a State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, ChinabUniversity of Chinese Academy of Sciences, Beijing 100049, China
As the most consumed beverage in the world, the material basis of the sensory quality for roasted coffee beanshas always received much attention. The objective of the present study was to clarify the physical morphologychanges, main chemical ingredients and cupping scores of arabica coffee beans of different roasting degrees, byscanning electron microscopy (SEM), nuclear magnetic resonance (NMR) and sensory analysis, respectively.Statistical analysis of the data by multivariate analysis demonstrated that trigonelline, sugars, malate, quinicacids, γ-butyro-lactone and acetate have the potential to be new roasting markers. Additionally, in all the sensoryindicators, body and acidity were found to be susceptible to roasting degree. Basing on cluster heatmap andsensory molecular network, the complex relationships between sensory indicators and ingredients were dis-cussed. The results of partial least squares regression (PLSR) showed that the content of the main coffee in-gredients can be used to predict the body score.
1. Introduction
As the second most frequently consumed drink after water, coffee,which has been grown in more than 70 countries, is closely related tothe lives of billions of people around the world and has become thesecond largest traded commodity worldwide after petroleum (Butt &Sultan, 2011). The quality of coffee beverages is influenced by multiplefactors such as altitude, soil, climate, processing procedures, roastingdegree, and brewing methods; among all the factors, roasting plays akey role in coffee beverage quality (Dutra, Oliveira, Franca, Ferraz, &Afonso, 2001).
Once green beans are roasted, intricate physical and chemicalchanges occur. Physical changes are mainly reflected in the dramaticchanges in the shape, water content, density, color, and internalstructure of beans (Schenker, Handschin, Frey, Perren, & Escher, 2000).For the observation of the microscopic appearance of coffee beans, SEMis the most effective method. Although Schenker et al. (2000) have triedto observe the structure of roasted coffee beans using SEM, the mor-phology of coffee beans at different roasting levels has not been clearlyexplained. The chemical changes are marked with Maillard reactionand caramelization reaction to produce pleasant or unpleasant
substances, which can directly decide the quality of the beverage(Baggenstoss, Poisson, Kaegi, Perren, & Escher, 2008; Steen, Waehrens,Petersen, Munchow, & Bredie, 2017, Liu et al., 2019). Previous studieshave shown that some of the main components, especially sugars andchlorogenic acids (CGAs) (Farah, De Paulis, Moreira, Trugo, & Martin,2006; Sittipod, Schwartz, & Paravisini, 2019), will be reduced by par-ticipating in the reactions, however, the main chemical compositionchanges at different roasting stages have not been well elucidated.
Beverage quality can be usually determined by sensory analysis inwhich a panel of trained, specialized “cuppers” evaluates coffee qualityusing either a table with scoring values (scoring method) or a sensorylexicon (descriptive method) (Worku, Duchateau, & Boeckx, 2016). Themost widely adopted evaluation standard is the “Coffee Cupping Protocolof Specialty Coffee Association of America”, which includes ten sensoryindicators: aroma, flavor, aftertaste, acidity, body, overall, clean up,uniformity, sweetness and balance.
Given that sensory analysis is affected by a variety of subjectivefactors which may lead to injustice (Romano et al., 2014; Worku et al.,2016), a theoretical alternative method is to find the material basis ofthe abovementioned sensory indicators, and based on these substances,to objectively evaluate the quality of coffee. In the past 30 years,
https://doi.org/10.1016/j.foodchem.2020.127329Received 11 January 2020; Received in revised form 24 May 2020; Accepted 10 June 2020
⁎ Corresponding author at: State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences,Kunming 650201, Yunnan, China.
considerable works have been devoted to the discovery of aromaticvolatiles produced during roasting, and have led to the identification ofover 1000 volatile organic compounds (VOC) (Colzi et al., 2017; Barie,Bucking, Stahl, & Rapp, 2015; Lindinger et al., 2008). In addition to theresearch on aroma, there are also reports on the material basis ofacidity, body and bitterness (Santos, Lopo, Rangel, & Lopes, 2016;Blumberg, Frank, & Hofmann, 2010; Frank, Zehentbauer, & Hofmann,2006; Rizzi, Boekley, & Ekanayake, 2004). Due to the intricacies of theroasting products, there is still a long way to match the indicators ofsensory evaluation with the trace ingredients produced in roasting;actually, even the relationship between the main ingredients and sen-sory indicators has not been fully understood, which makes it hard torealize the coffee quality evaluation model based on chemical compo-sitions. To best of our knowledge, there is no precedent for directlyusing the content of chemical components to predict the sensory score.
