9 months of storage.

Table 4 Results of triangle test comparing Robusta coffee brew made from fresh (2 oC) and aged (35 oC) green coffee beans. Correct Responses/Panelists (p-value) Months of Significant at Significant at storage Taste α= 0.05 Taste & Aroma α= 0.05

3 5/15 (0.60) 9/15 7/17 (0.33) 10/17

5 7/17 (0.33) 10/17 6/17 (0.52) 10/17

Table 5 Results of triangle test comparing Arabica coffee brew made from fresh (2 oC) and aged (35 oC) green coffee beans Correct Responses/Panelists (p-value) Months of Significant at Significant at storage Taste α= 0.05 Taste & Aroma α= 0.05

3 9/18 (0.20) 10/18 6/17 (0.52) 10/17

5 8/17 (0.17) 9/17 7/17 (0.33) 9/17

7 3/16 (0.94) 9/16 6/16 (0.45) 9/16

9 5/14 (0.52) 9/14 3/13 (0.86) 8/13

3.4.2 Packaging condition

Triangle tests performed by 16 panelists revealed that the coffee brews prepared from green coffee beans were stored at 2 oC in a glass jar compared to burlap bag at room temperature were not significantly different from each other (p-value= 0.9) in regard to both taste and flavor. Results suggested that changes, chemical or physicochemical to the bean, occurring in the two different storage environments, did not alter the green beans to an extent that would result in significantly different brew flavor profiles.

3.5 Impact of bean moisture content upon roasting

The impact of green coffee beans moisture contents upon roasting was evaluated.

Green beans stored in a burlap bag at room temperature for six months were used. Initially, these beans had a moisture content of ~ 6%. They were further divided into two groups for roasting: one set was used as is, i.e. 6% moisture content; the second set was adjusted to

14 % moisture content using water and equilibrated for 3 days at 2 oC.

a) 80 * 6% 60

14% 40 Value * 20

0 L a b

Coordinate

b) 80

60 6%

14% 40 Value

0 L a b Coordinate

Figure 13 LAB coordinates of a) green coffee beans b) roasted with different moisture content upon roasting. * indicates significant difference (p<0.001).

Results showed that the color of the green coffee beans was significantly impacted by change in moisture content before roasting. Green coffee beans that had 14% moisture content prior to roasting were less bright (lower L-coordinate) and were less yellow and more blue (lower b-value) than those of 6% moisture content (Figure 13). This is in 45

agreement with the storage study where fresh green beans with higher moisture content were more blue (lower b-value) (Figure 11). However, post-roasting, the coffee beans

(roasted) were not significantly different in color. It has been reported that the color of roasted coffee beans from green beans having different moisture content upon roasting is slightly higher in L* coordinate due to differences in required energy for roasting

(Baggenstoss, Poisson, Kaegi, Perren, & Escher, 2008b). The contradicting results could be due to the difference in roasting time and temperatures used between the two studies.

Similarly, a triangle test performed by 15 experienced panelists determined that the coffee brews were not significantly different (p-value= 0.38) for both taste and flavor evaluations. This suggested that the moisture difference upon roasting (Δ 8%) did not impact the sensory properties of the brew.

4. Discussion

The increase in yellow hue has been previously reported as an indicator of green bean quality loss based on industrial standards (Bicho, Letiao,Ramalho, Lindon, 2014).

Changes in color have been associated with detrimental flavor changes. Color change is suggested to be the result of cell membrane destruction and solute leakage (Bucheli et al.,

1998). A loss of flavor of coffee brew was observed with over prolonged storage of green beans (22 oC, and 63% relative humidity) and was attributed to loss of seed viability

(potential for germination) of the green bean (Dirk Selmar et al., 2008). However, in another study an increase in electric conductivity was correlated to blue color loss (more yellow) in green beans stored in jute bags which had decreased flavor quality upon roasting

(Ribeiro et al., 2011). Electric conductivity of stored seeds has been used to indicate

cellular membrane disruption which would lead to color and flavor change (Loeffler,

Tekrony, & Egli, 1988). In the current study, no significant differences were detected between fresh and aged samples using triangle tests. A significant change in green bean color upon storage at 35 oC did not reflect a change in flavor suggesting that the use of color to monitor flavor change or quality is inadequate.

This also suggested that several other factors including cellular integrity and metabolism are of greater importance to flavor formation. Other environmental factors such as bean genetics, country of origin, growing environment or processing methods would also be expected to be influential to coffee brew flavor.

Review of the triangle test results at the 5-month time point suggested that Arabica beans (p= 0.17) were closer to reaching significant difference from to its respective control, in comparison to Robusta beans (p=0.33). Robusta beans appeared to be more robust to change compared to Arabica beans as observed in this study. This is also reflected in the coordinate-b (yellow color) that did not significantly change over five months for Robusta beans. It is also possible that these differences are due to differences in green bean compositions, i.e. Arabica has higher amounts of lipids and sucrose compared to Robusta

(Wei & Tanokura, 2015). Additionally, differences in bean morphology between the species could also be responsible for the Robusta tolerance to color change. Robusta beans are known to be stronger and less porous (Sreenarayan, Subramanian, & Visvanathan,

1985).

Albeit no sensory differences were reported based on storage conditions, it should be noted that for evaluation of slight sensory differences, such as in this study, a larger number of panelists (50-100) might be required (Civille et al., 2015). A tetrad test is also

an alternative unspecified test that can be applied to provide more power to detect small sample differences, however it does add addition samples for evaluation which increases potential for panelist fatigue (Ennis, 1993). Nonetheless, the results of the current study indicated no obvious differences in coffee flavor quality as a result of storage conditions of the green beans.

5. Conclusion

The effectiveness of traditional industrial practices for green coffee bean quality evaluation as it relates to roasted coffee brew flavor was shown to be inadequate. The impact of temperature, storage time and packaging type during green coffee bean storage was evaluated and results showed significant changes on green bean color and moisture content. However, no significant differences in brew flavor profile (taste and overall flavor) were observed over a period of nine months. This demonstrated the need to develop better prediction tools and identify sensory relevant chemical markers for bean selection.

Chapter 4. Identification of chemical markers that positively impact coffee quality

Abstract

Untargeted LC/MS-Tof chemical fingerprints of coffee brew (n=18) were collected and modeled to predict the corresponding specialty coffee association cup score to provide chemical predictors of coffee grade. An orthogonal partial least squares (OPLS) model was developed with good fit and predictive ability of cup score (R2Y > 0.9, Q2 > 0.9). Four chemical compounds in coffee brew that were positively correlated to cup score were subsequently isolated and purified (>90%) by multi-dimensional preparative LC/MS fractionation system. A sensory recombination study with certified Q-graders (n= 5) confirmed three out of four compounds with molecular ions m/z 437, 419, and 671, significantly increased coffee cup score when added to a control coffee (p<0.001) and can be used to predict coffee quality class. High resolution accurate mass spectrometry along with 1D and 2D NMR experiments were used to elucidate the structures of three sensory active compounds. Compound m/z 437 was identified as novel compound 3-O-caffeoyl-1-

O-3-methylbutanoylquinic acid, its corresponding lactone 3-O-caffeoyl-1-O-3- methylbutanoyl-1, 5-quinide (m/z 419), and compound m/z 671 as a chlorogenic acid derivative (unknown). Additionally, 3-O-caffeoyl-1-O-3-methylbutanoylquinic acid was an endogenous compound in the green coffee beans, providing a chemical marker to predict the coffee brew quality.

1. Introduction

The production, trade, and consumption of coffee has changed significantly in this current “third wave of coffee” era where coffee has moved from a pure commodity to a specialty product (Samoggia & Riedel, 2018). An increasing number of consumers are interested in the intrinsic quality of coffee and more likely to prefer better tasting, higher quality coffees (Giacalone, Fosgaard, Steen, & Münchow, 2016). Flavor quality of coffee is one of the leading drivers of this trend associated with the overall consumer consumption experience that involves taste, aroma, and somatosensations.

A common practice in the coffee industry to assess the flavor quality of coffee beans (brew) is using the ‘cup score’ method of the Specialty Coffee Association (SCA).

Evaluated by certified Q-graders, the SCA quality score is composed of ten sensory metrics including fragrance/aroma, acidity, body, flavor, sweetness, clean cup, balance, aftertaste, uniformity, and overall impression (SCA, 2018). This method was developed to provide consistency of the quality rating and supports criteria for establishing retail price (Pereira et al. 2017). However, a lack of knowledge on coffee quality remains as only a few studies have related cup score to coffee flavor chemistry (Craig, Botelho, Oliveira, & Franca, 2017;

Tolessa et al., 2016). Many papers lack experimental designs that include comprehensive chemical and sensory evaluations. Coffee brew is a complex mixture of thousands of organic compounds endogenous to the green coffee beans or generated during roasting (M.

N. Clifford, 1985). Precursors in the green beans undergo thermal reactions including polymerization, caramelization, and the Maillard reaction between reducing sugars and amino acids, during the course of roasting (Buffo & Cardelli‐Freire, 2004). Flavor generation is highly dependent on the green bean composition, the level of precursors and 51

the degree of roasting which will ultimately determine the flavor of the final brew. Several factors of coffee processing, from farm to cup, are also known to influence coffee flavor quality such as species/cultivars, geographical origin, green bean processing method, roasting and storage (Feria-Morales, 2002; Sunarharum et al., 2014).

Chemical predictors of quality correlated to specific sensory attributes, in both green coffee beans and brew, have been sought for decades. Significant amount of work has been done to establish correlations between non-volatile compounds such as sugars, amino acids, phenolic compounds and fatty acids and quality in both green and roasted coffee beans (Kwon et al., 2015; Ralph Dorfner et al., 2003; Ribeiro, Ferreira, & Salva,

2011; Tolessa et al., 2016). Similarly, in coffee brew, the volatile aroma composition has been largely studied and drivers of quality have been explored. For example, high amounts of unsaturated aldehydes such as (E,E)-2,4-nonadienal and (E,Z)-2,4-heptadienal have been associated with high quality coffee brews. Conversely, high levels of 2- phenylacetaldehyde, 2-methyl-5-propylpyrazine were associated with lower quality coffee

(Piccino et al. 2014). Researchers have traditionally used ‘targeted’ flavor characterization approaches for compound discovery, limiting investigations to unimodal responses such as orthonasal roasty aroma, or bitter taste. Currently, limited amounts of information are available in regard to the direct causation of those compounds to coffee quality as it relates to the overall flavor experience.

