A pilot study of NMR-based sensory prediction of roasted coffee bean extracts

Food Chemistry 152 (2014) 363–369

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A pilot study of NMR-based sensory prediction of roasted coffee bean extracts Feifei Wei a,b, Kazuo Furihata a, Takuya Miyakawa a, Masaru Tanokura a,⇑ a b

Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan Japan Society for the Promotion of Science, 8 Ichiban-cho, Chiyoda-ku, Tokyo 102-8472, Japan

a r t i c l e

i n f o

Article history: Received 6 August 2013 Received in revised form 1 November 2013 Accepted 28 November 2013 Available online 4 December 2013 Keywords: NMR Sensory analysis Sensory prediction Multivariate analysis OPLS Roasted coffee beans

a b s t r a c t Nuclear magnetic resonance (NMR) spectroscopy can be considered a kind of ‘‘magnetic tongue’’ for the characterisation and prediction of the tastes of foods, since it provides a wealth of information in a nondestructive and nontargeted manner. In the present study, the chemical substances in roasted coffee bean extracts that could distinguish and predict the different sensations of coffee taste were identified by the combination of NMR-based metabolomics and human sensory test and the application of the multivariate projection method of orthogonal projection to latent structures (OPLS). In addition, the tastes of commercial coffee beans were successfully predicted based on their NMR metabolite profiles using our OPLS model, suggesting that NMR-based metabolomics accompanied with multiple statistical models is convenient, fast and accurate for the sensory evaluation of coffee. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction The development of artificial tongues to mimic the chemical sense of taste has received a great deal of attention (Savage, 2012). Such sensors could provide quality control in foods and agriculture, in addition to aiding in the development of new flavours and furthering our understanding of other aspects of human biology. Nuclear magnetic resonance (NMR) spectroscopy, which provides a wealth of information in a nondestructive and nontargeted manner, has been widely applied in food chemistry to obtain metabolite profiles of various kinds of biofluids and foods, such as milk (Hu, Furihata, Ito-Ishida, Kaminogawa, & Tanokura, 2004; Hu, Furihata, Kato, & Tanokura, 2007), mango juice (Koda, Furihata, Wei, Miyakawa, & Tanokura, 2012a) and rice wine (Koda, Furihata, Wei, Miyakawa, & Tanokura, 2012b). Recently, NMR metabolomic fingerprints have been successfully correlated with the sensory features of sour cherry juice (Clausen, Pedersen, Bertram, & Kidmose, 2011) and tomatoes (Malmendal et al., 2011), suggesting that NMR spectroscopy could be a very useful ‘‘magnetic tongue’’ for the characterisation and prediction of the taste of foods. Coffee is one of the most important internationally traded products. The distinctive flavour of coffee is certainly the principal rea-

⇑ Corresponding author. Tel.: +81 3 5841 5165; fax: +81 3 5841 8023. E-mail addresses: [email protected] (F. Wei), [email protected]. u-tokyo.ac.jp (K. Furihata), [email protected] (T. Miyakawa), amtanok@ mail.ecc.u-tokyo.ac.jp (M. Tanokura). 0308-8146/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2013.11.161

son for its high acceptability and enjoyment throughout the world. Although knowledge about the chemical composition of coffee bean extracts has been advanced in the past few decades, much about the flavour still remains unclear due to the lack of data about the specifics of coffee sensations. At the present time, human assessment is still the principal method for the sensory evaluation of coffee (Nebesny & Budryn, 2006). However, paying sensory analysts to assess every variety and origin of coffee bean extracts is prohibitively expensive and the result is unavoidably subjective. Therefore, in the present study, the utility of NMR spectroscopy as a potential tool to analyse the taste of coffee bean extracts was investigated. In our previous studies, the chemical composition of green coffee bean extracts (Wei, Furihata, Hu, Miyakawa, & Tanokura, 2010), roasted coffee bean extracts (Wei, Furihata, Hu, Miyakawa, & Tanokura, 2011), and their changes during the roasting process (Wei et al., 2012) and according to variety and origin (Wei et al., 2012) have been thoroughly studied by NMR-based comprehensive analysis. Here, we further sought to identify the chemical substances in roasted coffee bean extracts that could distinguish and predict the differential sensations of coffee taste by the combination of NMR-based metabolomics and human sensory tests and application of the multivariate projection method of orthogonal projection to latent structures (OPLS). When structured noise is present in a data set X (or Y), traditional projection techniques, such as partial least squares (PLS) regression can produce systematic variation of X (or Y), having a

