Discrimination of Chinese Teas with Different Fermentation Degrees

Article pubs.acs.org/JAFC

Discrimination of Chinese Teas with Different Fermentation Degrees by Stepwise Linear Discriminant Analysis (S-LDA) of the Chemical Compounds Quan-Jin Wu,† Qing-Hua Dong,§ Wei-Jiang Sun,*,†, and Wei-Long Zhou#

‡

Yan Huang,

‡

Qiong-Qiong Wang,†

†

College of Horticulture and ‡Anxi College of Tea Science, Fujian Agriculture and Forestry University, No. 15 Shangxiadian Road, Cangshan, Fuzhou 350002, China § Fuzhou Huaming Tea Research Institute, No. 6 Gutian Road, Jin’an, Fuzhou 350001, China # Hangzhou Tea Research Institute, China Coop, No. 41 Caihe Road, Hangzhou 310016, China S Supporting Information *

ABSTRACT: This study aimed to construct objective and accurate analytical models of tea categories based on their polyphenols and caffeine. A total of 522 tea samples of 4 commonly consumed teas with different fermentation degrees (green tea, white tea, oolong tea, and black tea) were analyzed by high-performance liquid chromatography (HPLC) coupled with spectrophotometry, utilizing ISO 14502, as analytical tools. The content of polyphenols and caffeine varied significantly according to differently fermented teas, indicating that these active constituents may discriminate fermentation degrees effectively. By principal component analysis (PCA) and stepwise linear discriminant analysis (S-LDA), the vast majority of tea samples could be successfully differentiated according to their chemical markers. This study yielded three discriminant functions with the capacity to simultaneously discriminate the four tea categories with a 97.8% correct rate. In classification of oolong and other teas, there were one discriminant function and two equations with best discriminant capacity. Furthermore, the classification of different degrees of fermentation of oolong and external validation achieved the desired results. It is suggested that polyphenols and caffeine are the distinct variables to establish internationally recognized models of teas. KEYWORDS: classification of tea category, chemical compounds, fermentation degrees, high-performance liquid chromatography (HPLC), principal component analysis (PCA), stepwise linear discriminant analysis (S-LDA)

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INTRODUCTION It is commonly acknowledged that tea is one of the top three alcohol-free beverages for it possesses multiple beneficial bioactivities. Generally, teas are classified according to “processing technology” and are characterized by “degree of fermentation”. According to various fermentation degrees and manufacturing processes, teas can mainly be classified into four categories: unfermented green tea, partially fermented white tea, semifermented oolong tea, and fully fermented black tea, which account for the majority of the worldwide consumption. As tea is consumed worldwide, some trade disputes occur now and then in terms of the types of teas. Traditionally, teas are evaluated organoleptically, which is often performed by highly trained specialists. However, the human panel test is somewhat subjective in that it is easily influenced by physical and mental conditions. There are also other crucial components that increase the difficulty of tea classification and the significance of developing discriminant methods. First, each tea category is different in tea assessment terms at home and abroad for its own characteristics and unique taste, making it difficult to understand and memorize. Second, there is a common confusion of teas owing to the resemblance of appearance, aroma, or taste, especially the confusion of oolong and white tea compared to other teas. The faint-scent type oolong tea known as “the champagne of tea” is more lightly fermented for © 2014 American Chemical Society

the purpose of increasing the greenness, making its aroma and taste similar to those of green tea,1,2 which cannot be identified quickly. For instance, semicircular oolong tea (especially Taiwan oolong) and gunpowder tea, are similar in shape and color, increasing the difficulty of their distinction. Identically, Wuyi Rock tea has a strip shape and bright orange that are similar to those of black tea. White tea, which is a special kind of tea, has almost equal health effects compared with green tea.3−5 However, they are frequently confused for their similar appearance on the international and domestic market. For instance, Anjibaicha (one kind of green tea) is often confused for its Chinese name is similar to that of white tea. Third, among the various tea-importing countries, the taxes are different according to the fermentation degree of the tea.1 Additionally, oolong and white teas have higher prices because their special flavor and health benefits have been known, but some products are mixed or adulterated with products, making their quality or flavor unsatisfactory. Thus far, a more reliable and quantifiable method to identify tea categories has not been Received: Revised: Accepted: Published: 9336

May 28, 2014 August 29, 2014 August 29, 2014 August 30, 2014 dx.doi.org/10.1021/jf5025483 | J. Agric. Food Chem. 2014, 62, 9336−9344