Untargeted flavoromic analysis is a newer analytical approach that combines comprehensive chemical profiling and sensory information to offer insights into chemical drivers of flavor properties of a complex food system (Andujar-Ortiz et al., 2015; Charve,

Chen, Hegeman, & Reineccius, 2011b). Recently, this approach was successfully applied 52

to model the chemical changes related to an ill-defined sensory perception, freshness, in citrus fruits (Ronningen et al., 2018b). Using this approach, a novel ionone glycoside which modulated the intensity of orange and fruity characters while increasing green bean notes was discovered.

The overall goal of this work was to apply untargeted flavoromic analysis to characterize the chemical drivers of high quality coffee. Multivariate statistical modeling of liquid chromatography mass spectrometry profiles were used to correlate coffee chemistry with coffee SCA cup score. Highly statistically predictive compounds were selected and further purified in order to conduct sensory recombination tests to validate their impact on cup score and elucidate their structure.

2. Materials and Methods

2.1 Chemicals

Methyl paraben, methanol, acetonitrile, citric acid and formic acid were purchased from Sigma-Aldrich (St. Louis, MO). Sodium phosphate monobasic (NaH2PO4), sodium phosphate dibasic (Na2HPO4) were purchased from Fisher Scientific (Fair Lawn, NJ).

2.2 Coffee Samples

2.2.1 Green coffee beans

Green coffee beans (crop year 2015-2016) were sourced from importing companies in the United States (n=18) and selected to represent multiple origins around the world and different green bean processing methods.

2.2.2 Coffee brew

Green coffee eans were roasted to their optimal condition using the standard SCA roasting conditions (SCA 2009). Freshly roasted beans were then allowed to degas for two days and then stored in glass bottles closed with PTFE lids after nitrogen flushing. Coffee brew (5% ground/ water) was prepared from freshly ground coffee beans using a drip- coffee maker (Moccamaster KBT741, Technivorm, Italy). Two biological replicates were prepared for each coffee sample. Sample clean-up was performed on Oasis HLB prime 96- well plate cartridge, 10 mg bed (Hydrophilic Lipophillic Balanced copolymer, Waters,

Milford, MA, USA). In brief, one ml of coffee brew (60%) was loaded on the cartridge,

500 µl of 5% methanol/water was used to wash the highly polar compounds off the cartridge. In a separate collection plate, 100 µl of 95% acetonitrile/water was then used to elute compounds retained on the cartridge and further diluted 1:4 with water for UPLC analysis.

2.3 Ultra Performance Liquid Chromatography Mass Spectrometry chemical profiling

Chemical fingerprinting was performed using Ultra High-Performance Liquid

Chromatography coupled with a Mass Spectrometer Ion Mobility Time of Flight

UPLCMS-IM-QToF Synapt G2-S (Waters, MA, USA). An Acquity H-Class quaternary flow solvent manager was used. A Cortecs UPLC C18+ column (2.1 x 100 mm) was kept at 40 oC in a Waters column manager. A flow rate of 0.5 mL/min was used with a tertiary solvent mobile phase consisting of (A) nanopure water, (B) acetonitrile, (C) 5% formic acid. The gradient was as follows: 0-0.5 min, B 5%; 0.5-11 min, B 5-50%; 11-12.5 min, B

50-95%; 12.5-14 min, B 95%; 14-15 min, B 95-5%, 9-10, B 5%; C was constant at 2%.

Electrospray ionization (ESI) was run in negative mode with source temperature of 120 oC, desolvation temperature of 400 oC, capillary voltage was set to 2.5 kV, cone sample 40 V,

Tof scan range was 50-1200 m/z and scan time was 0.3 sec for continuum data. Internal reference compound, Lock spray (Leucine-enkephalin, m/z 556.2771), data was acquired, and mass correction applied. Each SPE replicate was injected 2 times in randomized order.

Injection volume was 5 µl, with column standard injected every 10th run to check retention time shifts and mass spectrometer performance throughout experiment sequence.

2.4 Multivariate statistical analysis

Data processing including run alignment and peak picking was performed on

Progenesis QI software (Nonlinear, Durham, NC). Exported features, reported as retention time-mass/charge ratio and intensity, were subsequently filtered using an intensity threshold cutoff and coefficient of variance cutoff to systemically remove noisy and inconsistent data (R studio). Multivariate models were generated using SIMCA-P+ version

14.1 (Umetrics, Umea, Sweden). Data were scaled using pareto-scaling. Principle component analysis and Orthogonal partial least squared (OPLS) models were generated using 2 replicates from each sample. The x-variables were features for each sample with their intensity and the y-variables were the sample’s cupping scores. Variable of importance (VIP) list and the S-plot was generated to determine features predictive of quality.

2.5 Multidimension Liquid Chromatography- Mass Spectrometry Preparative

Fractionation

An excellent specialty coffee brew was used to isolate selected features (m/z 193,

437, 419, and 671). Coffee brew and sample preparation were performed as described in section 2.2.2 and adapted for larger scale extraction. A total volume of 800 ml of coffee brew was loaded on to four Oasis HLB prime (Waters) 6 g bed cartridges and allowed to pass through the cartridges. An initial washing step was performed using 200 ml of 5% methanol/water. Elution was performed in 4 steps using 50 mL of different ratios of methanol/water (40, 60, and 90%) and collected separately. SPE fraction 60% contained features m/z 193 and m/z 437 and fraction 90% contained features m/z 419 and m/z 671.

The fractions were freed from solvent (Rocket Synergy Purge, Genevac, UK) and lyophilized. SPE fraction 60% and fraction 90% were reconstituted in 30%, and 50% methanol/water with 0.1% formic acid, respectively. First dimension fractionation was performed on Prep LCMS-TQD (Waters) coupled with a Waters 2767 fraction collector.

A 50 mm x 50 mm Xbridge prep C18, 5 µm particle size column was used for separation

(Waters). Solvent gradient was optimized for each SPE fraction. Acquisition was performed in single ion monitoring (SIR) and multiple reaction monitoring (MRM) under negative ESI mode. After isolation, fractions were pooled, removed of solvent (Rocket

Synergy Purge, Genevac, UK) and lyophilized. Further purification was performed using a 50x100 mm Xbridge prep 5 µm Shield RP18 column (Waters) and a 10x250 mm Xselect

CSH prep 5 µm Phenyl-Hexyl column (Waters), respectively. LC solvent gradient was optimized to obtain the best separation for each feature. Each fraction isolated was injected on to the UPLCMS-IM-QToF Synapt G2-S (Waters) to ensure accurate peak collection.

Each compound was confirmed at >90% purity on the basis of total ion chromatogram peak area determined in MS scan mode in positive and negative ESI.

2.6 Quantification using Ultra Performance Liquid Chromatography and Tandem Mass

Spectrometry (UPLC/MS/MS)

Known quantities of compounds m/z 193, 437, 419, and 671 were used to quantify each compound in the brew of the three coffees representative of coffee class (Appendix

A. coffee #1, 6, and 17). Quantification was carried out using 5-point external calibration curves built for each compound in water and concentrations were adjusted based on extraction recovery. Sample preparation was performed as described in section 2.2.2 with the addition of methyl paraben as an internal standard. Analyses were carried out using an

Acquity H-Class UPLC system coupled to a Xevo- TQ-S Mass Spectrometer (Waters). A reverse phase BEH C18 (2.1 x 50 mm, 1.6 µm, Waters) was kept at 40oC in a Waters column manager. A flow rate of 0.5 mL/min was used. A binary gradient was used with mobile phase consisting of solvent (A) nanopure water with 0.1 % formic acid and (B) acetonitrile with 0.1% formic acid. The gradient was as follows: 0-0.5 min, B 5%; 0.5-6 min, B 5-50%; 6-7 min, B 50 %; 7-8 min, B 50-95%; 8-8.5 min, B 95%; 8.5-9 min, B 95-

5%, 9-10, B 5%. Electrospray ionization was run in negative mode with a source temperature of 120 oC, desolvation temperature of 550 oC, capillary 2.3 kV, and sample cone 20 V. Optimized MRM condition of each compound are presented in Table 6.

Methylparaben was monitored in ESI negative mode (transition 153 -> 93 m/z).

Relative concentration of all four compounds were determined in the green and roasted coffee beans. Five grams of green and roasted coffee beans were cryogenically

ground with liquid nitrogen for 20 seconds into fine powder. Ground samples (0.25 g) were accurately weighed intro 2 ml Eppendorf tubes, and 1.5 ml of a tri-solvent mixture consisting of acetonitrile: methanol: water (2:2:1 v/v) and 25 µl of methyl paraben was added (final concentration of 20 ppm). Each tube was mixed to ensure homogeneity then shaken at 200 rpm for 1 hour (Thermo Scientific Dubuque, IA). The tubes were then centrifuged for 15 mins at 10,000 rpm. Subsequent supernatant liquid (250 µl) was diluted with 1.75 ml of water then subjected to further SPE clean up. Sample clean-up was performed on a 96- well plate Oasis HLB prime cartridge, 30 mg. One ml of the diluted green bean extract was loaded on the cartridge and 500 µl of 5% methanol/water in water was used to wash the cartridge. In a separate collection plate, 200 µl of 95% acetonitrile/water was then used to elute the compounds retained on the column. The collected retentate was diluted to 1 ml with water for UPLC analysis. Relative concentration was calculated as the peak area of each compound by the peak area of the internal standard.

2.7 Sensory analysis

2.7.1 Coffee cup score evaluation

Sensory evaluation of coffee brew used to determine quality class of each coffee sample was conducted using the SCA cupping protocol (SCA, 2015), with five licensed Q- graders. The coffees were categorized into three categories based on their cup score: excellent specialty quality: 85 and above, very good specialty quality: 80- 84 and below specialty quality: below 80.

2.7.2 Recombination model evaluation

Sensory recombination was carried out to demonstrate causality/or validate relevance of compounds statistically correlated to high quality coffee. A coffee, 78.6 cup score, was selected as the control base coffee. Recombination models were prepared as the control coffee spiked with 0.71, 1.83, 9.60, 4.28 mg/L of compounds m/z 193, 437, 671, and 419 to mimic the levels of an excellent specialty coffee (Table 6). The spiking was performed with individual compounds and as a combination of all 4 compounds totaling in

5 recombination models and one control sample. Samples were blind coded, presented in a randomized order and evaluated using the cupping protocol by five Q-graders.

2.7.3 Sensory analysis of individual compounds flavor activity of compounds

Purified compounds were also tasted in water at concentrations presented in Table

6 for excellent specialty coffee, to determine flavor activity in a 0.025 M phosphate buffer adjusted to pH 5 with 0.1 M citric acid, and nanopure water. A consensus panel of four experienced sensory evaluators was used to assess the taste and flavor of the samples.