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F. Wei et al. / Food Chemistry 152 (2014) 363–369

component uncorrelated to Y (or X). OPLS provides a way to remove systematic variation from an input data set X not correlated to the response set Y; in other words, to remove variability in X that is orthogonal to Y (Trygg & Wold, 2002). The purpose of the present study was to develop a novel method to predict the tastes of coffee using NMR-based metabolomics. To achieve this, an OPLS model was established using the NMR spectroscopic analysis of roasted coffee bean extracts and the sensory features evaluated by the human sensory test as X and Y, respectively. Then, the correlated X–Y variations were analysed to investigate the relations between chemical components and sensory descriptors. Finally, the taste of four kinds of commercial roasted coffee beans was successfully predicted based on their NMR metabolite profiles using our OPLS model. 2. Materials and methods 2.1. Coffee beans Green coffee beans of the arabica type from Brazil and Colombia and of the robusta type from Indonesia were supplied by San-Ei Gen F.F.I., Inc. (Osaka, Japan). The green coffee beans from Colombia and Indonesia were roasted to light (L value 25.9 ± 0.4) and dark (L value 18.2 ± 0.2) roasting levels with a Fuji Royal R-105 roaster (Fuji Kouki Co., Ltd., Osaka, Japan), while those from Brazil were roasted to a medium roasting level (L value 22.9 ± 0.4) to use as a standard sample in human sensory tests, and then all the roasted coffee beans were milled with a Panasonic MK-61 M-G miller (Osaka, Japan). Each L value was measured using the Hunter colour system of ground coffee beans (particle size < 1000 lm) with a ZE-6000 colour metre (Nippon Denshoku Industries Co., Ltd., Tokyo, Japan). All roasted coffee beans were sealed under a vacuum and stored at 20 °C until use. For the sensory prediction experiments, four kinds of commercial coffee beans were purchased from a local supermarket in the city of Tokyo (Japan). The packaging for each of these samples included a description of the sensory features (see Table S1 in the supplementary data), and the beans were stored at 20 °C until analysis. 2.2. NMR spectroscopic analysis For NMR spectroscopic observations, the crushed beans (1.5 g) were incubated at 95 °C in a closed plastic tube with D2O (3.50 ml, 99.7%; Shoko Co., Ltd., Tokyo, Japan) for 1 h. The extracts were cooled on ice for 15 min and then centrifuged at 5000g at 4 °C for 5 min. The supernatants (500 ll) were moved to new tubes, and a trace amount of 4,4-dimethyl-4-silapentane-1-sulphonate (DSS; Wako Pure Chemical Industries, Ltd., Osaka, Japan) was added as an internal reference, with a chemical shift set to 0 ppm. The coffee bean extracts were then transferred into 5 mm NMR tubes. One-dimensional (1D) 1H NMR spectra were measured at 500 MHz on an Agilent (Varian) Unity INOVA-500 spectrometre (Agilent Technologies, Inc., Santa Clara, CA). The H2O signal was suppressed by the presaturation method, and the parameters for observation were: number of data points, 64 k; spectral width, 8000 Hz; acquisition time, 4.0 s; delay time, 2.0 s; and number of scans, 128. The free-induction decay (FID) NMR data were processed by MestReNova software (Version 8.0.1; MestReC, Santiago de Compostela, Spain). The signal assignments of the components in arabica coffee bean extracts were carried out in our previous studies based on the analysis of two-dimensional (2D) NMR spectra (Wei et al., 2010; Wei et al., 2011). To obtain information about the components of the roasted robusta coffee bean extracts, the signal