In the past few decades, in addition to direct use of instrumentationcombined with expert assessment to find flavor components, re-presented by gas chromatography–olfactometry-mass spectrometry(GC-O-MS) (Zou, Liu, Song, & Liu, 2018), instrumental detection com-bined with multivariate analysis has also been tried for the excavationof flavor substance and quality control of roasted coffee beans (Sittipodet al., 2019). For example, proton transfer reaction-mass spectrometrywas employed in the discrimination of coffee beans of different roastingdegree (Romano et al., 2014), and near infrared spectroscopy wasexploited as an analytical tool for on-line monitoring of acidity duringcoffee roasting (Worku et al., 2016).
As complicated the aromatic contents of coffee are, nearly all of thevolatiles are derived from nonvolatile ingredients of green beans, whichbreakdown and react during roasting, forming a complex mixture(Hashim & Chaveron, 1995). Therefore, in the current study, the mainchemical ingredients were chosen as research subjects to clarify theirproduction and changes during roasting, as well as assessing the linkbetween these changes and the quality of coffee beverages. Specifically,the microscopic appearance and main chemical ingredients of coffeebeans of different roasting degree were clarified with SEM and 1H NMR,respectively. On the other hand, influences of roasting on sensory in-dicators were studied. Further, an in-depth study of the relationshipbetween major chemical ingredients and sensory evaluation was carriedout and a sensory molecular network based on major chemical in-gredients was constructed. Finally, an exploratory study was conductedto build a sensory scores prediction model based on the characteristicsignal of the main coffee ingredients.
2. 2.Material and methods
2.1. Materials and instruments
D2O (99.9%) for NMR detection was purchased from Saen ChemicalTechnology (Shanghai) Co., Ltd. Samples were ground by a JiuyangJYL-B060 grinder. Centrifugation was performed on an 80–2 benchtopcentrifuge (Shanghai Medical Devices Co., Ltd.). The Bruker DRX-600 MHz NMR instrument (Bruker, Zurich, Switzerland) was used todetect 1H NMR spectra. SEM images were acquired with a Sigma300(CARL ZEISS) field emission scanning electron microscope. The malicacid (99.0%), citric acid (99.0%), and quinic acid (98.0%) for 1H NMRspectra confirmation were purchased from J&K Chemical Co., Ltd.Trigonelline (98.0%) was purchased from Acmec Biochemical Co. Ltd,Shanghai. Sucrose (98.0%), glucose (98.0%) and xylose (98.0%) werepurchased from Aladdin Reagent (Shanghai) Co., Ltd. Caffeine (99.0%)was prepared and tested for purity by HPLC.
2.2. Coffee bean samples
All sample coffee beans for cupping and 1H NMR detection, in-cluding 24 light roasted bean (LRB) samples, 24 moderate roasted bean(MRB) samples and 24 dark roasted bean (DRB) samples, were
produced in Pu'er City, Yunnan Province, China and all belong toArabica (Catimor) species. The roasting degree was mirrored by thecolor value of the beans: LRB, 80–95; MRB, 60–75; DRB, 40–55.
2.3. Morphological characterization of coffee beans with different roastingdegrees
For SEM characterization, three green beans, MRBs, and DRBs withuniform shape were selected, respectively. The beans were cut in thedirection of the axis to expose the inner section, and the inner sectionwas cut into 1–2 mm thick slices. For each bean, 2–3 sections weretaken for SEM observation. The surface was uniformly plated withsilver and then placed under a SEM (CARL ZEISS) to collect images.SEM magnification was ranged from 40 to 600 times.