2.8 Nuclear magnetic resonance

NMR spectra were obtained using a Bruker Advance III HD Ascend spectrometer equipped with a 5 mm triple resonance observe TXO cryoprobe with z-gradients, operating at 700 MHz for the 1H nucleus and 176 MHz for the 13C nucleus. (Bruker BioSpin,

Rheinstetten, Germany). Instruments were calibrated using the residual undeuterated

1 13 solvent as an internal reference CD3OD H NMR = 3.31 ppm, C NMR = 49.0 ppm.

2.9 Data analysis

For feature selection, peak intensity was evaluated by one-way ANOVA. When a significant effect was observed (a= 0.05), post-hoc multiple comparison tests were performed. Cup scores of sensory recombination models were evaluated by two-way

ANOVA. When significant effect was observed (a= 0.05), Dunnett’s test was performed compared to the control coffee. JMP® Pro 13 (SAS Institute Inc. Carry, NC).

3. Results and Discussion

3.1 Untargeted chemical profiling using liquid chromatography mass spectrometry

The objective of this research was to characterize chemical drivers of coffee cup score. Eighteen green coffee bean samples of various geographic origins were sourced and categorized by cup score and further into quality classifications: below specialty, very good specialty, and excellent specialty (cup score: 72.6-87.8 /100) according to SCA cupping method by certified Q-graders (Appendix A). The LC/MS profiles of coffee brews were collected, and a total of 2450 chemical features were extracted from the chromatographic data. Multivariate statistical analyses were then applied to establish the relationship between coffee brew chemistry and the cup scores.

Unsupervised principle component analysis (PCA) was modeled with the 2450 features to determine samples outliers and confirmed good reproducibility of data (data not shown). A supervised orthogonal partial least square (OPLS) model was then used to connect coffee chemistry to sensory data by correlating chemical features with coffee cup scores (Figure 14a). Review of model quality metrics (R2 and Q2) revealed high goodness of fit (R2Y=0.997) and high predictive ability based on high Q2 (0.979). The root mean 60

squared error of prediction (RMSEP) was 0.98 and indicated that the model was able to predict coffee cup score with less than 1 cup score error. Lastly, a permutation test was performed and indicated that the model is not overfitting (permutated R2 = 0.6, Q2 = -1.4).

Differentiation of samples by quality class was observed along principle component

(PC) from below specialty quality (left) and excellent specialty quality (right) (Figure 14a), suggesting the existence of chemical differences between coffee classes. This also confirmed that the model could successfully use the collected chemical features to predict coffee quality class. Features responsible for driving the differences in brew quality from class to class were reviewed based on their contribution and relevance to the model’s predictive ability. Multiple criteria were used to the select potential markers of quality, including variables of importance (VIP), covariance or magnitude of intensity change

(p[1]) and correlation to cup score (p(corr[1])), and ANOVA. VIP values and/or VIP rank have been successfully used for feature selection of flavor relevant information (Iwasa et al., 2015; Ronningen et al., 2018b). A VIP value above 1 is typically believed to substantially contribute to the predictive ability of the model (Galindo-Prieto, Eriksson, &

Trygg, 2014). In this study, 2450 features were initially narrowed down to 50 that showed the highest VIP rank. The covariance of each compound between classes of coffee (p[1]) and the correlation of each feature to the Y-variable, cup score, (p(corr[1])) was then used to generate the S-plot. (Figure 14b). The use of correlation and magnitude/ fold change is also a common practice for feature selection (Teegarden, Schwartz, & Cooperstone, 2019).

However, no cut off value is universal as they are highly dependent on the model. In this study, only the features exhibiting a p(corr[1]) > 0.7 and a p[1] > 0.075 were selected.

Lastly, ANOVA was performed to evaluate the effect of coffee class on feature intensity.

Ultimately, this led to the selection of four features of interest (retention time_m/z):

4.13_193 m/z, 7.00_437 m/z, 8.25_671 m/z, and 8.52_419 m/z.

These markers were extracted in high purity (>90%) from the brew using multi- dimensional LC and further quantified in three coffees representative of each class. All the compounds exhibited significant differences among quality classes (p<0.05) with higher amounts found in excellent specialty brew.

The concentration of compound m/z 193 ranged from 0.81 mg/L in the below specialty coffee to 1.5 mg/L in the excellent specialty coffee, corresponding to 2-fold change between extreme classes. The same extent of changes was observed for compound m/z 437 and m/z 419 exhibiting concentrations from 1.4 and 3.7 mg/L in the below specialty coffee to 3.2 and 7.9 mg/L in the excellent specialty coffee, respectively. Compound m/z

671 showed greater differences with about a 6-fold change, between below specialty coffee, 2 mg/L, and excellent specialty coffee at 11.7 mg/L (Table 6).

3.2 Sensory validation of brew markers on coffee cup score

The sensory impact of the statistically contributing markers to quality was validated with recombination models that were evaluated by five Q-graders. Recombination models were prepared using a control coffee with low cup score (78.7 points) and reconstituted at the levels of an excellent specialty coffee (Table 6) with purified compounds. Overall, the addition of 3 of the 4 compounds individually resulted in significant increases in cupping score (p<0.05) of the recombination models compared to the control (Figure 15). The addition of four compounds in combination also resulted in significant increases in cupping score.

The addition of compound m/z 437 induced the greatest change with an increase of

5.2 points from the control coffee. Compounds m/z 419, 671, increased cup score by 3.2, and 1.8 points, respectively. Compound m/z 193, increased cup score by 1.1 points.

Notably, the addition of m/z 437, 419, and 671 improved the control coffee that is a below specialty grade coffee to a very good specialty grade coffee (>80 points).

The recombination model with the addition of all four compounds increased cup score from 78.7 to 81.3 points, or 2.6 points. This indicated that no cumulative effect occurred as the full model (all compounds) score was higher than the addition of compound m/z 671 individually, but lower than the addition of compound m/z 419 and significantly lower than m/z 437 (p<0.001). This suggests the existence of synergistic interactions between compounds resulting in enhancement or suppression effects on the cup score. The occurrence of compound interactions is further demonstrated when the calculated mathematical average score of recombination samples m/z 193, 437, 419, and 671 (81.4 points) was nearly identical to the score of the recombination sample that included four compounds (81.3 points, Figure 15).

Further review of the compound activity (Figure 15) and the compound concentration (Table 6) indicated that although compound m/z 437 was present in low levels in excellent specialty coffee (3.21 mg/L) in comparison to compound m/z 419 (7.97 mg/L) or m/z 671 (11.68 mg/L), m/z 437 was the compound that had the greatest impact on cup score, 5.2 point increase (Figure 15).

Sensory relevant compounds (m/z 437, 419 and 671) were identified using high resolution MS and NMR. All identified compounds were reported as chlorogenic acid derivatives. Chlorogenic acids (CGA) are esters of quinic acid and trans-cinnamic acids

such as caffeic acid and ferulic acid. Compound m/z 437.1476 was assigned an elemental composition of C21H25O10 (D 6.6 ppm). It was further identified as 3-O-caffeoyl-1-O-3- methylbutanoylquinic acid (Figure 16a). The 1H NMR spectroscopic data displayed the characteristic pattern of a 3,4-dihydroxy cinnamoyl conjugate with three aromatic protons at δH 6.87 (d, J = 1.9 Hz, 1H), 6.80 (dd, J = 7.9, 1.9 Hz, 1H), 6.58 (d, J = 7.9 Hz, 1H) and two trans-olefin protons at δH 7.56 (d, J = 15.7 Hz, 1H) and 6.12 (d, J = 15.7 Hz, 1H). The

1H NMR spectrum further exhibited signals characteristic of a quinic acid group with three downfield signals at δH 5.70, δH 5.02, and δH 4.24 corresponding to methine protons attached to oxygenated carbons. Key HMBC correlation of H-3/C-9’ allowed for confirmation of the link between the cinnamoyl moiety and the quinic acid group located at C-3 (Table 7). Additionally, MS/MS spectra of 437 revealed fragments of 173, 161 m/z which are characteristic of chlorogenic acid (CGA) backbone (Clifford et al., 2003).

Fragment 335 m/z corresponded to neutral loss of 102 as well as product ion 101 m/z [M-

H]- which together are suggestive of a methylbutanoic acid moiety (Figure 17).

Elemental composition C21H23O9 was assigned for compound m/z 419.1313 (D 6.9 ppm). MS/MS data of compound m/z 419 revealed a similar fragmentation pattern to compound m/z 437 with ions 101, 161 and 335 m/z (Figure 16b). An elimination of water, likely from the chlorogenic acid quinic moiety of structure m/z 437 was also observed, suggesting a lactone function group. Compound m/z 419 was identified as 3-O-caffeoyl-1-

O-3-methylbutanoyl-1,5-quinide using 1D and 2D NMR experiments. Key COSY correlations between methine proton at C10 and methyl protons of C11/C12 were observed to confirm the identification. Lactones of chlorogenic acid have been previously identified in roasted coffee (Schrader, Kiehne, Engelhardt, & Maier, 1996). Lactones are formed

exclusively on chlorogenic acids free of substitution on C5 position and its formation is favored for 3-CQA over 4-CQA due to steric hindrance of the ester bond and the equatorial confirmation being more energetically stable on the former (Farah et al. 2005). A similar chlorogenic lactone, 3-O-caffeoyl- γ-quinide, has been identified as bitter-active in coffee using taste-guided fractionation (Oliver Frank et al., 2006).

Structural elucidation of compound 671.3101 m/z revealed another chlorogenic acid derivative. MS/MS fragmentation data supports the presence of fragment ions including ferulic acid (193 m/z) and 3,4-dimethoxycinnamate (207 m/z) and a methylbutanoate moiety (101 m/z) (Figure 16c). Further NMR experiments will be carried out to elucidate the structure.

In addition to cup scores, the Q-graders also documented generalized flavor descriptors of the recombination samples tested (Figure 15). In general, the recombination samples with addition of marker(s) were observed to also modify the retronasal aroma, taste and chemesthetic attributes. The recombination models were described with citrus, caramel and lemon fruit notes whereas the control was woody, old and astringent. Flavor attributes such as woody, old, and astringent which are typically associated with less desirable coffee (Peter Bucheli et al., 1998).

Four experienced panelists also evaluated compounds m/z 437, 419, and 671 individually at concentrations reported in the excellent specialty coffee in water to gain further insights into the flavor activity of each compound. Sensory results revealed that each compound showed no flavor activity when tasted in water at the pH and levels of an excellent specialty coffee. Flavor modulators or neutral-tasting compounds that modify flavor perception have been previously reported (Jelen, 2012). For example, cellotretraose,

a tasteless cellooligosaccharride, was found to suppress bitterness of caffeine (Ley, 2008).

Other compounds in the family of homoeriodictyol, tasteless on their own, exhibited a bitter suppression effect as well, on caffeine, quinine, and paracetamol (Ley et al., 2006).