assignments were carried out by comparing the NMR spectra with those of arabica coffee bean extracts. 2.3. Quantitative descriptive analysis (QDA) For sensory evaluation, the roasted coffee beans were mechanically brewed by an electric coffee maker (Model HCD-6GJ; Toshiba Co., Tokyo, Japan). The ground coffee bean powder (24 g) was placed in filter paper inside a funnel over a glass pot, and then about 360 ml of coffee bean extract was brewed at 85 °C for about 7 min from 420 ml of ion-exchange water in a tank. The extract was immediately cooled to 5–10 °C by a Graham condenser kept at 5 °C (pre-cooled). Each coffee bean extract was transferred into a 250 ml glass bottle (DURANÒ; SCHOTT, Mainz, Germany) which was sealed with a lid. Each glass bottle was randomly numbered except for the standard sample (Brazil, medium roasted, L value 22.9 ± 0.4). The sensory evaluation was performed by 13 trained assessors (3 females and 10 males; 27 40 years old) from San-Ei Gen F.F.I., Inc. (Osaka, Japan). At the time of the study, all the assessors had been working in the field of flavour and beverage development for at least 3 years, and they had all previously passed the requisite tests on the identification and differentiation of the five basic tastes. Before the evaluation sessions, the assessors selected 7 descriptors that aptly described the taste of coffee. Each descriptor was characterised as a reference substance (see Table S2 in the supplementary data). In the evaluation sessions, the coffee bean extracts were arranged in random order at 10–15 °C in a water-bath without reheating, in order to keep the sensory properties stable during the course of the experiment. All evaluations were undertaken in an odour-free test room under fluorescent light. The room temperature and humidity were kept at 23–26 °C and 52–72% with airconditioners. The samples were approximately 10 ml in volume and served in 50 ml plastic containers. Assessors tasted a standard before tasting samples as necessary during the session. A cup of water was prepared as a mouth rinse between each evaluation. The attributes were marked on a 15 cm unstructured line scale anchored at 1.5 (weak), 13.5 (strong) and a centre point, respectively, for each descriptive term. The sensory analysis was carried out using QDA in triplicate (n = 3). This experiment was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. 2.4. Multivariate analysis of 1H NMR For multivariate analysis, all the 1H NMR spectral data (four kinds of standard roasted coffee bean extracts, n = 6; four kinds of commercial coffee bean extracts, n = 1) were referenced, phased, baseline corrected, aligned and normalised by MestRe Nova, and then the data between 0.40 and 10.00 ppm were reduced into 0.04 ppm spectral buckets (bins). The residual water signals (H2O; bins 4.76–4.88 ppm) were removed. In order to avoid spurious principal components (PCs), areas of dramatically varying caffeine signals (N1CH3, N3CH3, N7CH3, C8H; bins 3.04–3.40, 3.68– 3.84, 7.60–7.80 ppm) (D’Amelio, Fontanive, Uggeri, Suggi-Liverani, & Navarini, 2009; Wei et al., 2010) were also removed, and the max bin of signal C8H in the caffeine molecule (among bins 7.60– 7.80 ppm) was picked up, renamed ‘‘bin caffeine’’ and used in the further multivariate data analysis. The resulting data sets (standard roasted coffee bean extracts) were then imported into SIMCA-P+ version 12.0 (Umetrics, Umeå, Sweden) to perform Principal Component Analysis (PCA). Prior to PCA, the data were mean-centred and then scaled using the scaling type of Pareto. Hotelling’s T2 region, shown as an ellipse

F. Wei et al. / Food Chemistry 152 (2014) 363–369

in the score plots, defined the 95% confidence interval of the modelled variation, and the quality of the PCA model was described by Rx2 (0.688) and Q2 (0.56) values. Rx2 was defined as the proportion of variance in the data explained by the model and indicates goodness of fit. Q2 was defined as the proportion of variance in the data predictable by the model and indicates predictability. 2.5. Multivariate analysis of QDA QDA results were conducted with ANalysis Of VAriance (ANOVA) by using the JMP10 software package (SAS Institute, Inc., Cary, NC) and were considered to be significantly different at p < 0.01. Then, a Tukey’s test (p < 0.05) was used to find significant differences between different coffee samples in each taste quality. The correlations among the obtained data were calculated using the Pearson’s correlation coefficient and p values less than 0.05 were considered to be statistically significant. 2.6. Prediction of sensory features For the standard OPLS model, the combined data sets (1H NMR data as variable X; corresponding QDA data as variable Y) of the standard roasted coffee bean extracts were imported into SIMCAP+, mean-centred and then scaled using the scaling type of Pareto for the variable X and Centred for the variable Y. The confidence level for membership probability was considered to be 95%, and the quality of the model was described by R2X (0.687), R2Y (0.986) and Q2 (0.97). Markers for the sensory descriptors were identified from the NMR signals that showed a strong correlation (|p| P 0.05 and |p (corr)| P 0.5 in S-plots, see Fig. S2 in the supplementary data) to the OPLS predictive scores for the sensory descriptors. For the predictive OPLS model, the NMR data of the commercial coffee bean extracts were added into the standard OPLS model leaving the corresponding Y sections blank. The Hotelling’s T2 was set to 95%, and the quality of the model was described by R2X (0.909), R2Y (0.997) and Q2 (0.976).