Journal of Agricultural and Food Chemistry

Article

Table 1. Description of Calibration Set and Validation Set of Tea Samples

HPLC analysis and pattern recognition to optimize the method of qualitative and quantitative flavonoid analysis. There is certain progress in terms of Wuyi Rock tea distinction, accumulation, and identification of nonvolatile compounds of the tea by the application of HPLC.20−23 As an effective chemometrics method, stepwise linear discriminant analysis (SLDA) is incomparable to others in terms of discrimination for its good statistical learning theory and highly efficient discriminant functions,24 which exclusively selected fewer useful variables by neglecting influences of other variables. Hence, this study applied HPLC analysis in combination with S-LDA to categorize the different fermented teas. Conducting stable and accurate classification of different kinds of tea in the current tea market is an important issue as well as the essential component of the international standard system. For the above reasons, the proposal of tea chemical classification has passed at the 24th International Conference of International Organization for Standardization (ISO), along with the setup of working groups. Therefore, we aimed to develop an accurate and efficient method based on HPLC to discriminate teas by potential nonvolatile compounds. The study was based on the fact that the biochemical composition varies significantly among different degrees of fermented teas. In our study, numerous representational samples covering four main tea categories in accordance with different fermentation degrees were collected. Polyphenols and caffeine were taken as the distinct variables to establish internationally recognized models of teas, providing reference for the establishment of chemical classification, which would help international tea trade and consumers.

developed. Therefore, it is necessary to develop accurate and stable methods to distinguish different levels of fermented teas. Flavor, frequently described as taste and aroma, is the most significant element for tea evaluation. Scientists are highly concerned about the relationship of flavor and biochemical nature. Tea is abundant in chemical components that differ in both content and their component ratio, which not only causes the multiple dimensions of tea quality but also brings some difficulties to the qualitative analysis of tea, especially when tea chemical information far exceeds the taster’s subjective analysis. Thus, it is crucial to take advantage of quantitative indicators to identify the tea. More recently, the tea field has made its greatest progress in characterizing the nature of tea at the level of chemistry. Unremitting efforts have been made to research the key components of various teas on the basis of biochemistry and physical characteristics by instrumental techniques. So far, to analyze the characteristic constituents and identify the categories or geographical origins of teas, researchers have been using spectrophotometry,6 high-performance liquid chromatography (HPLC),7,8 gas chromatography−mass spectrometry (GC-MS),9,10 inductively coupled plasma mass spectrometry (ICP-MS),11 surface desorption chemical ionization mass spectrometry (DAPCI-MS),12 total luminescence spectroscopy (TLS),13 near-infrared spectroscopy (FT-NIRS),14 electronic nose and electronic tongue,15,16 and machine vision technique.17,18 Among the various analytical tools, HPLC is widely used by the main exporting and importing countries for the purpose of identifying chemical compositions, providing high accuracy and sensitivity. As widely mentioned, the biochemical characteristics of tea should be attributable to the patterns composed of taste; however, such a method has not been applied systematically for the classification of tea categories. Therefore, we paid close attention to key components such as polyphenols and caffeine, which may be the first consideration for setting a discrimination standard.19 “Fermentation” in tea science is typically defined as the oxidation of polyphenols. Increased attention in potential health functions of flavonoid-rich tea has prompted the use of

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MATERIALS AND METHODS

Tea Samples. A total of 522 tea samples were collected according to fermentation degree, as described in Table 1. Among these samples, 482 tea samples, as the calibration set, were produced in 2010 and 2011, which were collected and purchased from reliable sources in China (463 samples, Table S1 in the Supporting Information) and other countries (19 samples, Table S2 in the Supporting Information).

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dx.doi.org/10.1021/jf5025483 | J. Agric. Food Chem. 2014, 62, 9336−9344


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fraction), 2% acetic acid (volume fraction) with 20 μg/mL EDTA, B = 80% acetonitrile (volume fraction), 2% acetic acid (volume fraction) with 20 μg/mL EDTA. Binary gradient conditions were as follows: The 100% mobile phase A lasted for 10 min, followed by a linear gradient to 68% mobile phase A and to 32% mobile phase B for 15 min, and was retained at this composition for 10 min. The system was reset to 100% mobile phase A and allowed to remain stable before the next injection. The five most abundant catechins from the extract, catechin (C), epicatechin (EC), epigallocatechin (EGC), epigallocatechin gallate (EGCG), and epicatechingallate (ECG), were assayed. Catechin and caffeine quantifications were performed using a caffeine calibration curve. For the quantification of the individual catechins, external catechin standards of defined purity and moisture content were used as a standard in conjunction with relative response factors (RRFs), calculated on a dry matter basis. The qualitative analysis of catechin monomer was performed by comparison of their peak areas and retention time on the basis of authentic compounds that were obtained from Sigma. Caffeine content was quantified as follows in accordance with ISO 14502-2:2005.