Other tasteless compounds have been reported as sweetness and bitterness inhibitors (Kurtz and Fuller 1990). The levels at which the compounds were tasted in this study could be below average detection threshold (subthreshold) therefore causing undetectable flavor activity on their own, however altering the coffee flavor when added at the levels of excellent specially coffee to a below specialty coffee brew. Enhanced sensitivity of an odor compound by a taste compound at subthreshold levels has been previously demonstrated with benzaldehyde and saccharin (Dalton, Doolittle, Nagata, & Breslin, 2000). In accordance with our findings, the changes in cup score and flavor of the recombination samples could be explained by taste modulation, or perceptual taste-aroma interaction. It is unlikely that at the levels added, physico-chemical interactions could occur between the non-volatile markers and coffee volatile aroma compounds (King and Solms 1982). Along this line, Charles et al. (2015) demonstrated that the addition of sugar modified sensory perception of espresso coffee but did not change its aroma release.

The impact of markers on each of the 10 individual attributes (refer to Table 2 in

Literature Review) of the total cup score was also further evaluated. The addition of compounds m/z 437 and 419 significantly increased flavor, aroma, aftertaste, acidity, body, balance, and overall impression attribute scores which led to a large increase in total cup score by 5.2 and 3.2 points, respectively. Increase in the overall impression attribute was the biggest contributor to the significant increase in total cupping score of the recombination models (p<0.05). This attribute increased by 1.1, 0.85, 0.7 points for

recombination model m/z 437, 419, 671, respectively, compared to the control which was given 6.5/10 points. Attributes uniformity, sweetness, and clean cup did not significantly change in the recombined model compared to the control, for all recombination models.

This work demonstrated that the addition of predictive markers to coffee brew cup score was able to significantly improve the quality from below specialty to very good specialty coffee (>80 points) through modulation of the flavor. The presence of a common methylbutanoic acid moiety was noted on the three structures. This potentially indicates the key role of this structural moiety on the observed increase in coffee quality score. In its free form, compound 3-methylbutanoic acid has been identified as a potent odorant in roasted coffee, exhibiting odor qualities such as sweaty and fermented (I. Blank et al.,

1991; Imre Blank et al., 1992; W. Holscher, Vitzthum, & Steinhart, 1990) and was suggested to contribute to the sour flavor of heated canned coffee drinks (Kumazawa &

Masuda, 2003). A 3-fold higher relative concentration of 3-methylbutanoic acid have been found in Arabica coffee compared to Robusta (I. Blank et al., 1991). Additionally, its prevalence in healthy green beans compared to defective green beans has also been demonstrated (Toci & Farah, 2008). This is consistent with our finding suggesting that the methylbutanoic acid moiety could be potentially released during coffee consumption and able to improve the flavor of coffee. Iwasa et al. (2015) reported two isomers of 3- methylbutanoyl glycoside in green coffee beans as precursors of 3-methylbutanoic acid in the coffee brew. The authors showed 3-methylbutanoic acid enhanced the attribute aftertaste in the SCA cup score when spiked at 0.0925 mg/L level into coffee. No significant effect on the total cup score was reported. Future work could examine the amount of 3-methylbutanoic released in the saliva during coffee consumption to further the

understanding of mechanisms impacting coffee quality. However, the role of 3- methylbutanoic acid remains controversial. Its presence in green beans has also been associated with low brew quality and positively correlated with green and earthy notes typically associated with defects (Bertrand et al., 2012).

Additionally, the three compounds shared a chlorogenic acid backbone. The flavor of chlorogenic acid derivatives and their flavor modifying properties such as sweetness enhancement (Upadhyay and Rao 2013) and bitter inhibition, have been reported (Riemer,

1993). The addition of 30 ppm of chlorogenic acid in the form of an extract prepared from green coffee beans was found to reduce the metallic and bitter off-taste of an acidic beverage (Chieng et al., 2002). Chlorogenic acid’s ability to increase water solubility of certain volatiles has also been demonstrated (King and Solms 1982).

3.3 Investigation of the impact of roasting on the coffee brew markers in green and roasted coffee beans

The impact of roasting on quality markers was evaluated by quantification of identified compounds in the green and roasted beans. Compounds 3-O-caffeoyl-1-O-3- methylbutanoylquinic acid (m/z 437) and a chlorogenic acid derivative (m/z 671), were found to be inherent to the green coffee beans by LC/MS analysis (data not shown).

Compound 3-O-Caffeoyl-1-O-3-methylbutanoylquinic acid (m/z 437) could be arising from the condensation or esterification of chlorogenic acid with 3-methylbutanoic acid which could be generated during the coffee fermentation process as produced by microorganisms (Feng et al., 2013; Hau Yin Chung, Pui Kwan Fung, & Kim, 2005;

Schieberle & Grosch, 1988).

The levels of 3-O-caffeoyl-1-O-3-methylbutanoylquinic acid (m/z 437) in the green bean and corresponding roasted bean for each sample were plotted and showed significant positive correlation (r) of 0.76, p< 0.0001 (Figure 18). Compound 3-O-Caffeoyl-1-O-3 methylbutanoylquinic acid was present in about 3-fold higher amount in the in the green coffee bean compared to the corresponding roasted bean extract. Two samples of below specialty coffees (circled samples, Figure 18), showed similar levels of 3-O-Caffeoyl-1-O-

3-methylbutanoylquinic acid in the green beans as excellent specialty coffee. Further review of cupper’s flavor notes for these two samples revealed that these two outliers had flavor defects including old, baggy (coffee jute), paper, vegetal, raw potato flavors, and moldy notes which resulted in the deduction of cup score. The two outliers were removed from the sample data set and a second correlation plot between the levels of 3-O-Caffeoyl-

1-O-3-methylbutanoylquinic acid in green coffee beans was plotted against coffee cup score and achieved significant correlation value of 0.71, p< 0.0001 (data not shown). Based on the observed linear relationship between green coffee bean and cup score, 3-O-Caffeoyl-

1-O-3-methylbutanoylquinic acid (m/z 437) could be used directly as a quality marker for green coffee bean selection.

Compound 3-O-caffeoyl-1-O-3-methylbutanoyl-1,5-quinide (m/z 419), the 2nd most influential compound on cup score, was only detected in the roasted beans; no detectable amounts were found in the green beans. Its formation during roasting was likely through cyclization and further dehydration of the quinic acid hydroxy group at C3 position on 3-O-Caffeoyl-1-O-3-methylbutanoylquinic acid (see Figure 17). Results showed that the relative concentration of compound m/z 671 in the green beans was not linear to cup score (data not shown). This suggested that the existence of cofounding parameters driving

the formation of m/z 671 during roasting, and later on, in the brew. Hence, m/z 671 is a good predictor for brew quality, but it may not be a highly predictive marker in the green coffee beans.

4. Conclusion

Untargeted flavoromic analysis was successfully applied to characterize chemical drivers of coffee quality. Four statistically correlated compounds (to cup score) were further examined though sensory recombination models with Q-graders. Three chlorogenic acid derivative compounds were shown to significantly increase coffee cup score, namely novel 3-O-caffeoyl-1-O-3-methylbutanoylquinic acid (m/z 437), novel 3-O-caffeoyl-1-O-

3-methylbatanoyl-1,5-quinide (m/z 419), and unknown chlorogenic acid derivative m/z

671. These findings provide a valuable tool to facilitate selection and negotiation for producers and the industry. Insights of chemical markers of high quality coffee can lead to investigation of process optimization strategies in future studies

Excellent Specialty (HQ) a) Very good Specialty (MQ) Below Specialty (LQ) (b)

PC2

R2X 0.769 R2Y 0.997 2

Q 0.979 RMSEP 0.98

PC1 p(corr[1])

p[1]

[1]) rr p(co

p[1]

Table 6 Concentration (mg/L) of select chemical markers in coffee. Different letters indicate significant difference according to Tukey post-hoc multiple comparison (α=0.05). Multiple reaction monitoring was performed using electrospray ionization negative mode (ESI-).

ESI Concentration (mg/L) ± standard deviation (-) Parent Product Ion Ions Below Very good Excellent m/z specialty specialty specialty 193.0 193.0 149.0 121.0 0.81±0.04c 1.24±0.06b 1.52±0.00a

437.1 437.1 173.1 275.1 1.38±0.05c 1.99±0.13b 3.21±0.07a

671.3 671.3 221.1 207.0 2.08±0.04c 5.53±0.39b 11.68±0.26a

419.1 419.1 161.1 179.1 3.69±0.23b 5.89±0.39a 7.97±0.12a

86 ** 84 Specialty ** Quality * (> 80 cup score) 82 ** 80 78 Control 76 Below Specialty Total cup score Quality 74 72

1. Control

2. Recombination3. Recombination 1934. m/zRecombination 4375. m/zRecombination 419 m/z 671 m/z

Calculated average of sample 2,3,4,5 6. Recombination 193, 437, 419, 671 m/z

Figure 15 Total cup score of control and recombination [control + compound(s)]. * indicate significant difference from control according to Dunnett (p<0.05), ** (p<0.005). Area highlighted in yellow represents the range of cup score for coffee that are Below Specialty quality grade (<80 points). Area in blue represents range of cup score of coffee that are considered Specialty grade (>80 points).

275.1

a) 173.0

275 m/z

161.0 Neutral loss 162 437.1 101.0 Relative Intensity

m/z

b) 161.0

101.0 179.0

317.0 Relative Intensity 419.1 335.0 m/z

c) 207.0 249.0 193.0 Intensity 671.3 301.1 101.0 Relative m/z

Figure 16 MS/MS fragmentation of compounds (a) m/z 437, (b) m/z 419, and (c) m/z 671

Table 7 1H and 13C spectroscopic data of compound m/z 437 (MeOD, 700 MHz)

Compound position δ , type δ (J in Hz) HMBC c H 1 79.12, C 2a 38.52, CH 1.94, ddd (17.7, 11.8, 5.7) 3, 4, 1 2 2b 2.35, ddd (12.9, 5.3, 3) 3, 4, 1 3 67.53, CH 5.69, qd (11.2, 5.1) 4, 9’

4 75.64, CH 5.03, ddd (10.3, 7.1, 3.0)

5 69.49, CH 4.24, dq (13.2, 3.1)

6a 36.26, CH 2.14, m 2 6b 2.07, m

7 180.78, C

8 172.92, C 9a 43.03, CH 2.27, dd (14.5, 7.1) 8, 11, 10 2 9b 2.20, dd (14.5, 7.3) 8, 11, 10 10 25.57, CH 2.07, m 11, 12

11 21.32, CH3 0.92, m 9, 10 12 21.40, CH 0.92, m 9, 10 3 1’ 124.47, C 2’ 105.08, CH 6.87, d (1.9) 6’, 7’, 3’ 4’,

3’ 152.74, C

4’ 156.04, C 5’ 107.67, CH 6.58, d (7.9) 1’, 3’, 4’ 6’ 122.47, CH 6.80, dd (7.9, 1.9) 2’, 7’, 4’ 7’ 147.73, CH 7.56, d 2’, 6’, 8’, 9’ 8’ 110.95, CH 6.12, d 1’, 9’

9’ 167.46, C

Figure 17 Compound m/z 437 (MeOD, 700 MHz) with designated carbon number (right) and corresponding COSY (blue line) and key HMBC correlation (arrows) (left).

a) r = 0.76 18 Excellent Specialty 16 Very good Specialty 14 b)Below Specialty 12 10 8 Flavor defect 6 4 2

Relative conc. in green bean green in conc. Relative 0 0 1 2 3 4 5 6 Relative conc. in roasted bean Figure 18 Relative concentration in green and roasted coffee beans of compound m/z 437. Relative concentration is calculated as compound peak area/internal standard peak area. Pearson correlation (r) = of 0.76, p< 0.0001. Eclipse highlight samples with cup flavor defect.