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To determine which chemical substances were significantly correlated with the variety and roasting level of the coffee beans used for preparing extracts, PCA was applied to the 1H NMR spectra of 24 independent data sets from roasted robusta and arabica coffee bean extracts, at light or dark roasting levels (n = 6 each). As shown in Fig. S2A in the supplementary data, the PCA models using projections into two dimensions (PC1 of 0.378 and PC2 of 0.212) showed statistically significant separation among the standard roasted coffee bean extracts, indicating that the most significant differences in composition are from varying roasting levels (PC1) rather than species (PC2). The PC1 loading plot indicates components responsible for variables contributing to the classification according to the roasting level. As shown in Fig. S2B, the negative side of the PC1 loading plot reveals that the contents of chlorogenic acids, trigonelline, caffeine, 5-HMF, citrate, malate and acetate are higher in the roasted coffee bean extracts at the light roasting level, while the positive side indicates that the levels of quinic acids, quinide, N-methylpyridinium, mannose and lipids are higher at the dark roasting level. The PC2 loading plot indicates components responsible for distinguishing the species of roasted coffee bean extracts but not the roasting level. As shown in Fig. S2C, the contents of chlorogenic acids, caffeine, lactate, quinide, quinic acids and lipids are higher in the roasted robusta coffee bean extracts, whereas the levels of polysaccharides, acetate, citrate, malate, formate, trigonelline, N-methylpyridinium and 5-HMF are higher in the roasted arabica coffee bean extracts. In our previous study, the green coffee bean extracts of arabica was found to contain higher levels of sucrose, trigonelline, citrate and malate (Wei et al., 2012). In the present study, no sucrose was found but higher levels of acetate, formate and 5-HMF were detected in the roasted arabica coffee bean extracts. These findings are in accordance with previous reports that sucrose in green coffee beans is closely related with the formation of aliphatic acids and 5-HMF in roasted coffee beans (Murkovic & Bornik, 2007).

3.2. Quantitative descriptive analysis 3. Results and discussion 3.1. NMR analysis The 1H NMR spectra of the standard roasted coffee bean extracts are shown in Fig. 1, and exhibit complicated signal patterns because of overlaps and chemical-shift perturbations. As shown in Fig. 1, for both roasted arabica and robusta coffee bean extracts, 26 components were identified: 3 isomers of chlorogenic acids (i.e., 3-caffeoylquinic acid (3-CQA), 4-caffeoylquinic acid (4-CQA) and 5-caffeoylquinic acid (5-CQA)), quinic acid, c-quinide, syllo-quinic acid, a-(1-3)-L-arabinofuranose, a-(1-5)-L-arabinofuranose, b-(13)-D-galactopyranose, b-(1-6)-D-galactopyranose, b-(1-4)-D-mannopyranose, acetate, c-butyrolactone, caffeine, choline, citrate, 2-furylmethanol, formate, myo-inositol, lactate, lipids, malate, N-methylpyridinium, nicotinic acid, 5-(hydroxymethyl)furfural (5-HMF) and trigonelline. Because the caffeine molecules formed complexes with chlorogenic acids, the chemical shifts of caffeine varied dramatically in different sample matrices of roasted coffee bean extracts (D’Amelio et al., 2009), which was considered to be the reason for the failure in alignment. Since the unwelcome chemical-shift perturbations give a more complex multivariate model that includes spurious PCs and that may be misleading for the identification of marker compounds, areas of varying caffeine signals were removed, leaving only the max bin of signal C8H to represent the distribution of caffeine in the roasted coffee bean samples.