Another 40 samples, as the validation set, were produced in 2012 (Table S3 in the Supporting Information). Teas with different degrees of fermentation were sorted from different grades and producing regions and analyzed by HPLC and spectrophotometry for categories discriminate investigation. All samples were kept in ziplock bags and stored in a refrigerator at 4 °C until determined. HPLC and Spectrophotometry Analysis. All of the determinations were assayed in triplicate according to International Standards, which have been applied to provide robust validated analytical tools in cases of international transactions and disputes. The determination of total polyphenols and catechins was performed according to the procedure described by previously reported ISO 14502.23,25 Chemicals. The following chemicals and solvents were used: Methanol (≥99.5%), Folin−Ciocalteu phenol reagent, sodium carbonate (≥99.8%), gallic acid (≥98.5%), ethylenediaminetetraacetic acid disodium salt, dehydrate (EDTA, ≥99.0%), and L-ascorbic acid (≥99.7%) were purchased from Sinopharm (Beijing, China). (+)-Catechin (C, ≥99%), (−)-epicatechin (EC, ≥98%), (−)-epigallocatechin (EGC, ≥95%), (−)-epigallocatechin gallate (EGCG, ≥95%), and (−)-epicatechin gallate (ECG, ≥98%) were obtained from SigmaAldrich (St. Louis, MO, USA.). Caffeine (Aladdin, Shanghai, China), acetonitrile (HPLC grade, ≥99.9%, Lichrosolv, Germany), and acetic acid (HPLC grade, ≥99.7%) were obtained from Fisher Scientific. Determination of Dry Matter Content. The dry matter content was assayed according to the protocol described by ISO 1572 and ISO 1573. Processed tea leaves were milled into a dried fine powder by using a grinding mill and then passed through a sieve of aperture size 500 μm. A sample of 5 ± 0.001 g of tea was weighed to place in a preweighed aluminum dish and dried in a drying oven (DHG-9140A, Lantian, Hangzhou, China) at 103 ± 2 °C for 6 h and weighed until two successive weighings did not exceed 0.005 g. The dry matter content of the samples was calculated from the weight differences. Preparation of Extracts. Samples weighing 0.2 ± 0.001 g were placed in extraction tubes (10 mL). Five milliliters of preheated 70% water/methanol extraction mixture was filled into each tube individually and incubated in the water bath for 10 min at 70 °C (DK-S22 Electric Water Bath, Jing Hong, Shanghai, China) and vortexed for 5 and 10 min, respectively. The extraction tubes were removed from the water bath, allowed to cool to room temperature, and centrifuged for 10 min at 3500 rpm using a centrifuge (Lu Xiang Yi, Shanghai, China). The supernatant was carefully decanted into graduated tubes. The extraction steps were repeated as described above. The extracts were combined and made up to 10 mL with cold methanol/water extraction mixture. Analysis of Total Polyphenols (TPs). The total polyphenol content was determined spectrophotometrically according to International Standard ISO 14502-1:2005, using Folin−Ciocalteu’s reagent. Using a pipet, 1 mL of sample extract was pipetted into a 100 mL volumetric flask and filled to the mark with distilled water. One milliliter of the diluted sample was transferred into each tube, mixed with 5 mL of diluted Folin−Ciocalteu phenol reagent, allowed to stand for 3−8 min, and then mixed with 4 mL of 7.5% sodium carbonate solution for 1 h before spectrometric analysis. Distilled water was used as blank, whereas the gallic acid standards were used for quantification. A bestfit linear calibration graph was constructed from the mass of anhydrous gallic acid. The total polyphenols content of tea was detected by using a Shimadzu UV-260 spectrophotometer with a maximum absorption of 765 nm. Analysis of Catechins and Caffeine Content. Catechins and caffeine analyses were conducted according to ISO 14502-2:2005 protocol. One milliliter of the sample extract was made up to 5 mL with stabilizing solution (10% v/v acetonitrile, 500 μg/mL EDTA, and ascorbic acid). The mixture was filtered through a 0.45 μm Millipore filter and put into vials, and it can maintain stability for 24 h when stored at 4 °C. Catechins and caffeine analysis were assayed by using a Dionex UltiMate 3000 HPLC on a phenyl bonded column, using gradient elution with a thermostatically controlled column compartment and an ultraviolet detector set at 278 nm. Elution was performed according to the following system: flow rate, 1 mL/min; temperature, 35 ± 0.5 °C; injection volume, 10 μL; A = 9% acetonitrile (volume