2.4. Detection of major chemical constituents by 1H NMR
2.4.1. Extraction solvent screeningBefore formal 1H NMR analysis, the extraction solvent was first
screened. The solvents of four different polarities, n-hexane (b. p68.7 °C), ethyl acetate (b. p 77.2 °C), ethanol (b. p 78.4 °C), and water(b. p 100.0 °C), were used to extract coffee of three roasting degrees(from the same green bean sample), respectively. In total, 500 g beansof three roasting degrees were crushed and passed through an 80 meshsieve. For each individual experiment, 50 g (M0) of coffee powder and200 mL (V0) of extraction solvent were added to a 500 mL Erlenmeyerflask, and extracted in a 60 °C water bath for 1 h with the aid of ul-trasound. After that, 10 mL (V1) of the extraction solution was removedand concentrated under vacuum. The weight of the concentrate (M1)was accurately weighed and the extraction rate (Y) was calculated bythe following formula:
= ×Y V MV M
100%0 1
1 0
2.4.2. 1H NMR signal acquisitionSix samples were randomly selected from three roasting degree bean
samples, respectively. A total of 18 samples were obtained for 1H NMRanalysis. For each sample, the beans were pulverized and sievedthrough an 80 mesh sieve to obtain uniform powder. Accuratelyweighed samples of 40.0 mg were placed in a 100 mL centrifuge tube,and 800 μL D2O was added. The samples were treated ultrasonically ina water bath at 80 °C for 1 h and centrifuged at 4000 rpm for 15 min.Then, 450 μL of the supernatant solution was stored in a nuclearmagnetic tube at a constant temperature of 4 °C. The collection of 1HNMR data was performed on the same day as sample extraction on aBruker DRX-600 MHz NMR instrument. 1H NMR sampling parameterswere as follows: sampling data point 655536; peak width: 12,019 HZ;acquisition time: 2.7 s; relaxation time 10 s; number of scans 4.Presaturation method was used for water peak suppression.
1H NMR data was displayed in MestReNova and the main chemicalingredients were identified against literature and standards (Wei,Furihata, Hu, Miyakawa, & Tanokura, 2011). The characteristic che-mical shifts of the main compounds were integrated to obtain theirabsolute peak areas, and the result are shown in Table S2.
2.5. Sensory evaluation
The sensory analysis was conducted by four Q-Grader paneliststrained on the SCAA cupping protocol (Association, 2015). A total of 10indicators including aroma, flavor, aftertaste, acidity, body, balance,overall, clean cup, uniformity and sweetness were scored during thesensory evaluation. The sensory indicators were evaluated on a scalefrom 6 to 10 with 0.25 increments and the sum of all ten sensory in-dicators’ scores was the final score of an individual sample.
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2.6. Data processing
Multivariate analysis was performed using SIMCA 14.1 (Umetrics,Sweden). For the main chemical ingredients data set (n = 18), cen-tralized was performed for the 1H NMR absolute peak area, then,principal component analysis (PCA) was performed. The chemical in-gredients data set was centered and normalized for partial least squaresdiscriminant analysis ((PLS-DA)). For the cupping score data set(n = 72), the data were centered and normalized, after which, PLS-DAwas performed to explore the effect of roasting degree on sensoryevaluation. The data set for the body score prediction was also cen-tralized before PLS modeling. Analysis of variance (ANOVA) was con-ducted for the significance examination of all the models.
The Pearson correlation matrix between 7 cupping indicators andcompounds was calculated by SPSS 16.0. The cluster heatmap wasdrawn based on the numpy, matplotlib and seaborn packages in python3.5, and the sensory molecular network was drawn in Cytoscape 3.6.1.
3. Results and discussion
3.1. The production and change of coffee flavor substances
3.1.1. Changes in microstructure and main chemical ingredients duringroasting
The change in microstructure caused by roasting has major influ-ence on the final quality of coffee beverage. During roasting, the greenbeans are heated at 200–240 °C for 10–15 min. External temperature,roasting time, coffee bean size, shape, water content and other factorswill affect the change of coffee microstructure and the occurrence ofchemical reactions, thus affecting the generation and release of flavorsubstances.
Due to unusually thick cell walls and the tight alignment between
cells, the green coffee beans are very hard (Fig. 1A, 1D), so they can beregarded as aggregates of microreactor units, which provide consider-able support during roasting pressure buildup (Baggenstoss et al.,2008). The continued increase in temperature is accompanied by de-gradation of flavor precursor compounds such as sugars, amines, andCGAs to produce a large amount of flavor compounds. It is generallybelieved that when the temperature rises to approximately 154 °C, theMaillard reaction between sugars and amines begins, and the Maillardreaction products, coffee melanoidins, make the color of the beansdarker.
The resulting volatile flavor compounds and CO2 accumulate in thecavity inside the coffee bean, causing pressure inside the beans to rise.Roasted coffee beans can withstand theoretical pressures up to 16 bar(Baggenstoss et al., 2008). When the vapor pressure and the CO2
pressure inside the beans build up, the “1st crack” occurs. The “blowholes” produced at this time become the outlets of the flavor com-pounds inside the beans. As shown in Fig. 1B, 1E, after the “1st crack”(MRB), a loose pore-like structure was observed under SEM, the mi-cropores gradually became dense from the periphery to the middle, andan obvious crack appeared in the middle. Oil droplets can be observedaround and at the bottom of the pore structure.