The results of the QDA mean are shown in Fig. 2. The light roasted arabica coffee bean extracts were characterised by sourness; the dark roasted arabica and robusta coffee bean extracts seemed to taste very similar to each other, as observed by QDA. Consistent with the NMR results, the differences between species of the roasted coffee bean extracts also tended not to be obvious with the deepening of roasting. However, NMR showed higher sensitivity in species discriminations even at dark roasting levels, as shown in Fig. S2(A), while QDA provided very similar sensory characteristics. Among the 7 taste descriptors evaluated in the human sensory test, the ANOVA analysis indicated that umami is not significantly related with the variety or the roasting level of the coffee bean extracts. Therefore, we removed the taste of umami from our further analysis. To further investigate the significant differences in each taste descriptor among the coffee bean extracts, the Tukey’s test was carried out and the results are shown in Table S3 in the supplementary data. There were significant differences between the light roasted arabica coffee bean extracts and the other extracts with respect to the taste of sweetness. Significant differences in coffee body, sourness, and bitterness were observed between all coffee bean extracts, except between the dark roasted robusta and arabica coffee bean extracts. The taste of astringency was found to be significantly varied according to the roasting level but not the varieties of the coffee beans. The only significant difference in saltiness was between the light roasted robusta and arabica coffee bean extracts.

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(A)

quinic acids and quinide

quinic acids and polysaccharides

caffeine

arabica

myo-inositol

β-(1-6)-D-galactopyranose N-methylpyridinium

β-(1-4)-D-mannopyranose

robusta

lipids

2-furylmethanol γ-quinide

nicotinic acid

acetate

(B)

choline

5-hydroxymethylfurfural trigonelline

formate trigonelline

γ -butyrolactone

citrate and malate

β-(1-3)-D-galactopyranose α-(1-5)-L-arabinofuranose

α-(1-3)-L-arabinofuranose

arabica

lactate

chlorogenic acids robusta

9.5

9.0

8.5

8.0

7.5

7.0

6.5

6.0

5.5 1

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

H (ppm)

Fig. 1. Assigned 1H NMR spectra of arabica (solid black line) and robusta (dashed grey line) roasted coffee beans at (A) dark roasting levels and (B) light roasting levels.

sweetness** arabica (light)

10.0

arabica (dark)

8.0

umami

body**

6.0 4.0

and even sweetness; saltiness changed positively with sourness; and coffee body showed positive correlations with both sweetness and astringency.

robusta (light) robusta (dark)

2.0

3.3. Correlations between chemical components detected by NMR and sensory descriptors

0.0

bitterness**

astringency**

sourness**

saltiness**

Fig. 2. Spider web plot of the sensory descriptors for the four tested coffee bean samples. The symbol ‘‘⁄⁄’’ indicates a significant difference (p < 0.01) as calculated by ANOVA.

To characterise the correlations between different sensory descriptors, the Pearson’s correlation coefficients were calculated. These are summarised in Table 1. Negative correlations of sourness with bitterness, sweetness, astringency and body were observed; bitterness showed positive correlations with body, astringency

To show the correlation between the chemical components and the sensory descriptors of roasted coffee bean extracts, all possible correlations between the NMR signals and the analysed sensory descriptors were sought by OPLS models. Although the extraction time for the NMR analysis was made much longer than that for the human sensory test in order to obtain a high enough concentration for NMR observation, the relationship between NMR-visible components and sensory features was still credible, since the same extraction time was used for all kinds of coffee bean samples in the same analytical platform, NMR or QDA. In fact, only the relative variations among differential coffee bean extracts can be under consideration by the OPLS model, while the variations between the NMR and QDA samples were removed as so-called structured noise (Trygg & Wold, 2002). The score plot of the standard OPLS model is shown in Fig. 3(A). Similar to the PCA results derived from NMR only, the four kinds of

Table 1 Pearson’s correlation coefficients between sensory descriptors. Sweetness Sweetness Body Astringency Saltiness Sourness Bitterness Umami a

Body

Astringency

0.39a 0.39a 0.04 0.13 0.43a 0.33a 0.09

p < 0.05 in Pearson’s correlation comparison.