Chapter 5. Characterizing markers of low-quality coffee using an untargeted flavoromic

analytical approach

Abstract

Untargeted LC/MS-Tof chemical profiling of eighteen coffee brews from beans sourced from different origins were collected and modeled to predict the corresponding specialty coffee association cup score to provide chemical predictors of coffee quality. An orthogonal partial least squares (OPLS) model was developed with good fit and predictive ability of cup score for coffee brew (R2 > 0.9, Q2 > 0.9). Six highly predictive chemical markers of low cup scores were subsequently isolated and purified (>90%) by multidimension preparative LC/MS fractionation systems. Sensory recombination models were used to determine the sensory impact of the addition of compounds as individuals and as a combination on the SCA cup score as evaluated by five Q-graders. The addition of four of the six compounds individually significantly decreased the cup score (p <0.1). Structural elucidation of statistically significant markers through MS/MS and 2D NMR experiments revealed the compounds were diterpenes, in the class of ent-kauran-oic acid. Compound m/z 659, which was is the most negatively influential compound on cup score, was identified as novel compound ent-16α, 17-dihydroxy-kauran-19-diglucoside. Its aglycone form, ent-16α, 17-dihydroxy-kauran-19-oic acid (m/z 335), was the 2nd most influential compound on cup score. Compound m/z 351 was tentatively identified as 16b,17,18- trihydroxy-ent-kauran-19-oic acid and unknown compound 9.48_333 (rentetion time_m/z) as another ent-kaurane diterepene. Compounds m/z 335 and 351 were endogenous to green coffee beans and provide chemical quality marker indicators of low-quality coffee.

1. Introduction

Coffee is appreciated by consumers all of over the world. The market share for specialty coffee is 59% of all coffee consumed on a daily basis and this number continues to increase (SCA, 2017). The quality assessment of coffee is directly related to the chemical composition of the raw coffee beans and the processing conditions from farm to cup

(Sunarharum, Williams, & Smyth, 2014). As the quality of coffee is associated with the selling price, significant effort has been made to understand the factors that influence bean quality and to identify markers of coffee flavors (Clifford, 1985).

For decades, the term “quality” was used with respect to off-odors found in coffee brew (Clifford, 1985). Characterizing these defects has been a popular research topic in literature (Toci & Farah, 2008). For example, musty and earthy off notes in coffee brew were found to be caused by 2,4,6 trichloroanisole in green coffee beans (Spadone et al.

1990). Moldy/mushroom aroma in the brew has been suggested to be from the production of 1-oct-en-3-ol in green coffee infected with microorganisms (Iamanaka et al. 2014). In roasted beans, several compounds such as 4-ethyl-2-methoxyphenol and 2,5-dimethylfuran have been determined to be markers for improper roasting conditions (Yang et al. 2016).

Additionally, some studies have related the content of non-volatile constituents of the bean to quality. For example, high levels of the amino acids histamine and tryptamine were correlated to the presence of defective beans (Oliveira, Franca, Glória, & Borges, 2005) and the diterpene 16-O-methylcafestol was found to be exclusively in Robusta beans

(Speer & Mischnick, 1989). Since then, this diterpene has been used as a marker for

discrimination between Robusta and Arabica, with Arabica having a universally preferred flavor.

Nonetheless, the relevance of defects on coffee quality is not adequately defined as there is a lack of agreement of how to define the term “coffee quality” in the literature. In

1984, the Specialty Coffee Association (SCA) cupping protocol was developed and has since become widely used throughout the coffee industry. Evaluated by the Coffee Quality

Institute (QCI) certified Q-graders, the SCA cup score is composed of ten sensory metrics including fragrance/ aroma, acidity, body, flavor, sweetness, clean cup, balance, aftertaste, uniformity, and overall impression (SCA, 2015). The cup score classifies coffee into quality classes from below specialty to specialty grade. Samples are evaluated based on overall flavor which encompasses taste, aroma and somatosensations.

The vast majority of coffee research has been based on targeted analytical approaches to identify new compounds associated with flavor perception; however, this approach is limited to compounds having distinct flavor activity of their own. This approach is not suited to understand coffee quality where well-defined attributes, like bitterness, may not be the main driver of quality. A recent developed untargeted flavoromics approach adopts experimental designs devised to reveal sensory relevant information (Ronningen et al., 2018b). This method combines comprehensive chemical and sensory information of a system with the use of multivariate statistics (Cevallos-

Cevallos, Reyes-De-Corcuera, Etxeberria, Danyluk, & Rodrick, 2009). Hence, in this work, untargeted flavoromic analysis was applied to characterize chemical drivers that negatively impact quality. Highly predictive compounds of lower coffee quality were selected from statistical models, and sensory recombination was performed to validate their

impact on cup score using Q-graders. Findings were further explored in green coffee beans to investigate the source of these markers of quality.

2. Materials and Methods

2.1 Chemicals

2.2 Coffee Samples

2.2.1 Green coffee beans

2.2.2 Coffee brew

Beans were roasted to their optimal condition using the standard SCA roasting conditions (SCA 2009). Freshly roasted beans were then allowed to degas for two days and then stored in glass bottles closed with PTFE lids after nitrogen flushing. Coffee brew (5% ground/water) was prepared from freshly ground coffee beans using a drip-coffee maker

(Moccamaster KBT741, Technivorm, Italy). Two biological replicates were prepared for each coffee sample. Sample clean-up was performed on an Oasis HLB prime 96-well plate cartridge, 10 mg bed (Hydrophilic Lipophillic Balanced copolymer, Waters, Milford, MA,

USA). In brief, one ml of coffee brew (60%) was loaded on the cartridge, 500 µl of 5% 82

methanol in water was used to wash the highly polar compounds off the cartridge. In a separate collection plate, 100 µl of 95% acetonitrile/water was then used to elute compounds retained on the cartridge and further diluted 1:4 with water for UPLC analysis.

2.3 Ultra Performance Liquid Chromatography Mass Spectrometry chemical profiling

Chemical fingerprinting was performed using Ultra High-Performance Liquid

Chromatography coupled with a Mass Spectrometer Ion Mobility Time of Flight

50-95%; 12.5-14 min, B 95%; 14-15 min, B 95-5%, 9-10, B 5%; C was constant at 2%.

Electrospray ionization (ESI) was run in negative mode with source temperature of 120 oC, desolvation temperature of 400 oC, capillary voltage was set to 2.5 kV, cone sample 40 V,

Tof scan range was 50-1200 m/z and scan time was 0.3 sec for continuum data. Internal reference compound, Lock spray (Leucine-enkephalin, m/z 556.2771), was acquired, and mass correction was applied. Each SPE replicate was injected 2 times in randomized order.

Injection volume was 5 µl, with column standard injected every 10th run to check for retention time shifts and mass spectrometer performance throughout experimental sequence.

2.4 Multivariate statistical analysis

Data processing including run alignment and peak picking was performed on

2.5 Multidimension Liquid Chromatography- Mass Spectrometry Preparative

Fractionation

Compounds (retention time_m/z) 7.92_333, 8.21_333, 9.48_333, 335, 351, and 659 were isolated from a below specialty quality coffee that contained the highest amounts of these compounds. Coffee brew and sample preparation were performed as described in section 2.2.2 and adapted for larger scale extraction. A total volume of 800 ml of coffee brew was loaded on to four Oasis HLB prime (Waters) 6 g bed cartridges and allowed to pass through the cartridges. An initial washing step was performed using 200 ml of 5% methanol/water. Elution was performed in 4 steps using 50 mL of a methanol/water solvent

(20, 40, 60, and 90%) and collected separately. Compound m/z 659 was isolated from the

40% fraction, compounds m/z 351, 335 from the 60% fraction and RT_m/z 7.92_333,

8.21_333, and 9.48_333 from the 90% fraction. The SPE fractions were freed of solvent

(Rocket Synergy Purge, Genevac, UK) and lyophilized. This SPE procedure was performed multiple times and the same fractions were combined. The 40, 60, and 90% SPE fractions were reconstituted in 20%, 30%, and 50% methanol and 0.1% formic acid respectively. First dimension fractionation was performed on Prep LCMS-TQD (Waters) coupled with a Waters 2767 fraction collector. A 50 mm x 50 mm Xbridge prep C18, 5 µm particle size column was used for separation (Waters). Solvent gradient was optimized for each SPE fraction. Acquisition was performed in single ion monitoring (SIR) and multiple reaction monitoring (MRM) under negative ESI mode. After isolation, fractions were pooled, removed of solvent (Rocket Synergy Purge, Genevac, UK) and lyophilized. Further purification was performed using a 50 x100 mm Xbridge prep 5 µm Shield RP18 column

(Waters) and a 10 x 250 mm Xselect CSH prep 5 µm Phenyl-Hexyl column (Waters), respectively. LC solvent gradient was optimized to obtain the best separation for each feature. Each fraction isolated was injected on to the UPLCMS-IM-QToF Synapt G2-S

(Waters) to ensure accurate peak collection. Each compound was confirmed at >90% purity on the basis of total ion chromatogram peak area determined in MS scan mode in positive and negative ESI.