% caffeine =

(A sample − A intercept ) × Fstd × Vsample × d × 100 Scaffeine × msample × 10000 × wDM,sample

where Asample = peak area of the individual component in the test sample, Aintercept = peak area at the point the caffeine calibration line intercepts the y-axis, Scaffeine = caffeine calibration line slope, V = sample extraction volume, d = dilution factor, msample = mass in grams of test sample, and wDM,sample = dry matter content of test sample. As a percentage by mass on a sample dry matter basis, the total catechin content of tea was calculated by the summation of individual catechins. Nongalloylated catechins and galloylated catechins were calculated as follows:

% total catechins = (% EGC) + (% C) + (% EC) + (% EGCG) + (% ECG) % nongalloylated catechins = (% EGC) + (% C) + (% EC) % galloylated catechins = (% EGCG) + (% ECG) Data Analysis. The instrumental data were constructed by several pattern recognition methods, such as principal component analysis (PCA) and stepwise linear discriminant analysis (S-LDA). PCA was performed by SIMCA (version 13.0.2), which was processed for the sake of having a better visualization of differences among the various objects by projecting them into the space of the first significant components. One-way ANOVA and S-LDA were processed by the SPSS statistical package (version 17.0 for Windows). The ANOVA analysis was used for calibration set (463 samples) covering four kinds of tea samples to identify statistical separation among the means. Experimental results were performed as means ± standard deviation (SD) by SigmaPlot (version 10.0). S-LDA was carried out to extract best discriminant variable separating tea categories, which enters or removes variables by analyzing their effects on the discrimination of the groups based on the Wilks’ lambda criterion. In our study, EGC, C, EC, nongalloylated catechins (N-GC), EGCG, ECG, galloylated catechins (G-C), total catechins (TC), caffeine (CAF), and total polyphenols (TP) were assayed; considering the differences in processing, we also calculated the ratios of TC and TP, N-GC and G-C, N-GC and TC, G-C and TC, EGCG and TC, and EGC and TC.26 For the discriminant four-class problem, we constructed three discriminant functions (DFs) by a linear combination of the extracted features. The F values to enter and to remove were set at 3.84 and 2.71, respectively. The approach yielded four equations that matched each tea category. For the discrimination of oolong and other teas, the values were set at 2.6 and 1.6, respectively. All 16 variables were used in the above analysis. For the classification of different fermentation degrees of oolong tea, the values were set at 1.0 and 0.5, respectively, and the first 10 variables without the ratios were utilized. The 9338



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Table 2. Polyphenols and Caffeine Content and ANOVA Analysis of Tea Categoriesa content (%) of calibration set no.

compound

1 2 3 4 5 6 7 8 9 10

EGC C EC EGCG ECG N-GC G-C TC CAF TP

GT (n = 164) 1.87 0.04 0.40 6.83 1.47 2.32 8.30 10.63 2.39 14.94

± ± ± ± ± ± ± ± ± ±

0.50 0.05 0.11 1.61 0.41 0.56 1.87 2.30 0.49 2.04

a a a a a a a a a a

WT (n = 50) 0.88 0 0.25 5.02 1.76 1.14 6.79 7.93 4.06 16.03

± ± ± ± ± ± ± ± ± ±

0.28 0.01 0.13 1.64 0.55 0.36 2.12 2.34 0.51 2.92

OT (n = 198)

b b b b b b b b b ab

2.14 0.08 0.68 5.07 1.13 2.90 6.20 9.10 2.52 16.76

± ± ± ± ± ± ± ± ± ±

0.90 0.05 0.27 1.79 0.27 1.14 2.01 2.54 0.58 3.11

c c c b c c b c ac bc

BT (n = 51) 0.15 0.07 0.27 0.37 0.41 0.50 0.79 1.28 2.87 11.73

± ± ± ± ± ± ± ± ± ±

0.30 0.05 0.32 0.40 0.50 0.66 0.86 1.34 0.95 3.76

d ac b c d d c d c d

F value, significance * 145.66 * 23.16 * 94.24 * 214.83 * 119.94 * 137.96 * 209.57* 213.78 * 109.88 * 45.67 *

Values are shown as means ± standard deviation, analyzed by one-way ANOVA, utilizing the Dunnett T3 test. Values that do not share a common letter within the same row are significantly different (P < 0.05) from each other.