0.04 0.46a

a

0.46 0.04 0.60a 0.67a 0.02

0.11 0.30a 0.56a 0

Saltiness

Sourness

0.13 0.04 0.11

0.43a 0.60a 0.30a 0.32a

0.32a 0.08 0.02

0.68a 0.08

Bitterness 0.33a 0.67a 0.56a 0.08 0.68a 0.07

Umami 0.09 0.02 0 0.02 0.08 0.07

F. Wei et al. / Food Chemistry 152 (2014) 363–369

(A)

arabica (light) arabica (dark)

30

robusta (light) robusta (dark)

OPLS2

20 10 0 -10 -20 -30 -40 -50

-40

-30

-20

-10

0

10

20

30

40

OPLS1

(B)

sourness

0.08

OPLS2

0.06 0.04

saltiness astringency

0.02 bitterness body

0 sweetness

-0.02 -0.04 -0.15

-0.1

-0.05

0

0.05

0.1

OPLS1 Fig. 3. OPLS (A) score plot of X–Y and (B) the scatter plot of Y derived from the 1H NMR spectra (X) and QDA (Y) of the roasted coffee bean samples.

roasted coffee bean extracts were significantly different from each other even if their sensory characteristics were taken into account. According to the scatter plot of sensory descriptors (see Fig. 3(B)), the transition from the upper-left corner to the bottom-right corner of the map shows the simultaneous decrease of the sourness and saltiness and increase of the bitterness, body, sweetness and astringency. In general, light roasted arabica coffee bean extracts are characterised by sourness as well as saltiness; dark roasted arabica coffee bean extracts are instead characterised by a more marked astringency and bitterness. On the other hand, the dark roasted robusta beans are characterised by bitterness, body and sweetness. However, none of the descriptors was found to be the marked sensory characteristic of light roasted robusta beans by the present model, indicating the relatively plain or mild features of light roasted robusta beans compared to the samples. To characterise the correlations between NMR signals and the analysed sensory descriptors, the correlation coefficients were calculated by S-plots for each sensory descriptor. In the S-plot of a given sensory descriptor, which is available in the supplementary data (Fig. S2), variables fulfilling the covariance of |p| P 0.05 and the correlation of |p (corr)| P 0.5 were selected and considered as having a relationship with the given descriptor. According to our previous signal assignment (Wei et al., 2011), the captured components and their correlations with each descriptor were summarised as shown in Table 2. It has been described that chlorogenic acids have a mild bitter taste and might contribute to the bitterness of coffee drinks (Campa, Doulbeau, Dussert, Hamon, & Noirot, 2005). However, whether chlorogenic acid is actually bitter in taste remains a matter of controversy. Here, we observed a negative correlation between chlorogenic acid and the bitterness of coffee; that the content of chlorogenic acids was reduced along with the bitter taste of coffee.