2.6 Quantification using Ultra Performance Liquid Chromatography and Tandem Mass

Spectrometry (UPLC/MS/MS)

Known quantities of compounds (RT_m/z) 7.92_333, 8.21_333, 9.48_333, 335,

351, and 659 were used to quantify each compound in the brew of the three coffees

representative of coffee class (Appendix A. coffee #1, 6, and 17). Quantification was carried out using 5-point external calibration curves built for each compound in water and concentrations were adjusted based on extraction recovery. Sample preparation was performed as described in section 2.2.2 with the addition of methyl paraben as an internal standard. Analyses were carried out using an Acquity H-Class UPLC system coupled to a

Xevo- TQ-S Mass Spectrometer (Waters). A reverse phase BEH C18 (2.1 x 50 mm, 1.6

µm, Waters) was kept at 40oC in a Waters column manager. A flow rate of 0.5 mL/min was used. A binary gradient was used with mobile phase consisting of solvent (A) nanopure water with 0.1 % formic acid and (B) acetonitrile with 0.1% formic acid. The gradient was as follows: 0-0.5 min, B 5%; 0.5-6 min, B 5-50%; 6-7 min, B 50 %; 7-8 min, B 50-95%;

8-8.5 min, B 95%; 8.5-9 min, B 95-5%, 9-10, B 5%. Electrospray ionization was run in negative mode with a source temperature of 120 oC, desolvation temperature of 550 oC, capillary 2.3 kV, and sample cone 20 V. Optimized MRM condition of each compound are presented in Table 1. Methylparaben was monitored in ESI negative mode (transition 153

-> 93 m/z).

Relative concentration of all six compounds were determined in the green and roasted coffee beans. Five grams of green and roasted coffee beans were cryogenically ground with liquid nitrogen for 20 seconds into fine powder. Ground samples (0.25 g) were accurately weighed intro 2 ml Eppendorf tubes, and 1.5 ml of a tri-solvent mixture consisting of acetonitrile: methanol: water (2:2:1 v/v) and 25 µl of methyl paraben was added (final concentration of 20 ppm). Each tube was mixed to ensure homogeneity then shaken at 200 rpm for 1 hour (Thermo Scientific Dubuque, IA). The tubes were then centrifuge for 15 mins at 10,000 rpm. Subsequent supernatant liquid (250 µl) was diluted

with 1.75 ml of water then subjected to further SPE clean up. Sample clean-up was performed on a 96- well plate Oasis HLB prime cartridge, 30 mg. One ml of the diluted green bean extract was loaded on the cartridge and 500 µl of 5% methanol/water was used to wash the cartridge. In a separate collection plate, 200 µl of 95% acetonitrile/water was then used to elute the compounds retained on the column. The collected retentate was diluted to 1 ml with water for UPLC analysis. Relative concentration was calculated as the peak area of each compound by the peak area of the internal standard.

2.7 Sensory analysis

2.7.1 Coffee cup score evaluation

Sensory evaluation of coffee brew used to determine quality class of each coffee sample was conducted using the SCA cupping protocol (SCA, 2015), with five licensed Q- graders. The coffees were categorized into three categories based on their cupping score: excellent specialty quality: 85 and above, very good specialty quality: 80- 84 and below specialty quality: below 80.

2.7.2 Recombination model evaluation

Sensory recombination was carried out to demonstrate causality/or validate relevance of compounds statistically correlated to low quality coffee. A coffee sample, 82.4 cup score, was selected as the control base coffee. Recombination models were prepared as the control coffee spiked with 5.12, 3.08, 2.14, 1.97, 2.32, 0.64 mg/L of compounds

(retention time_m/z) 7.92_333, 8.21_333, 335, 351,659, 9.48_333 to mimic the levels of a below specialty coffee (Table 6). The spiking was performed with individual compounds and as a combination of all 6 compounds totaling 7 recombination models and one control

sample. Samples were blind coded, presented in a randomized order and evaluated using the cupping protocol.

2.7.3 Sensory analysis of individual compounds flavor activity of compounds

Purified compounds were also tasted in water at concentrations presented in Table

2.8 Nuclear magnetic resonance

NMR spectra were obtained using a Bruker Avance III HD Ascend spectrometer equipped with a 5 mm triple resonance observe TXO cryoprobe with z-gradients, operating at 700 MHz for the 1H nucleus and 176 MHz for the 13C nucleus. (Bruker BioSpin,

Rheinstetten, Germany). Instruments were calibrated using the residual undeuterated solvent as an internal reference CD3OD 1H NMR = 3.31 ppm, 13C NMR = 49.0 ppm.

2.9 Data analysis

For feature selection, peak intensity was evaluated by one-way ANOVA. When a significant effect was observed (a= 0.05) post-hoc multiple comparison tests were performed. Cup scores of sensory recombination models were evaluated by two-way

ANOVA. When a significant effect was observed (a= 0.1), a student t-test was performed compared to the control coffee. JMP® Pro 13 (SAS Institute Inc. Carry, NC).

3. Results and Discussion

3.1 Untargeted chemical profiling using liquid chromatography mass spectrometry

The overall goal of this research was to identify compounds that decrease coffee cup score. Eighteen green coffee bean samples of various origins were sourced and categorized into three classes based on their cupping scores; below specialty (< 80), very good specialty (80-85), and excellent specialty (85-90), as evaluated by certified Q-graders using the SCAA cupping protocol (SCAA, 2015). LC/MS- Tof chemical profiling of fresh coffee brew resulted in the extraction of 2450 features used to predict coffee quality. An

OPLS model was built to correlate features with coffee cup score for each coffee sample

(R2X =0.76, R2Y= 0.99, Q2= 0.97). The predictive plot depicting the predictive cup score vs the true cup score revealed high accuracy of this model. The root mean squared error of prediction (RMSEP) was 0.98 and indicated that the model was able to predict coffee cup score with less than 1-point error (Figure 19a).

Multiple criteria assessments were used to select the top features highly predictive of lower cup score. Features in the top 50 variables of important predictive (VIP pred) and had p [1] < -0.1, and p (corr[1]) < -0.7 of the S-plot were selected (Figure 19b). Next,

ANOVA and subsequent post-hoc analyses were used to determine markers that have significant differences (p <0.001) in ion intensity between classes of coffee. Five features

(retention time_m/z): 7.92_333.2, 6.41_705 m/z, 6.02_351.2, 8.21_333.2, and 9.04_335.2 met these criteria. A 6th feature 9.48 _333.2 m/z was also selected as it has the same m/z as two selected features and was within the top 30 VIP pred. These six features were further evaluated by sensory recombination analysis to validate their impact on cup score.

Precursor scan experiments and daughter scans were used to conclude features as true 89

compounds rather than a fragment or adduct of a compound. Results revealed that feature

705 m/z was the formic acid adduct [M - H + 46]- of molecular ion m/z 659 [M-H]-, this compound will now be called m/z 659.

The quantification of the six markers predictive to low cup score was performed in three coffees: below specialty, very good specialty and excellent specialty. Results showed that all the compounds present in coffee brews below 6 mg/L (Table 8). Significant differences were found in the concentration of each compound between each quality classes (p< 0.01), with below specialty coffee containing the highest amounts of each compound. The concentration of compound m/z 7.92_333 ranged from 0.1 mg/L in excellent specialty coffee to 5.2 mg/L in the below specialty coffee. Similarly, compounds m/z 9.48_333, 335, 351 and 659 exhibited concentrations ranging from 0- 0.1 mg/L in excellent specialty coffee to 0.66-3.0 mg/L in below specialty coffee. All six compounds varied from 20 to 38-fold from below specialty to excellent specialty; whereas only a 1 to

2-fold difference in concentration was observed between below specialty and very good specialty coffee. Excellent specialty coffee contained very low amounts of all six compounds with levels below 0.14 mg/L and thereof, supports the relevance of the markers as highly predictive of low cup score.

3.2 Sensory validation of brew markers on coffee cup quality

The sensory impact of the selected markers on the cup score or coffee quality was validated with recombination models using five Q-graders. Recombination samples were prepared using a control coffee spiked with compounds at the levels reported in the below specialty grade coffee (Table 8). Overall, a significant effect of the compound addition on

cup scores were demonstrated (p<0.05) with four of the six compounds (m/z 659, 351, 335, and 9.48_333) inducing a significant decrease on cup score (Figure 20).

Compound m/z 659 reduced the cup score by the greatest amount from the control with 1.8 points. The addition of compounds m/z 351, 335, and 9.48_333 individually reduced cup score by 1.2, 1.1, 1 point from the control, respectively. The addition of all six compounds (combined) reduced the cup score by 1.3 points which was less than the effect of the single addition of compound m/z 659 alone (∆1.8). This suggested that the larger influence of m/z 659 on cup score was hindered when added in combination with other markers. No additive effects were observed indicating that chemical and/or perceptual mechanisms are occurring, modulating the effect of single compounds. A similar observation was reported with the addition of four compounds predictive of high quality coffee to a control (see chapter 4).

Sensory relevant compounds (RT_m/z) 9.48_333, 335, 351, and 659 were identified using high resolution MS and NMR. All compounds are in the family of ent-kaurane diterpenes. Diterpenes are usually found in large abundance in the lipid portion of green coffee beans mainly in their ester form with saturated and unsaturated fatty acids (18.5% dry matter). Free form or non-esterified diterpenes are found only at 0.4% dry matter

(Kurzrock & Speer, 2001). O-Methylcafestol, cafestol and kahweol, are the three primary diterpenes that are present in high amounts coffee (Kurzrock & Speer, 2001).

Compound m/z 9.48_333, and two non-sensory active compounds of exact mass

333.2064 corresponds to C20H29O4 (∆m/z=0.6ppm). Identification was performed in positive ESI mode, [M+H]+ = 317 and they share the same MSMS fragments 299, 271 and

253 m/z suggested the 3 compounds are isomers. NMR spectra of all isomers reveal 20

carbons, multiple chiral centers and an acid functional group as seen by the 13C NMR,

HSQC and HMBC correlations (data not shown). Additionally, high field HMBC correlations of a characteristic methylene group adjacent to a hydroxy group were observed. Altogether, data were indicative of a diterpene backbone structure of the ent- kauran-oic acid class. Further NMR experiments will be carried out to elucidate the structure of compound m/z 9.48_333.

Compound m/z 9.48_333 reduced cup score by one point in this study. Small structural changes involving isomerization of m/z 333 appear to have a major influence on flavor activity as the two other isomers (retention time_m/z 7.93_333 and 8.21_333) were not sensory active. Chemical isomers can elicit different tastes quality and have different taste threshold. For example, at equal concentrations, amino acid L-tryptophan is bitter while D- tryptophan is sweet (Solms, Vuataz, & Egli, 1965) likely due to differences in threshold values. However, differences in threshold may not guarantee different taste quality of enantiomers either (Schiffman, Sennewald, & Gagnon, 1981).

Compound m/z 335.2245 corresponds to elemental composition of C20H31O4

(Dm/z= 6.8 ppm) and was identified as ent-16α, 17-dihydroxy-kauran-19-oic acid by comparison of 1H and 13C previously reported (Pechwang et al., 2010). A similar compound, Paniculoside IV, containing one glucoside group on the same position as compound m/z 335, was previously identified (Rakotondraibe, Harinantenaina, Kasai, &

Yamasaki, 2002) and aided in the identification of 335 m/z. This compounds have been previously found in roasted coffee, however no sensory activity was reported (Obermann

& Spiteller, 1975).