a

classification criterion of the equations was as follows: each equation is evaluated by substituting the biochemical data, for which the highest outcome can be regarded as the tea category. In our study, original classification, cross-validation (with the characteristic of “leaving-oneout”), and external validation were used to test the performance of models based on the feature variables.

had significant differences (P < 0.05) of relative abundance with respect to categories. The highest compounds of green tea compared to other teas were EGCG, galloylated catechins, and total catechins as the steaming process preserves the greatest degree of polyphenols, resulting in the bitter and astringent taste, although caffeine was the lowest of all teas. With regard to white tea, ECG and caffeine showed the highest contents because the processes include neither pan-frying nor rolling. Because drying temperature and cell damage rates of white tea are lower than those of other teas, the sublimation of caffeine of white tea is much less than that of other teas. On the contrary, white tea showed the lowest content of C and EC; in addition, it also had lower EGCG, non-galloylated catechins, and TC compared to oolong and green teas.27 For oolong tea, total content and constituents of nongalloylated catechins and total polyphenols were the maximum compared to other teas, but total content and constituents of galloylated catechins were significantly lower than in green tea. It is possible that galloylated catechins are the dominant autoxidation compounds in the oxidation process of oolong tea. The results showed that green tea contained higher ECG and EGCG, but lower EGC, than oolong tea,1 but when compared to white tea it had lower ECG and higher EGC and EGCG. Except C, EC, and caffeine, other compounds of black tea were at the lowest level compared with other teas. Most polyphenols of black tea are dramatically decreased during enzymatic oxidation, involving polyphenol oxidase. Prevailing biochemistry of teas was mainly consistent with previous results.26 Therefore, the significant

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RESULTS AND DISCUSSION Theoretical Basis of Tea Classification. As listed in Table 2 and shown in Figure 1, it is remarkable that several

Figure 1. Differences of polyphenols and caffeine contents.

compounds were found to have statistically significant differences in tea categories. Almost all of the measured compounds

Figure 2. Scatter plots of (A) the three first principal components and (B) the two first principal components. 9339



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differences of chemical compounds of tea might provide the fundamental condition of tea classification and were preferred for further analysis using PCA and S-LDA. Tea Classification with PCA. The 463 samples were divided into four groups ranked according to their fermentation degrees. PCA calculations using a reduced number of variables were performed to have a better recognition of the sample information. It is useful for data visualization because a multiplex data set can be effectively observed when it is reduced to a lesser dimensionality. Three significant components were obtained when PCA was performed using the calibration set, jointly accumulating 83.1% of the total variance. Figure 2A shows the PCA 3D score plot with the first, second, and third components explaining 42.0, 30.6, and 10.5% of the total variance, respectively. All 463 samples are projected as small balls of different colors depending on their categories in the plot. Although there was no clear separation between the objects, some trends can be observed. As is shown in Figure 2A, clear separations of green tea (green balls) and black tea (red balls) samples can be detected, indicating that the degrees of fermentation were quite different from each other. The different producing methods may contribute to the distinctive chemical profile of green tea and black tea, besides their appearance differences. Normally, green tea is characterized by its nonfermentation by steaming process, whereas black tea is fully fermented by the fermentation process, which may explain their distinctive chemical profiles. Separation of white tea (yellow balls) and oolong tea (blue balls) samples was indistinct, indicating that their chemical profiles are much more similar in contrast to those of green tea and black tea. Although all 16 variables gave meaningful information to the classified differences in some degree, some might virtually detract from the simultaneous discrimination of the four tea categories. The results of separating fermented tea from nonfermented tea on the basis of the two first components were negative when some variables were concentrated by the procedure (Figure 2B). Therefore, a more effective feature selection and extraction procedure is desired. Tea Classification with S-LDA. To extract the most discriminating features for the simultaneous discrimination of the four tea categories, S-LDA was performed with the whole variables. Twelve variables apart from EGCG, EGC, N-GC, and the ratio of G-C and TC were selected by Wilks’ lambda criterion, whereas the three discrimination functions were constructed, describing 67.5% (DF1), 21.6% (DF2), and 10.9% (DF3) of the total variance, respectively, as shown in Table3. Furthermore, the absolute correlation and contribution of each variable were associated with discriminant function for reflecting its discriminant capacity. The feature variables contributing the most between green tea and oolong tea basically had significant differences, apart from the caffeine that was not of remarkable content, whereas EC was not significant between white tea and black tea (Table 2). Figure 3 shows the biplot and 3D score plot of canonical discriminant functions, with corresponding tea categories that have been satisfactorily separated. All samples are projected as different shapes and colors depending on their categories in the plot. Green tea and oolong tea have a very distinct separation in the biplot of DF2 and DF3 (Figure 3A), whereas white tea and black tea have a very distinct separation in the biplot of DF1 and DF2 (Figure 3B). Figure 3C shows that green tea and white tea can be effectively classified by DF1 and DF3.