The combustion causes the CO2 to continue to accumulate. Afterreaching a certain pressure, the “2nd crack” occurs and the interiorbegins to coke or carbonize. Previous study found that although therewere high pressure conditions during the roasting process, no signs ofcell wall rupture were observed in the SEM image (Baggenstoss et al.,2008; Schenker et al., 2000), which may be due to the change of the cellwall from the glass state to a more elastic state at high temperatures.From our observation, after the “2nd crack”, the pore size did notchange significantly, but the crack in the middle became obviouslylonger, the oily substance on the surface increased, and the cell struc-ture in the center of the bean began to be destroyed (Fig. 1C, F).
Fig. 1. SEM image of coffee beans. Green bean (A, D). Medium roasted beans (between 1st crack and 2nd crack, B, E). Dark roasted beans (after 2nd crack, C, F). (Forinterpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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3.1.2. Exploring changes in the main chemical ingredients based on 1HNMR
To further clarify the changes in the main chemical ingredients inbeans during roasting, 1H NMR was adopted in the analysis of roastedbeans. First, the extraction solvent screening experiment was carriedout. n-hexane, ethyl acetate, ethanol and water were used to extractcoffee bean of three roasting degrees, respectively, and their extractionrate and 1H NMR profiles are shown in Figs. S4 and S5, respectively.Interestingly, the extraction rate of n-hexane, ethyl acetate and ethanolincreased with the roasting degree, which may be due to the reaction ofcarbohydrates during roasting to form small or moderately polar mel-anoidins. Although these macromolecular compounds do not showobvious 1H NMR signals, their content can increase with roasting de-gree, resulting in an increase in the organic solvent extraction rate. Theextraction rate of water was much higher than that of the other threesolvents, and no significant difference was detected in the water ex-traction rates of the three roasting degrees. 1H NMR spectra of theextracts of the four solvents showed that the water extract containedthe most abundant types of compounds. Sugar, caffeine, CGAs, andtrigonelline are all very water soluble (Upadhyay, Ramalakshmi, & Rao,2012; Jeszka-Skowron, Zgola-Grzeskowiak, & Grzeskowiak, 2015), butare very low soluble in n-hexane, ethyl acetate. Although caffeine andCGAs have good solubility in hot ethanol, sugars, as the most importantflavor precursor compounds in coffee, are almost insoluble in ethanol.Among the four solvents, water has the highest boiling point, whichmeans that when water is used as extraction solvent, not only can themain chemical components in the coffee be extracted to the utmostextent, but also the error caused by the evaporation of the solvent canbe reduced. In addition, hot water is also the extraction solvent in coffeebrewing. Therefore, although a small portion of the fat-soluble com-ponent may be neglected, in consideration of various advantageous,water was finally determined as the extraction solvent for the nextexperiment.
Wei et al. (2011) proposed the rapid identification of the mainchemical components in roasted coffee beans using NMR technology,which was successfully applied by Ciaramelli, Palmioli, and Airoldi(2019) to identify the variety and origin of coffee. Considering that thecompounds in roasting are substantially dynamic, and the change speedof different compounds vary from their chemical or physical properties,we intend to further reveal the changes profile in the main chemicalingredients of roasted coffee beans using 1H NMR technology.
Six samples were randomly selected from each roasting degree, anda total of 18 samples were obtained for 1H NMR detection. Based on thedetection method of Wei et al. (Fig. S6), and further confirmed with the1H NMR data of the standards (Fig. S7), the characteristic signals ofsugars, CGAs, caffeine, trigonelline, quinic acids, acetate, formate, ci-trate, malate, γ-butyro-lactone, choline, 2-furyl-methanol and N-me-thyl-pyridinium were identified from the extracts (Fig. 2, Table S1).
The pyrolysis of sugars and polysaccharides gave rise to a widerange of volatiles including aliphatic carbonyls, alcohols, acids, furansand cyclic diketones (Steen et al., 2017). Simultaneous interaction witha nitrogen-containing fragment could give rise to pyrazines, pyridines,pyrroles and, in some cases, imidazoles (Liu et al., 2019). Althoughthese reactions can violently occur in lightly roasting degree, obviouslythey were not going thoroughly, for there were plenty of sucrose andother sugars remaining in LRB samples (Fig. 2). The sugars were sub-stantially degraded after moderate roasting, and completely dis-appeared in DRB samples.