Compound m/z 659. 3256 corresponds to elemental composition C32H51O14 (Dm/z=

3.3ppm). MSMS fragmentation revealed (m/z 659 -> 335) corresponding to neutral loss of

324 (two glucoside units) (Figure 21). 1H and 13C NMR data of compound 659 m/z revealed structural features very similar to compound m/z 335. With compound 659 m/z containing additional resonances indicative of two glucosides containing b-glyosidic linkages determined through signals at δ 5.39 (d, J = 8.1 Hz, 1H) and δ 4.36 (d, J = 7.8 Hz, 1H).

Compound 659 m/z, which was the most influential compound on cup score, was identified as ent-16α, 17-dihydroxy-kauran-19-diglucoside. The link between the first glucoside and the diterpenoid moiety was established through the HMBC correlation between 1’ and C-

18, and key HMBC correlations between 1” and 6’ allowed for the assignment of the second glucoside attachment point.

Lastly, compound 351.2177 m/z corresponds to C20H31O5 (∆m/z=1.7ppm) was identified as 16b,17,18-trihydroxy-ent-kauran-19-oic acid by comparing previously reported NMR and MS data (Tanaka, Murakami, Saiki, & Chen, 1985).

In addition to the cup score analysis, the by Q-graders also noted general flavor descriptors of the samples. The recombination samples addition of negative compounds was reported to mask desirable flavors such as caramel, stone fruit and lemon that were found in the control. Conversely, the recombination samples were perceived as astringent, woody, and extremely sour which was not observed in the control sample. Thus, the addition of nonvolatile markers was able to modify the retrosanal aroma and some specific taste attributes of coffee.

The flavor activity of each compound was further evaluated in isolation in buffer pH 5 and in nanopure water, at levels naturally present in below specialty coffee and 93

revealed no or little taste activity (low bitter, low astringency). No aroma was perceived for each compound either, suggesting these compounds modified the aroma attributes and cupping score through flavor modulation. It has been previously reported that weak or neutral-tasting compounds can influence flavor perception (Jelen, 2012). Other tasteless sweetness and bitterness inhibitors have also been reported (Kurtz and Fuller 1990).

Enhanced sensitivity of odor compounds have been reported when paired with subthreshold levels of taste compounds (Dalton, Doolittle, Nagata, & Breslin, 2000).

The observed changes to cup score and flavor notes of the recombination samples could be explained, in part, by a change in perception of flavor due to perceptual taste- aroma interaction enhancing the perception of undesirable flavors and masking desirable flavors. This flavor modulating phenomenon was also observed when positive correlating markers to quality were added to a coffee with low cup score (see chapter 4). In a similar manner, it has also been shown that the non-volatile ionone glycoside modulated the perceived intensity of orange and fruity attributes while increasing an undesirable green bean-like attribute in an orange beverage system (Ronningen et al., 2018).

It is unlikely that at the ppm levels the compounds were spiked into the recombination samples, that physico-chemical interactions with the non-volatile tastants changed the volatility of the coffee aroma. Tastants have been previously shown to influence orthonasal and retronasal perception regardless of physical interaction in the matrix. King et al. (2006) reported that sweeteners can strongly influence the fruity character in a beverage and could not simply be explained by the solubility of the sweetener affecting aroma release, however no mechanism explanation was given.

The SCA total cup score is composed of the sum of 10 attributes; the effect of each compound added to the recombination samples (versus control) for the individual attributes was evaluated. In the recombination samples, the three attributes acidity, flavor, and aftertaste were significantly (p<0.05) lower than the control (data not shown).

The diterpenes identified in this work did not exhibit taste activity at concentrations found in coffee and no sensory properties of the previously identified m/z 335, and 351 diterpenes have been reported to the best of our knowledge. This work demonstrates for the first time, the flavor modulating properties of these 4 diterpenes. These compounds appear to mask desirable flavor attributes while highlighting undesirable flavor attributes.

Some ent-karuene glycosides have been shown to have taste qualities. Common food additive, Stevioside and Rebaudioside A, are intense sweet tasting ent-karuene glycosides (Kinghorn & Soejarto, 1989). Structure/taste relationship of ent-kauraene glycosides have been previously investigated. Ratios of glucose unit at the 13-hydroxyl group and the 19-carboxyl group of the diterpene, Rubusoside, were previously reported to influence sweetness and taste quality. Lower ratio i.e. 1:2, was found to be less sweet and more bitter tasting (Darise et al., 1984). Bitter tasting furokurane glucoside has also identified in roasted Arabica coffee at 5.4 mg/L of filtered coffee, and have taste threshold of 30 mg/L (Lang, Klade, Beusch, Dunkel, & Hofmann, 2015). Kubo and Kubota (1979) discussed that some diterpene glycosides are bitter while the aglycone are tasteless and suggested that the sugar moiety is responsible for the perceived bitterness. Although in the current study, neither the glucoside or aglycone form elicit potent flavor activity on their own at the concentrations tested.

The diterpene-containing lipid portion of the coffee beverage has been reported to vary with different methods of preparation (Ratnayake, Hollywood, O’Grady, & Stavric,

1993). Scandinavian and Turkish style coffee brews contain higher amounts of diterpenes cafestol and kawehol compared to instant and drip filtered coffee which contain negligible amounts (Gross, Jaccaud, & Huggett, 1997; Urgert et al., 1995). Lipophilic containing compounds are mostly retained in the coffee grounds and the filter paper when drip coffee preparation method is used.

3.3 Investigation of the impact of roasting on the coffee brew markers in green and roasted coffee beans

To better understand the source of the 4 diterpenes that were reported to negatively influence cup scores in the coffee brew (Figure 20), green coffee beans and the corresponding roasted beans of all the coffee samples were evaluated. Compounds m/z 335,

351, and 659 were found to be inherent to green coffee beans. Compound 9.48_333 m/z however, was not detected in the green bean, indicating that it is likely generated during the roasting process.

The diterpene glycoside (m/z 659) was found 1-3 fold higher in green coffee beans than in its respective roasted beans suggesting it is degrading during roasting (Figure 22a).

Conversely, compound 335 was found in higher amounts in the roast beans compared to the green coffee beans (Figure 22b), suggesting that two sugar moieties of m/z 659 were cleaved to m/z 335 during roasting. Interestingly, it was observed that the amount of m/z

659 in the green bean did not equate to the amount of 335 m/z being generated in the roasted bean. It is likely that m/z 659 might also be transforming into compounds m/z 351

and m/z 9.48_333. This is in agreement with the high relative amount of m/z 351 in the roasted bean compared to green beans and the generation of m/z 9.48_333 upon roasting

(Figure 23).

There is disagreement in literature on the impact of roasting on diterpene content.

Known diterpenes cafestol, kahweol, and 16-O-methylcafestol, were found degraded with increasing roasting severity (Kölling-Speer et al. 1999;Wurziger and Purazrang 1972), while other works have demonstrated their stability over roasting (Urgert et al., 1995). The disagreement could be due to the different extraction methods, various detection methods for quantitation, and the ambiguity between reporting esterified or free form diterpenes over the years. The amounts of free diterpenes analyzed in this study were shown to increase with roasting.

Factors other than roasting can also be influencing the concentration of diterpenes between the farm to cup. Free form diterpenes were found at levels < 200 mg/dry matter which is around 0.7-3.5 % of the total diterpene in green beans. Concentration of diterpenes has been shown to be greatly affected by genetic factors, as studied in traditional and modern cross varieties (Kitzberger et al., 2013). No differences have been found in the levels of total diterpenes in healthy and defective green coffee beans (Goncalves et al.

2009). Ratio of cafestol/kahweol in green beans have been used to study cup score

(Novaes, Oigman, De Souza, Rezende, & De Aquino Neto, 2015). Maier (1981) suggested diterpene glucoside can be cleaved by B-glycosidase during green bean storage.

Pearson correlation between relative concentration in the green beans and roasted beans showed that m/z 335 and 351 have significant correlation, r =0.61, 0.70, respectively

(p<0.0001) (Figure 23 a,b). Based on the observed linear relationship between green coffee 97

bean and roasted bean, these two compounds could be used directly as a quality marker for green coffee bean selection.

4. Conclusions

Untargeted flavoromic analysis successfully identified highly predictive compounds of below specialty grade coffee. Sensory recombination models validated the sensory impact of four diterpenes which significantly decreased cup score as evaluated by

Q-graders. Two of the four compounds were also identified as quality indicators in the green coffee beans. These findings provide a valuable tool to facilitate better selection and negotiation for producers and industry. Insights to the source of markers of poor quality can lead to investigation of mitigation strategies in future studies.

Excellent Specialty PVery good Specialty Below Specialty

re R2X 0.769 R2Y 0.997 2

True cup score (points) cup score True Q 0.979 di RMSEP 0.98

ct Predicted cup score (points) iv

e b) pl

ot re

p(corr[1]) of di p ct

re iv p[1]

Figuredi 19 (a) Predictive plot of predicted cup score vs true cup score createde from Orthogonal partial least square model (OPLS) and (b)S- plot. Each dot represents chemical feature and red color dots indicate ct selectedpl features of interest. e ot d of c p u 99 re p di sc ct or e

Concentration (mg/L) ± standard deviation Retention m/z Transition time Excellent Very good Below specialty specialty Specialty 7.92 333.2 317.2*à253.2 0.14±0.0c 4.87±0.26b 5.26±0.25a

8.21 333.2 317.2*à253.2 0.00±0.0c 2.55±0.14b 3.08±0.09a

9.04 335.2 301.2*à255.2 0.11±0.01c 1.10±0.16b 2.25±0.09a

6.02 351.2 351.2à321.2 0.09±0.0c 0.91±0.03b 2.06±0.03a

6.33 659.2 659.2à335.2 0.10±0.0c 1.09±0.05b 2.42±0.07a

9.48 333.2 317.2*à253.2 0.02±0.01c 0.55±0.04b 0.66±0.02a

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85.0 84.0 * Control 83.0 * * ** * 82.0 81.0 80.0 79.0 Total cup score cup Total 78.0 77.0 76.0

1. Control

5. Recombination6. Recombination m/z7. 335Recombination m/z 351 m/z 659

2. Recombination3. Recombination m/z4. 7.92_333 Recombinatio m/z 8.21_333 m/z 9.48_333

8. Recombination m/zCalculated all compounds average of samples 4,5,6,7 Calculated average of samples 2,3,4,5,6,7

Figure 20 Mean total cup score of control and recombination [control+ compound(s)]. *indicate significant difference from control according to student t-test (α=0.05), ** (α=0.005)

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335.2

659.3

705.3

[M - H + FA]-

Intensity Relative

m/z

Figure 21 MS fragmentation of m/z 659, and its formic acid adduct m/z 705

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a) m/z 659 Green bean

4 Roasted bean 3 2 1 0 Relative concentration Relative 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 sample

High cup score

b) m/z 335

0.008

0.006 0.004 0.002

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Relative concentration Relative sample

Figure 22 Relative concentration in green bean (green) and in roasted beans (grey) of compound (a) m/z 659 and (b) m/z 335

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m/z 335 r = 0.61 0.0012

0.001

0.0008

0.0006

0.0004 Excellent Specialty Very good Specialty 0.0002 Below Specialty 0 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 Relative conc. in green bean green in conc. Relative Relative conc. in roasted bean

m/z 351 r = 0.70 2 1.8 1.6 1.4 1.2 1 0.8 0.6 Excellent Specialty 0.4 Very good Specialty 0.2 Below Specialty 0

Relative conc. in green bean green in conc. Relative 0 2 4 6 8 10 12 Relative conc. in roasted bean

Figure 23 Relative concentration in green bean and in roasted beans of compound (a) m/z 335 and (b) m/z 351

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Chapter 6. General discussion and conclusion

105

General discussion

The research presented in this dissertation demonstrates the successful application of untargeted flavoromic analysis for the discovery of chemical drivers of coffee quality as it relates to flavor properties. Initially, this work demonstrated that current industrial methods to evaluate quality of green coffee beans are not adequate to predict flavor quality of the brew and showed the need for a more reliable tool.