Table 3. Prediction Results of S-LDA of Tea Categories Using the Original Cases and Cross-Validation and Corresponding Discriminant Functions and Chemical Compounds prediction of calibration set tea category

original cases (%)

cross-validated (%)

discriminant functions and chemical compounds

green oolong

98.2 (161/164) 96.5 (191/198)

97.6 (160/164) 94.9 (188/198)

white black

100.0 (50/50) 100.0 (51/51)

98.0 (49/50) 94.1 (48/51)

DF2 and DF3: EC, EGC/ TC, ECG, N-GC/TC, C, N-GC/G-C, CAF, TP DF1 and DF2: TC/TP, TC, G-C, EGCG/TC, EC, EGC/TC, ECG, NGC/TC, C, N-GC/G-C

total

97.8

96.1

Figure 3. Scatter plots of (A) DF2 and DF3, (B) DF1 and DF2, (C) DF1 and DF3, and (D) 3D score of DF1, DF2, and DF3.

Meanwhile, Figure 3D shows the S-LDA 3D score plot with the first, second, and third functions explaining 100% of the total system variance, showing the three significant functions can maximally separate all samples. In summary, four categories can be well separated by using the three DFs extracted. Oolong and Other Tea Classification with S-LDA. For the sake of establishing the International Standard of oolong tea, we also conducted the discrimination of oolong and other teas. Oolong tea usually has a higher unit price than green and black teas in the international tea market for its complex processing steps and limited supply.1 Hence, it is important to distinguish oolong from other teas. There were 198 samples of oolong tea and 265 samples of other teas (total 463 samples), which were performed by S-LDA, yielding one DF derived from the canonical discriminant fuction (Table S4 in the Supporting Information) and two equations derived from classification function coefficients (Table S5 in the Supporting Information) with the best capacity of their discrimination. Most samples could be successfully differentiated according to their chemical markers. When DF1 yields a positive value, the tea is classified as oolong tea (group centroids, 1.574) and when negative, other teas (group centroids, −1.176). 9340



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Figure 4. Scatter plots of (A) 48 samples elaborated by DF1 and DF2, (B) 48 samples of the three first principal components, (C) 47 samples from different producing regions (Anxi and Wuyi Mountain) of the two first principal components, and (D) 39 samples collected from high to low grades (special grade, levels 1, 2, 3, and 4) of the two first principal components.

of chemical compounds coupled with S-LDA in different fermentation degrees of tea, this study conducted S-LDA of different fermentation degrees of oolong tea. It is important for us to know whether polyphenols and caffeine are suitable for the same tea category with different fermentation degrees. According to the processing procedure, oolong tea can mainly be divided into three typical kinds with the lightest, intermediate, and highest degree of fermentation, respectively. In this study, 48 observations (Table S6 in the Supporting Information) of 3 classes were selected from 198 samples of oolong tea: faint-scent Tieguanyin (LO, lightest fermentation degree) and familiar-scent Tieguanyin (HO, intermediate fermentation degree) derived from Anxi and baking type oolong (BO, highest fermentation degree) derived from Wuyi Mountain in Fujian province. These samples were collected at distinct grades so as to confirm the applied effect of chemistry on tea classification. Figure 4A shows different fermentation degrees of oolong tea have been well-separated by the biplot of canonical discriminant functions. As expected, a clear separation of the three kinds of oolong tea was achieved with the DF1 (84.4%) and DF2 (15.6%) representing 100% of the total variance, with a 100% correct rate. PCA of the three kinds of oolong tea were also separated dramatically (Figure 4B). Although separations of the different fermentation degrees of oolong tea obtained by S-LDA and PCA are very encouraging results, we also want to ascertain how other features, such as producing region and quality, may affect the fermentation separations. In our study, PCA was put forward on 47 observations (Table S7 in the Supporting Information) of the above kinds of oolong tea derived from the two producing regions (Anxi and Wuyi Mountain, Fujian province). A clear separation according to fermentation degree was achieved, although producing areas were also considered (Figure 4C). To evaluate the effect of quality on fermentation discrimination, a