CGAs can be mainly divided into caffeoylquinic acids (CQAs) with 3isomers (3-, 4-, and 5-CQA), feruloylquinic acids (FQA) with 3 isomers(3-, 4-, and 5-FQA), and dicaffeoylquinic acids (diCQAs) with 3 isomers(3,4-diCQA; 3,5-diCQA; 4,5-diCQA) (Moeenfard, Rocha, & Alves,2014). They are lost as a consequence of the thermal breakage ofcarbon-carbon covalent bonds, resulting inisomerization in the initialroasting stages and epimerization, lactonization, and degradation in thelater stages (Farah et al., 2006; Sittipod et al., 2019). The decreasing
order of CGA isomers was CQAs > diCQAs > FQAs (Moon, Yoo, &Shibamoto, 2009). As shown in Fig. 2, although they degraded moreslowly than sugars, CGAs were significantly reduced as the roastingdegree increased, and some CGAs remained in DRB. Contrary to CGAs,the quinic acids (quinic acid and syllo-quinic acid), as the main de-gradation products of CGAs, increased with roasting degree. Accordingto Moon and Shibamoto (2010) quinic acids will degrade with darkroasting to form catechol, phenol, benzonic acid and 2-furyl-methanol;however, in this study, the degradation was not detected.
There were different findings in the change in caffeine duringroasting, Rodarte, Abrahao, Pereira, and Malta (2009) found that caf-feine was not degraded at any roasting degree. However, according tothe research result of Hecimovic, Belscak-Cvitanovic, Horzic, andKomes (2011) based on Arabica and Robusta coffee beans, LRB con-tained the highest overall content of caffeine, which exhibited a de-crease with intensified roasting. Our results showed that during darkroasting, although not particularly obvious, caffeine did have a de-creasing trend (Fig. 2).
Previous studies have reported that trigonelline degraded duringroasting (Rodarte 2007; Baggenstoss et al., 2008), and was largely de-composed upon thermal treatment into N-methylpyridinium, nicotinicacid and methylnicotinate (Lang, Yagar, Eggers, & Hofmann, 2008;Stadler et al., 2002). The 1H NMR spectra showed that trigonellineexperienced no significant change in LRB or MRB samples, and theabovementioned degradation mainly occurred in DRB (Fig. 2). Similarto trigonelline, choline was experienced significantly reduce after darkroasting.
The N-methylpyridine, including 2-methyl-; 2,3-dimethyl-; 2,5-di-methyl-; 2,6-dimethyl-; trimethyl-and tetramethyl pyradine were de-tected in coffee beans, among which, 2-methylpyridine had the largestratio (Hashim & Chaveron, 1995). They were reported as a kind ofMaillard reaction products of sugars and amines or the degradationproducts of trigonelline (Hashim & Chaveron, 1995). N-methylpyridinebegan to form and accumulate during the medium roasting phase of thesample, and achieved the highest content after dark roasting (Fig. 2).Additionally, acetate and γ-butyro-lactone significantly increased inDRB samples.
3.1.3. Roasting degree marker mining basing on multivariate analysisTo accurately describe the influence of roasting on the content of
the main coffee ingredients, the characteristic chemical shift intervalsof the compounds were integrated to obtain the absolute peak areainformation of the compounds (Table S2), and further PCA and PLS-DAanalysis was conducted.
PC1 and PC2 of the PCA analysis model explained 80.0% and 16.4%of variance, respectively. In the score plot, three groups of samples weredivided into three clusters from bottom left to top right in increasingorder of roasting (Fig. S8), and no outlier existed in all samples. ThePLS-DA model fit four principal components (p < 0.01). The goodnessof fit of the model was R2X = 0.938, R2Y = 0.944, and the predictivepower was Q2 = 0.866. The score plot of the PLS-DA model showedthat the samples of three groups were well distinguished from left toright in descending order of roasting (Fig. 3A). The correspondingloading plot showed that compounds marked with blue, includingCGAs, trigonelline, sucrose, other sugars, choline, and caffeine, had thehighest content in LRB, while N-methyl-pyridinium, quinic acids, γ-butyro-lactone, malate, 2-furyl methanol and acetate, which aremarked with brown, had the highest content in DRB.