Coffee quality needs to be evaluated for the overall flavor experience that involves taste, aroma, and somatosensation. Flavoromics combines sensory evaluation with untargeted chemical profiling in order to identify marker correlated to flavor changes.

Sensory evaluation is then performed on statistically correlated compounds though sensory recombination models. This technique enables the discovery of novel compounds influential to sensory properties by either direct sensory response or though modulation effects. In this research, the cup score developed by the Specialty Coffee Association

(SCA) was used to define coffee quality. This protocol was created as a way to evaluate quality based on ten objective criteria for coffee brew that encompass all flavor modalities.

In order to capture the chemistry of a regular cup of coffee, LC/MS/Tof chemical fingerprint of drip coffee brew was collected. This brewing method was selected as it represents the most common experience for coffee consumption. However, prior literature on known diterpenes, for instance, suggested that brewing conditions such as time, temperature, filter material, and basket geometry had a drastic effect on their concentration in the brew. Diterpenes are found in negligible amounts in filtered coffee in comparison to non-filtered, such as Turkish style coffee brew, in which they can reach up to 3.9 mg/ cup

(Urgert et al., 1995). In this work, coffee diterpenes were identified as markers of lower 106

quality coffee; hence, suggested future work could evaluate the impact of coffee brewing methods such as espresso, French press, or cold brew on these markers of quality. This could also be valuable for the isolation and purification steps to maximize the extraction yields. Additionally, direct extraction of roasted coffee beans could be optimized for compounds of interest. For instance, recent technologies such as microwave assisted and supercritical CO2 extractions, have been successfully applied for the extraction of chlorogenic acid and diterpenes (Araújo & Sandi, 2007; de Azevedo et al., 2008; Upadhyay

& Jagan Mohan Rao, 2013).

Flavoromics is a new area of research and limited amount of resources regarding chemical feature selection process is available. Out of the thousands of features collected only a few undergo further investigation. This step needs to be considered methodically as time and cost considerations are involved for the subsequent steps that include fractionation, sensory validation and structural identification. This work demonstrated for the first time the successful use of multiple criteria for features selection and was able to reduce the number of features, from 2945 to 10 among which 7 had a significant impact on cup score. A variable of importance (VIP) value > 1 was first used to filter the less contributing features to the model. However, when multicollinearity is present, such as in this study, the sole use of VIP is not sufficient to reduce the number of variables and multiple criteria assessments should be used (Chong & Jun, 2005). Therefore, additional filters were applied by setting cut off values for covariance (fold-change of feature intensity between samples) and correlation to cup score, as represented on the S-plot. Loose criteria can result in the selection of a higher number of features which may not be relevant; whereas too strict criteria may not capture enough information. Review of the feature

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selection criteria in Chapter 4 showed that the most influential compound on sensory properties (m/z 437) had a lower VIP than m/z 671, which was less influential on cup score, demonstrating that high VIP does not guarantee sensory relevance. In brief, VIP value is weighted by the feature’s correlation to the Y variable and the variance of this compound in all the samples, with more emphasis given on the latter. However, according to psychophysics curves of perception, intensity of the stimulus is not linear to concentration changes (Stevens, 1969). This is also aligned with the results presented in Chapter 5, in which compounds showing a greater fold change in concentration did not necessarily induce greater sensory changes. These examples demonstrate the importance of combining multiple selection criteria to minimize the possibility of excluding sensory relevant compounds, as well as the importance of sensory validation to determine sensory relevance.

Using the flavoromics approach, two classes of compounds with significant sensory relevance to coffee quality were discovered; diterpenes and chlorogenic acid derivatives.

Their flavor modulation properties and the mechanism behind their activity are of value for future research in order to apply this knowledge to produce higher quality coffee products. Seven compounds predictive of coffee quality that showed significant impact on coffee brew flavor, individually and in combination, were purified and characterized. Three novel chlorogenic acid derivatives were found highly predictive of high cup score coffee:

3-O-caffeoyl-1-O-3-methylbutanoylquinic acid (m/z 437), 3-O-caffeoyl-1-O-3- methylbutanoyl-1, 5-quinide (m/z 419), and unknown chlorogenic acid derivative (m/z

671). Notably, these compounds, not flavor active on their own, were able to modify the retronasal aroma perception, by bringing new notes described as caramel, bright and lemon

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fruit. These compounds exhibited a common structural moeity, a 3-methylbutanoic acid.

Similarly, four compounds belonging to the diterpene family, predictive of lower cup score, did not show any flavor activity on their own, yet significantly decreased the cup score of a low cup score brew. Altogether, this work demonstrated that flavor perception is a complex sensation that, beyond the input of individual stimuli, also involves chemical and perceptual interactions. Prior literature demonstrated, for instance, the role of subthreshold compounds on flavor perception and can potentially be related to our findings. Enhanced sensitivity of odor by taste at subthreshold levels has been demonstrated (Dalton et al.,

2000). It is possible that the markers were tested at below average detection threshold

(subthreshold), thus did not have direct response, yet still modulated the flavor of the coffee. It has also been previously observed that weak or neutral-tasting compounds can influence flavor (Ley et al., 2006). Additionally, chlorogenic acid derivatives have been known for their flavor modifying properties such as sweetness enhancing (Minjien, Chieng,

Alex, Hausler, Han, 2002; Upadhyay & Jagan Mohan Rao, 2013) and bitter inhibiting

(Riemer, 1993) which is aligned with our findings. Physico-chemical mechanisms can also occur and modify the release of volatile aroma compounds from the matrix. In one study, the odor threshold of trichloroanisole, a common coffee off-flavor, was significantly higher when added to a coffee matrix in comparison to water (J.C., Spadone, G. Takeoka, 1990).

This potentially indicated sensory changes due to the complexity of coffee odor impacting the discriminative ability of the panel on a perceptual level, or chemical interactions between trichloroanisole and the matrix. An increased volatility of coffee aroma compounds by phenolics and chlorogenic acids has been previously demonstrated (King &

Solms, 1982). However, in the current study, it is unlikely that, at the level added, these

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compounds affected the aroma release on a physico-chemical level and suggests their potential as ‘true’ flavor modifiers. Future work could evaluate the flavor threshold of these compounds and investigate their flavor modulation properties. Discovery of true flavor modulators has high potential to be used for the development of clean label ingredients and products with applications not limited to coffee.

This research defined the term coffee “quality” with respect to the criteria set by the Specialty Coffee Association (SCA). Cupping by professional evaluators have been the primary way coffee beans are monetarized in the industry. However, high scores from Q- graders may not necessarily correlate to high liking from consumers. In wine, the lack of agreement between expert and consumer ratings has been shown and liking was found to be dependent on the consumers’ experience, their wine knowledge and sensory skills

(Schiefer & Fischer, 2008). Along these lines, it has been suggested that the notion of quality cannot be defined by a fixed set of few attributes but should be specific to each consumer’s preference and their coffee consumption habits (Geel, Kinnear, & de Kock,

2005). Flavoromics could be applied to understand chemical drivers of consumer preference of coffee products. Further descriptive analysis could be also be performed by trained professionals to refine the knowledge of which specific aroma or taste attributes are associated with higher or lower cup scores. This information is valuable for the coffee industry and would support the development of products tailored to specific consumer groups.

Discovery of coffee markers opens several opportunities for the coffee industry to produce higher quality products. These findings could be used to develop guidelines for roasting and coffee brewing conditions to optimize the formation of valuable compounds.

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In order to achieve these goals, more work is needed to understand the formation and degradation of quality markers during the roasting process. For example, the most relevant compound to quality, m/z 437, appeared to be degraded during the roasting process, producing compound m/z 419 which has a lower sensory influence on cup score. Studies on roasting conditions at various temperature, time, or using different roaster types, could lead to roasting conditions to produce optimal flavor. Mitigation of diterpenes could be performed using various roasting and brewing techniques. Despite being associated with low quality, the appropriate level of diterpenes needs to be determined as complete removal could impact the overall flavor of coffee.

Lastly, this research examined coffee flavor chemistry using LC/MS to focus on low and non-volatile compounds. However, flavor is defined as the integration of taste, aroma and somatosensation, hence it would be valuable to complete this research using the

GC/MS platform to characterize volatile compounds. Integrating both streams of data might lead to novel discoveries and a better overall understanding of coffee quality.

Conclusion

This dissertation demonstrates the use of untargeted flavoromic analysis to characterize chemical drivers of coffee brew quality. Seven novel flavor modulating compounds were discovered, including three chlorogenic acid derivatives and four ent- kaurane diterpene compounds. According to professional coffee cuppers, the addition of these compounds to the brew significantly changed the quality score by modulating flavor properties. These findings can be used to provide guidelines for roasted bean process

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optimization as well as green coffee bean selection and breeding strategies with regards to flavor quality.

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Appendix A. Quality classifications of 18 coffee samples based on the average cupping

score from five certified Q-graders

Quality Coffee # Average Cupping Score Classification 1 87.8 2 86.4 Excellent 3 85.9 Specialty 4 85.3 5 84.3 6 83.6 7 83.4 8 83.4 Very good 9 82.9 Specialty 10 82.4 11 81.0 12 80.9 13 80.0 14 79.8 15 79.6 Below 16 76.6 Specialty 17 75.4 18 72.6

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