DF1 = 5.406 × EC − 1.616 × ECG − 0.553 × N‐GC − 0.124 × TC + 0.255 × TP − 0.014 × (N‐GC/G‐C) + 0.028 × (N‐GC/TC) + 0.035 × EGCG/TC + 0.082 × EGC/TC − 5.926

An unknown sample also can be predicted by the following equations. After the feature compounds are detected, we bring the data into the two equations, the result of which is higher and could be regarded as the tea category. This method is more convenient and visualized than DF1. Equ (oolong) = 12.246 × EC + 19.699 × ECG + 16.712 × N‐GC − 10.745 × TC + 3.716 × TP + 0.114 × (N‐GC/G‐C) + 1.127 × (N‐GC/TC) + 2.549 × EGCG/TC − 0.315 × EGC/TC − 110.037 Equ (others) = −2.621 × EC + 24.144 × ECG + 18.232 × N‐GC − 10.404 × TC + 3.015 × TP + 0.152 × (N‐GC/G‐C) + 1.050 × (N‐GC/TC) + 2.452 × EGCG/TC − 0.542 × EGC/TC − 93.193

Fermentation Degree Classification of Oolong Tea with S-LDA. For the purpose of further testing the applicability 9341



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In our study, 10 samples of each category produced in 2012 were collected from the dominant market to perform the external validation. Both the discrimination of the four kinds of tea categories and the discrimination of separating oolong tea from other teas were ideal. In external validation of the four different teas, two samples of oolong tea were discriminated wrongly; one wrongly identified sample was oolong crude tea, and the other one was oolong dust tea. The two kinds of tea are of poor quality, resulting in their having lower levels of biochemical compounds than other oolong teas. Besides, there were each samples of green tea and white tea wrongly classified. In external validation of oolong and other teas, one sample of oolong and two samples of other teas were identified incorrectly. Comparison of S-LDA and PCA. By S-LDA compared to PCA, sufficient information for the simultaneous discrimination of the four tea categories was provided, without nearly indistinctive separation, and a simple and effective discriminant model can be constructed on the basis of the reduced variable. In other words, it is possible that the chemicals or biochemicals can be extracted from the complex data when S-LDA was processed, which is most effective in sorting tea categories by different degrees of fermentation. Even so, PCA is still a useful tool for preliminary data analysis and effective visualization of the sample structure.24 Comparison of Green and Black Tea from Other Countries. To study whether this method can apply to international teas, we collected 10 samples of green tea from Japan and 9 samples of black tea from Sri Lanka and then conducted the two-class discrimination. Utilizing the same 16 variables, we carried out the S-LDA procedure; as expected, all 19 samples were classified correctly with the introduction of three variables. The variables were EGCG, TP, and the ratio of EGCG and TC. In addition, when these samples were added into the external validation to test the models of the four kinds of teas constructed by 463 samples, all 19 samples were discriminated correctly. This is a milestone for future research in chemical classification. In conclusion, a novel approach to accurately identify analytical markers for discriminating fermentatin degrees of tea was provided, based on potential chemical compounds analyzed by HPLC and spectrophotometry. Moreover, this study discovered that significant differences in chemical compounds may remarkably discriminate tea categories and their fermentation degrees, even if the teas are similar in appearance and taste, which can only be correctly distinguished by experienced tea tasters. The feature of chemical compounds caused by processing also seems dominate over other features (such as producing region and quality). Feature extraction and selection achieved by S-LDA could successfully construct robust models for tea discrimination and fermentation degree. In the future, greater international samples should be collected to improve the applicability of the models. Other standardized methods for catechin oxidation polymers, such as theaflavins or thearubigins, might become available and useful to discriminate the tea categories.19 Additional data will also contribute to establish more robust classification models of teas.