The clarification of the changes in the main chemical ingredients ofroasted beans is also meaningful to the selection of roasting degreemarkers. Although detecting the external color of the beans is still themain means of determining roasting degree (Ruosi et al., 2012), con-sidering the influence of geography, climate, variety and other factorson the flavor precursor compounds, it is hard to become an unifiedstandard; thus, more factors must be concluded to conduct more ac-curate and unified assessment. Due to their characteristic changes
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during roasting, CGAs, free amino acids, alkylpyrazines, 2-furfuryl al-cohol and 5-methylfurfural/2-acetylfuran ratio have been proposed asroasting markers (Dutra et al., 2001; Hashim & Chaveron, 1995;Dorfner, Ferge, Yeretzian, Kettrup, & Zimmermann, 2004; Ruosi et al.,2012). The variable importance in projection (VIP) value of the PLS-DAmodel, first published by Wold and Johansson (1993), is a parameter ofscreening biomarkers in metabolomics, and the variable with VIP valuegreater than 1 can be considered as a differential variable betweengroups (Farres, Platikanov, Tsakovski, & Tauler, 2015). Among the VIPvalues corresponding to the first component of the current PLS-DAdiscriminant model, nine (class) compounds, including N-methyl-pyr-idinium, quinic acids, CGAs, γ-butyro-lactone, other sugars, trigonel-line, 2-furyl-methanol, sucrose, and acetate had VIP values greater than1 (Fig. S9), indicating that these compounds play important roles in thedifferentiation of roasting degree.
In addition to the roasting markers already reported in previousarticles, from the results of PLS-DA analysis, other ingredients, such astrigonelline, sugars, malate, quinic acids, γ-butyro-lactone and acetatealso have the potential to be markers of roasting degree. As discussed insection 3.1.2, the contents of sugars (sucrose and other sugars) andquinic acids in beans of different roasting degree were varied sig-nificantly, making them ideal markers for distinguishing roasting de-gree. Although obvious changes occurred mainly in DRB, the content oftrigonelline, γ-butyro-lactone and acetate have the potential to becomeauxiliary indicators for evaluating the degree of roasting. It is worthnoting that although the content of γ-butyro-lactone in roast coffee isrelatively low, the significant changes make it a great contribution tothe discriminant model.
3.2. Sensory evaluation
To minimize human error in sensory evaluation, all 72 samples of
three roasting degree were included. To intuitively see overall change,the aroma, flavor, aftertaste, acidity, body, balance, and overall wereplotted with heatmap (Fig. S10). On the whole, in addition to bodyscore, which increases as the degree of roasting, the MRB samples hadhigher cupping scores in terms of aroma, flavor, aftertaste, acidity,balance, and overall than LRB and DRB.
To quantify the extent to which the roasting degree affects the scoreof the evaluation, multivariate data analysis was further conducted.Based on aroma, flavor, aftertaste, acidity, body, balance, and overallscore of 72 samples, a 72*7 data matrix was constructed, and the matrixwas further analyzed by PLS-DA analysis. The goodness of fit of themodel was R2X = 0.9, R2Y = 0.825 the predictive power wasQ2 = 0.785, and the p value for ANOVA was lower than 0.01. As seenfrom the score plot, three different categories of coffee can be wellclassified (Fig. 4A). In this experiment, VIP value was used to determinethe contribution of the input sensory indicators to the discriminantmodel. It can also directly reflect the influence of the roasting degree onsensory indicators. The model fitted a total of four principal compo-nents, and VIP values of variables in each component are shown inFig. 4B. In the first component, VIP values of body, acidity, balance, andflavor were greater than 1. Among other three main components, onlyVIP values of body and acidity were greater than 1, and total VIP valueindicated that the influence of roasting degree on sensory indicators canbe ranked as: body > acidity > balance > flavor > balance >overall > aroma.
3.3. Relationship between main chemical ingredients and sensory indicators
In this section, the data set including the main chemical ingredients,cupping score and color value for the 18 samples was used to calculatethe Pearson correlation coefficient of the variables (Tables S4 and S5).The cluster heatmap shown in Fig. 5A was drawn based on the
Fig. 2. 1H NMR spectrum identification of main chemical ingredients in roasted bean extracts. 1H NMR spectrum is arranged according to the roasting degree, withLRB in front, MRB in the middle, and DRB in the end. The characteristic signals of sugars, CGAs, caffeine, trigonelline, quinic acids, acetate, formate, citrate, malate,γ-butyro-lactone, choline, 2-furyl-methanol, lipid, and N-methyl-pyridinium were identified from the extracts.
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calculation result. Usually, the correlation strength of the variables isjudged by the following range of values: correlation coefficient 0.8–1.0,extremely strong correlation; 0.6–0.8, strong correlation; 0.4–0.6,moderate correlation; 0.2–0.4, weak correlation; 0.0–0.2, weakly cor-related or uncorrelated. To intuitively clarify the complex relationshipbetween compounds, sensory indicators, total cupping score and colorvalue, and variables with moderate to extremely strong correlationwere screened to construct a sensory molecular network, in whichcorrelation was represented by lines of different colors and thicknesses(Fig. 5B).