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On the other hand, the levels of the water-soluble degradation products of chlorogenic acids during the coffee bean roasting progress, such as quinic acid, syllo-quinic acid and quinide, increased along with the bitter taste of coffee. Our present study suggests that those degradation products of chlorogenic acids that have a strong bitter taste (Blumberg, Frank, & Hofmann, 2010), rather than the chlorogenic acid itself, are more likely to directly contribute to the bitterness of roasted coffee. It has been reported that trigonelline is not a dominant contributor to roasted coffee bitterness (Belitz, 1975), and alkylpyridiniums may indeed be responsible for a portion of the perceived bitterness in the coffee brew (Stadler, Varga, Hau, Vera, & Welti, 2002). We found that the coffee body positively correlated with the content of polar lipids, mannose, quinide and quinic acids, but negatively correlated with that of arabinose, galactose and trigonelline. The polar lipids are important surface-active agents, which help to form and stabilise foam and emulsion and have previously been related with the formation of coffee body (Buffo & CardelliFreire, 2004). Polysaccharides are known to be responsible for the increase of coffee viscosity (Navarini et al., 1999). We have previously reported that the concentration of water-soluble mannose is constantly increased during the roasting progress due to its lower thermal lability (Wei et al., 2012). Therefore, it is reasonable to suppose that mannose might be the major contributor to coffee body. In contrast to bitterness and body, the sour taste of coffee was negatively correlated with the content of lipids, quinide and quinic acids, and positively correlated with that of chlorogenic acids, citrate, malate, formate, acetate, trigonelline, arabinose and galactose. Malic, citric, phosphoric and acetic acids have been identified as the major contributors to coffee sourness (Degenhardt, C., S., & F., 2006). Consistent with these previous reports, we observed a higher level of organic acids, such as citrate, malate, formate and acetate, in roasted coffee bean extracts with a significant sour taste. In particular, the increased concentration of aliphatic acids in the arabica coffee bean extracts at a light roasting level might have been derived from the degradation of sucrose, which is present at a higher level in the green coffee bean extracts of arabica (Gin, Balzer, Bradbury, & Maier, 2000; Wei et al., 2012). Coffee sweetness is found to be positively related to lipids, quinic acids, mannose, and quinide, and negatively related to chlorogenic acids, citrate, formate, malate, trigonelline, arabinose and galactose. Interestingly, almost all of the components positively related with the sweet taste of coffee are not sweet themselves. Conversely, quinic acids and quinide are even slightly bitter. Coffee sweetness is increased along with coffee bitterness. These results appear to be in conflict with the previous reports that an increase in saccharide, which enhances the sweet taste, will inhibit the bitter taste (Malmendal et al., 2011). In fact, the relationship between sweetness and other tastes such as bitterness is variably affected at low intensities/concentrations (i.e., their tastes strengthen each other or weaken each other) and might be symmetrically suppressive only at medium and high concentrations (Keast & Breslin, 2003). On the other hand, the components negatively related with coffee sweetness, such as citrate and malate, have a very strong sour taste. It has been reported that the relationship between sweetness and sourness is variably affected at low intensities/concentrations but symmetrically suppressive at medium and high intensities/concentrations. This might partially explain the trends between sweetness and bitterness and the mutually suppressive relationship between sweetness and sourness observed in our present study. Similar to bitterness, coffee astringency was positively related with lipids, quinic acids, mannose, and quinide and negatively related with chlorogenic acids, citrate, malate, trigonelline, arabinose and galactose. Consistent with previous reports on cranberry juice

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Table 2 Correlation between coffee bean components and sensory descriptors highlighted by OPLS Models.a Bin No. (ppm) or bin name

3-CQA 4-CQA 5-CQA Acetate Caffeine Citrate Formate Lactate Lipids Malate N-methylpyridinium Quinic acid Syllo-quinic acid Trigonelline a-(1-3)-L-arabinofuranose a-(1-5)-L-arabinofuranose b-(1-3)-D-galactopyranose b-(1-4)-D-mannopyranose b-(1-6)-D-galactopyranose c-Quinide

5.36 7.40 5.32 1.96 Caffeine 2.76 8.44 1.36 0.92 2.60 8.72 2.00 2.08 4.40 5.20 5.08 4.64 4.72 4.44 2.52

Sweetness p

Bitterness

p (corr)

p

Sourness

p (corr)

p

p (corr)

Astringency

Saltness

p

p

p (corr)

0.13 0.14 0.18

0.87 0.88 0.87

0.11 0.12 0.13

0.88 0.70 0.90

0.13 0.14 0.11 0.07

0.89 0.91 0.56 0.50

0.09 0.11 0.10

0.75 0.78 0.58

0.07 0.09

0.56 0.57

0.05

0.64

0.07 0.09

0.59 0.58

0.06

0.71

0.08 0.11

0.57 0.70

0.60 0.75

0.09 0.08

0.74 0.73

0.65 0.66 0.76 0.65 0.63 0.85 0.58 0.50 0.56

0.74 0.74 0.60 0.79 0.94 0.78

0.08 0.11

0.12 0.13 0.13 0.06 0.07 0.14 0.09 0.16 0.10

0.09 0.08 0.05 0.12 0.16 0.12

0.75 0.98 0.66

0.90 0.87 0.82 0.52 0.83

0.69 0.67 0.82 0.70 0.61 0.70

0.12 0.17 0.10

0.08 0.13 0.14 0.10 0.13

0.12 0.13 0.14 0.06 0.05 0.08 0.16 0.10

0.54 0.56

0.09 0.10 0.17 0.11 0.13

0.92 0.79 0.94 0.75 0.89

Body p (corr)

0.07 0.06 0.14 0.19 0.18 0.05 0.10 0.09

0.53 0.59 0.58 0.56 0.85 0.63 0.51 0.54

0.13

0.87

0.06 0.06 0.10

0.53 0.61 0.72

0.10

0.50

p

p (corr)

0.09

0.73

0.12 0.16 0.12

0.78 0.93 0.77

0.08 0.10 0.09 0.13 0.13

0.87 0.54 0.56 0.86 0.83

Only one bin of each compound were picked up, values of |p| < 0.05 and |p (corr)| < 0.5 in S-plots were excluded or shown as blank.