second PCA was put forward on 39 observations (Table S8 in the Supporting Information) of different kinds of oolong teas collected at distinct grades (special grade and levels 1, 2, 3, and 4). As expected, fermentation degrees still played a crucial role in sample clustering compared to quality grades, as shown in Figure 4D. Taking all of the results of PCA into consideration, it is possible to confirm that the processing technology clearly dominates over the other features (such as producing areas and quality) contained in the nonvolatile compounds of tea. On the basis of the results obtained in our work, fermentation discrimination by potential composition demonstrated that it is also applicative for different fermentation degrees of oolong tea. It was previously reported that taste reconstruction experiments were conducted to identify the key nonvolatile compounds in ready-to-drink green teas using HPLC, and regression models were developed to assess the intensities of bitterness and astringency.22 Our study confirmed that the HPLC method was also feasible for fermentation discrimination of various teas, even though the different fermentation degrees of the same tea category were of similar appearance and producing area. Moreover, our study provides an objective method based on chemical compounds for detecting fermentation degrees of teas. External Authentication of the Models of Teas. To test the merits of the model, we also conducted two strategies of validation. Foremost, the discriminant ability of the model carried out by S-LDA was tested using the original cases and cross-validation procedure, as shown in Table 3. The external validation was carried out as the secondary validation strategy, as shown in Table S3 in the Supporting Information. It is a useful method that takes the accurate classification of the validation set as the outstanding criterion to evaluate the established tea models. By the original cases program, the majority of samples (the correctly classified rate was 97.8%) of the four categories were correctly predicted. The results showed that there were few samples overlapping between green and oolong teas. There were three samples of green tea wrongly identified as oolong tea, whereas seven samples of oolong tea were misclassified as green tea (shown in Figure 3A). The major misclassified samples of oolong were of low grade. On the other hand, by the cross-validation procedure, the discriminant results were similar to the original cased program (the correctly classified rate was 96.1%). Furthermore, by the results of absolute correlation of each variable related to any discriminant function, which was not shown (in the SPSS processed results of S-LDA), it is possible to be the marker for each category. For instance, green tea and oolong tea were separated effectively by DF2 and DF3 (Figure 3A), indicating chemical compounds with the largest absolute correlation, with these two discriminant functions, contributing most to the distinction of the two kinds of teas. In the classification of oolong and other teas, 93.4% samples of oolong tea and 95.5% samples of other teas were classified correctly using DF1. The results showed that four samples of green tea, seven samples of black tea, and one sample of white tea were wrongly discriminated as oolong tea. Thirteen samples of oolong tea were misclassified as other teas. The results indicated that it became more difficult to classify oolong tea from other teas when two-class discrimination was conducted. It is supposed that the feature information on each tea category was lost by their combination to be a class.

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ASSOCIATED CONTENT

S Supporting Information *

Calibration set of four kinds of teas discriminant (Table S1), calibration set of the discriminant of green tea and black tea (Table S2), validation set of four kinds of teas discriminant 9342



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(Table S3), canonical discriminant function coefficients (Table S4), classification function coefficients (Table S5), calibration set of different fermentation degrees of oolong tea (Table S6), 47 samples of different fermentation degrees of oolong collected from different producing areas (Table S7) and 39 samples of oolong collected from high to low grades (Table S8). This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*(W.-J.S.) Phone: + 86 591 86392857; Fax: + 86 591 86392736;. E-mail: [email protected]. Funding

This study was kindly supported by grants from the Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (20133515110006) and the Public Welfare Industry Research Project (201410225-4) undertook by General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are grateful to Jin-lei Li, Abrar Muhammad (Department of Plant Protection, Fujian Agriculture and Forestry University, Fujian, China), Kai-jin Kuang (Cross-Strait Nature Reserve Research Center, Fujian Agriculture and Forestry University), and Mubasher Hussain (Institute of Applied Ecology, Fujian Agriculture and Forestry University) for their technical advice. We acknowledge Tie Guan Yin Group, Ba Ma Tea Co., Wuyi Star Tea Co., Fujian Tea Import and Export Co., Yan Shang Tea Co., Lapsang Tea Industry, Fujian Yuan Thai Tea Industry, Many Tasty Tea Co., Pin Pin Xiang Tea Co., Fujian Tian Hao Tea Co., Zhenghe Rui Ming Tea Co., and Guangdong Grand Tea Co. (China) for their assistance with sample collection.

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ABBREVIATIONS USED C, catechin; EC, epicatechin; EGC, epigallocatechin; EGCG, epigallocatechin gallate; ECG, epicatechin gallate; N-GC, nongalloylated catechins; GC, galloylated catechins; TC, total catechins; TP, total polyphenols; CAF, caffeine; ISO 145021:2005, determination of substances characteristic of green and black tea − part 1: content of total polyphenols in tea − colorimetric method using Folin−Ciocalteu reagent; ISO 14502-2:2005, determination of substances characteristic of green and black tea − part 2: content of catechins in green tea − method using high-performance liquid chromatography; ISO 1572, tea preparation of ground sample of known dry matter content; ISO 1573, tea determination of loss in mass at 103 °C; DF, discriminant function; LO, faint-scent Tieguanyin; HO, familiar scent Tieguanyin; BO, baking type oolong

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REFERENCES

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Discrimination of Chinese Teas with Different Fermentation Degrees

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