As the maximum amount of ingredients present in coffee waterextract, the content of sugars (sucrose and other sugars) was extremelystrongly negatively correlated with body and was strongly negativelycorrelated with balance, aftertaste, flavor, and overall. A reasonableexplanation is that the insufficient Maillard reaction of sucrose resultingin the inadequate formation of favorable flavor compounds, indirectly
leads to a lower cupping score. Indeed, a large body of literature re-ported that sugars can produce various flavor compounds that are fa-vorable for cupping by the Maillard reaction during roasting (Liu et al.,2019; Velasquez, Pena, Bohorquez, Gutierrez, & Sacks, 2019). In ad-dition, from the results of cluster analysis, it can be seen that the effectof choline on sensory quality was very similar to that of sugars.
Trigonelline, CGAs and caffeine were under the adjacent branchesof the cluster analysis, indicating they have similar correlation betweencontent and sensory evaluation. In previous reports, the degradationproducts of trigonelline and CGAs along with caffeine were consideredto be related to the bitterness of coffee drinks (Locas & Yaylayan, 2004).CGAs can be thermally transformed into the bitter-tasting CGA lactones5-O-caffeoyl-muco-γ-quinide, 3-O-caffeoyl-γ-quinide, 4-O-caffeoyl-muco-γ-quinide, 5-O-caffeoyl-epi-δ-quinide, 4-O-caffeoyl-γ-quinide,3,4-O-dicaffeoyl-γ-quinide, 4,5-O-dicaffeoyl-muco-γ-quinide, and 3,5-O-dicaffeoyl-epi-δ-quinide (Frank et al., 2006; Rizzi et al., 2004).
Fig. 3. The score plot (A) and corresponding loading plot (B) of the first two dimensions of PLS-DA analysis based on the 1H NMR data of samples. (n = 18).
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Additionally, CGAs or its analogues were found to be important flavorregulating substances (Sittipod et al., 2019). Similar to sugars, thecontent of these three ingredients was extremely strongly negativelycorrelated with body. In addition, CGAs showed moderately negativecorrelation with balance.
The remaining nine (classes) compounds, including formate, citrate,malate, 2-furyl-methanol, lipids, γ-butyro-lactone, quinic acids, acetateand N-methyl-pyridinium, are distributed in the same cluster.Combined with the score plot and loading plot of the PLS-DA analysis inFig. 3, the content of these compounds was found to be relatively highin MRB or DRB. The correlation heatmap showed that they were allpositively correlated with the body score. Among them, 2-furyl-me-thanol, γ-butyro-lactone, quinic acids, acetate and N-methyl-pyridiniumshowed strong correlation. In addition, these nine compounds showedoverall low positive correlation with balance, flavor, aftertaste, aromaand overall.
Although acetic acid content was significantly increased in DRB, theacidity of coffee did not increase. In fact, it is prone to produce bittersubstances and mask the acid taste of coffee in DRB. For instance,several heterocyclics were suggested as potential bitter-tasting agents:2-furyl-methanol, which can be formed by the degradation of sugarsand quinic acid at high temperatures produces the bitter taste of roastedcoffee due to the interaction with dihydroxy-benzene or trihydrox-ybenzene (Kreppenhofer, Frank, & Hofmann, 2011); 5-hydroxymethyl-2-furanaldehyde, pyrazines, diketopiperazines and various trigonellinethermolysis products are also putative candidates for the key bittercompounds in coffee (Blumberg et al., 2010; Frank et al., 2006). Ad-ditionally, not all organic acids produce acid taste; apart from the bittertaste of CGAs and its lactone derivatives mentioned above, quinic acids,a kind of well-known thermal degradation products of CGAs, were re-ported to exhibit an aspirin-like bitter taste at a threshold level of10 ppm (Frank et al., 2006).
In addition to sweetness, clean up and uniformity, which were notincluded in the analysis due to their small differences between samples,dense strong correlations between sensory indicators are shown inFig. 5B. Among them, balance with flavor, balance with aftertaste, andflavor with aftertaste were of extremely strong correlation, and it islikely that these indicators are different descriptions belonging to asame intrinsic characteristic. Body and acidity showed relatively lowcorrelation with other indicators, indicating that they independentlydescribe an intrinsic feature and play an irreplaceable role in the sen-sory evaluation. Another important feature of roasted beans, colorvalue, was strongly or extremely strongly correlated with all detected
compounds except lipids and acetic acid, showing the rationality toreflect the content of the main components by detecting color value,while it only extremely strongly correlated with body in all sensoryindicators.