(Peleg & Noble, 1999), quinic acids contribute to both bitterness and astringency. According to a previous study, astringency might be strongly related with the pH of coffee (Rubico & Mcdaniel, 1992). It has been reported that the acidic component decreases and the pH increases along with the change from light to dark roasting levels (Fuse, Kusu, & Takamura, 1997). Therefore, it is reasonable that the astringency of coffee is increased during the roasting progress. Similar to coffee sourness, the salty taste of coffee was positively related with most of the acids as well as with all of the polysaccharides except mannose, and negatively related with chlorogenic acids and caffeine. Although the well-known salty substances such as sodium ions and potassium ions cannot be directly detected by NMR spectroscopy, it has been proved that green coffee beans are rich in a variety of metal ions, such as Na+ and K+ (Anderson & Smith, 2002). In fact, there is a strong correlation between sourness and saltiness at even low concentrations/intensity of such metal ions (Keast & Breslin, 2003). Furthermore, in accordance with our findings, the taste of coffee brewed with metal ion-rich mineral water is more acidic than that brewed with distiled water (Pangborn, Trabue, & Little, 1971). 3.4. NMR-based sensory predictions The predictive OPLS model was established using the NMR spectra of standard roasted coffee bean extracts and their corresponding sensory features evaluated by human sensory test, as well as the NMR spectra of four kinds of commercial coffee bean extracts without the QDA value. As shown in Fig. 4, all four kinds of commercial coffee bean extracts, namely Mokha taste, Mild taste, Kilimanjaro taste and Dark roast taste, are located in the PC1 and PC2 positive area, indicating their chemical makeup and sensory features are similar to those of dark roasted arabica coffee bean extracts. According to the loading plots of sourness and bitterness and the localisation of the commercial coffee bean extracts in the score plot, Mokha taste has the highest level of sourness followed by Mild taste, Kilimanjaro taste and Dark roast taste. Meanwhile, Dark roast taste has the highest level of bitterness followed by Kilimanjaro taste, Mild taste and Mokha taste. These predictive results are exactly in agreement with the marked sensory features of the commercial coffee beans (see Table S2 in the supplementary

sourness rness Mokha-blended bitter bitterness Mild-blended

30 sourness

20

Kilimanjaro-blended Dark roast-blended

saltiness 10 OPLS2

a

Compound name

astringency

0

-10

body bitterness

sweetness

-20

light roasted arabica dark roasted arabica

-30

light roasted robusta dark roasted robusta

-40 -50

-40

-30

-20

-10

0 OPLS1

10

20

30

40

Fig. 4. Score and scatter plots of the predictive OPLS model including the four commercial roasted coffee bean samples.

data), suggesting NMR-based metabolomics combined with multivariate statistical analysis could be used in sensory prediction for roasted coffee bean products. The major advantage of the NMR-based sensory prediction with multiple statistical methods (such as the OPLS model), known as the ‘‘magnetic tongue’’, is that it can provide a faster, cheaper, and more accurate and objective way to predict the taste of food than the human tongue itself. This advance in technology will shed increased light on the field of quality control in foods and agriculture. However, it also should be noted that one of the potential limitations of the ‘‘magnetic tongue’’ is that it cannot determine the exact taste of a certain chemical component the way a human tongue can. It can predict the sensory characteristics of food only by catching the relative change in the concentrations of the chemical components. This means that the chemical component with no change in its concentration will be ignored by the OPLS model even if it may make a considerable contribution to the sensory characteristics of the food. At the same time, all the chemical components sharing the same trends of change in their concentrations may be captured as markers by the OPLS model for the given sensory descriptor, e.g., bitterness, irrespective of whether they actually taste bitter.

F. Wei et al. / Food Chemistry 152 (2014) 363–369

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