3.4. Establishment of a sensory prediction model
Although it has always been the mainstream evaluation method forcoffee quality, sensory evaluation is thought to be fairly subjective andgenerally less replicable and consistent than physically based mea-surements. Genetic factors, health, age, education, past experiences,food habits, smoking habit, and cultural and religious patterns can allbe factors that interfere with the objective score of the cupping panels(Romano et al., 2014; Worku et al., 2016). Therefore, researchers havelong been exploring alternative methods for evaluating coffee quality(Worku et al., 2016; Romano et al., 2014).
In this research, 11 compounds with extremely strong or strongcorrelations with body (apart from lipids, citrate, malate and formate)were tested to be used as input variables to establish a body scoreprediction model. Data showed that there were high correlations be-tween input variables (Table S5, marked by yellow), suggesting that thevariables had a strong collinearity and it was not appropriate to es-tablish the prediction model using the least squares regression (LSR)method. PLSR method combines the advantages of PCA and LSR, whichcan extract the latent variables (integrated variables) ti and ui inthe X and Y data sets, respectively. ti and ui can maximize the originalinformation in X(Y) dataset, and at the same time, satisfy the maximumextent linear correlation. This method can not only solve the problem ofmulticollinearity but also exhibit good modeling effects when thenumber of observed samples is small, so it was chosen as the modelingmethod.
The model fits a total of three principal components and the fit ofthe model was R2X = 0.993, R2Y = 0.834; the predictive power of themodel was Q2 = 0.755 (p < 0.01). The scatter plot of t1/t2 showed nooutlier present in observed samples (Fig. S11). A linear relationship isshowed in the scatter plot of t1 and u1 (Fig. 6A). Additionally, thepredictive ability of the model was further checked by comparing be-tween prediction and observed body value, and the result showed thatthe gaps between the predicted and observed score in all samples werewithin 0.2 (Fig. 6B). All the evidence suggests that it is feasible topredict the body score of coffee beverage by testing the content of themain ingredients.
Fig. 4. The score plot (A) and corresponding loading plot (B) of the first two dimensions of PLS-DA analysis based on the sensory evaluation data of samples.(n = 64).
G. Hu, et al. Food Chemistry 331 (2020) 127329
7
4. Conclusion
The changes in microstructure and main chemical ingredientsduring the roasting of coffee beans were clarified by SEM and 1H NMR,respectively. 1H NMR combined with multivariate analysis was con-firmed to be an effective strategy to monitor roasting degree.Trigonelline, sugars, malate, quinic acids, γ-butyro-lactone and acetateshowed potential to be used to monitor the roasting degree of coffeebeans.
The complexity of the chemical composition in roasted beans de-termines that it is impossible for researchers to describe a sensoryfeature using one or a kind of compounds. To more accurately clarifythe material basis of sensory indicators, in the current research, the
method of sensory molecular networks was proposed to reveal the in-tricate relationship between major compounds and sensory indicators.
Multivariate analysis and correlation analysis proved that the con-tent of coffee's main ingredients did have significant effects on acidityand body, especially the effect on body. The PLS model based on themain chemical components was used in an exploratory manner forprediction of body score and demonstrated good prediction perfor-mance. Other cupping indicators considered in this research, includingbalance, flavor, balance, overall and aroma, were less affected by thecontent of the main component. To replace these indicators, undetectedvolatile components or trace non-volatile components must be con-sidered.
Fig. 5. A) The cluster heatmap for the correlationbetween coffee sensory indicators and major che-mical ingredients. B) The flavor molecular networkshowing the correlation between total cuppingscore, sensory indicators, color value and majorchemical ingredients. The thickness of the line cor-responds to the strength of the correlation. Weakcorrelation and medium correlation (positive andnegative) are indicated by gray lines; strong andextremely strong positive correlation are indicatedby blue lines; strong and extremely strong negativecorrelation are indicated by pink lines. (For inter-pretation of the references to color in this figurelegend, the reader is referred to the web version ofthis article.)
The authors declare that they have no known competing financialinterests or personal relationships that could have appeared to influ-ence the work reported in this paper.
Acknowledgements
This research work was supported financially by the NationalNatural Science Foundation of China (Nos. 31670364, U1902206),Project of Key New Productions of Yunnan Province (No. 2015BB002),
Special Fund Project of Pu’er municipal government (2017) and Expertworkstation Project of Dr. QIU (2018) as well as Foundation of StateKey Laboratory of Phytochemistry and Plant Resources in West China(P2015-ZZ09).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodchem.2020.127329.
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