phenolic composition of black tea liquors as a means of predicting price and country of origin

J Sci Food Agric 1991, 55, 627-641

Phenolic Composition of Black Tea Liquors as a Means of Predicting Price and Country of Origin

Ian McDowell, John Feakes and Clifton Gay

Natural Resources Institute,* Central Avenue, Chatham Maritime, Chatham, Kent ME4 4TB, UK

(Received 17 July 1990; revised version received 6 December 1990; accepted 28 December 1990)

ABSTRACT

Seventy-seven black teas from seven countries were analysed by HPLC. The relative levels of the I1 most prominent peaks detected at 380 nm were determined. The teas were priced or scored by professional tea tasters. Statistical analysis of the HPLC results, with principal component analysis (PCA) and canonical variate analysis (CVA), highlighted the characteristic diflerences in phenolic constituent levels which contributed most powerfully to the discrimination between countries. Multiple regression was used to investigate the relationship between price (or score) and phenolic constituent level. Certain phenolic constituents seem to be important in determining quality but further work is recommended to ensure the signijicance of the observed trends.

Key words: Black tea quality, phenolic composition, HPLC, multivariate statistics, theaflavins, flavonol glycosides.

INTRODUCTION

The quality of plain black teas is valued on the basis of flavour and appearance. These two complex parameters are used by professional tea tasters as a means of pricing teas in relation to market conditions. Market conditions vary throughout the year and from year to year, due to changes in the supply and quality of tea caused by climatic and economic factors. Tasters often act as agents or brokers for both sellers and buyers of tea. In addition to purchasing teas the buyers also

* The Natural Resources Institute is an agency of the Overseas Development Administration.

627

J Sci Food Agric 0022-5142/91/$03.50 0 1991 SCI. Printed in Great Britain

628 I McDowell, J Feakes, C Gay

blend teas of different character to maintain the specific flavour of each product on the consumer market.

At present, assessment by tea tasters is the only comprehensive and rapid method of determining tea quality. Although tea tasters have great powers of discrimination, the objectivity of their evaluations has not been established. Hence there is a requirement among the producer countries to find objective methods of assessing flavour and quality which are not subject to market variations. This would allow agronomists, plant breeders and process engineers in the producer countries to undertake quality assessment of teas produced as a result of change in agronomic and manufacturing practice.

Progress has already been made in relating certain groups of tea constituents to quality. For certain teas (eg Malawi), the theaflavins are very important to quality (Hilton and Palmer-Jones 1975). They are highly correlated with quality and price for some producers (Ellis and Cloughley 1981) but others find little or no correlation (Roberts and Fernando 1981; Sivapalan et a1 1985; Owuor et a1 1986). The contribution of aroma constituents to tea flavour and quality has been assessed (Yamanishi et a1 1968; Howard 1978). These compounds are of great importance to producers of high value flavoury teas (Yamanishi et al 1968). Flavoury teas are defined as having the characteristic taste and aroma of fine teas; usually associated with high-altitude growing areas (BSI 1982). But the aroma constituents are considered to be of little consequence to producers of plain teas (Cloughley et al 1982). It is clear that quality, and hence value, depend on a complex of constituents, and it is therefore important to study a wider range of tea constituents and their relation to quality. In order to determine which chemical constituents make the greatest contribution to quality, principal component analysis (PCA) has been applied to the analysis of the aroma volatiles of coffee (Shimoda et al1985), whisky (Martin-Alvarez et al1988) and the sensory properties of coffee (Wada et al 1987) and wine (Otsuka et al 1985; Heymann and Noble 1989). The volatile profile of these commodities (including tea) is very complex: for example, coffee has more than 500 volatile constituents identified so far. Shimoda et al (1985) noted that the first and second principal components could discriminate between arabica and robusta coffee, differing blends, and degrees of roast. Martin-Alvarez et al(1988) could detect adulteration in whisky brands using PCA.

EXPERIMENTAL

Samples

Seventy-seven commercial teas (Table 1) were obtained from a London tea broker (George White & Co) of which 35 were priced and 42 scored by the company.

The scoring system was on a scale of 1-5 for each of six attributes (colour, strength, briskness, brightness, flavour and thickness with milk; BSI 1982) giving a total possible score of 30.

The pricing was done in the normal manner (ie tasted, evaluated and priced for the London market).

Phenolic composition of black tea liquors 629

TABLE 1 Origin and number of teas analysed

Countryfregion Code Priced Scored

Assam North India Bangladesh Sri Lanka Kenya Tanzania Malawi Total

A N B S K T M

8 1 I 5 5 3 6

35

~

10 4

16 0 0 0

12 42

Sample preparation

A 2-g sample of black tea was added to 200ml of distilled water in a round-bottomed flask (250ml). The tea was refluxed for 1 h using a Liebig condenser and immediately vacuum filtered through Whatman No 41 filter paper using a Buchner funnel and flask. The round-bottomed flask was washed three times with hot distilled water, and the washings were combined and filtered as described above. The liquor was transferred to a volumetric flask (250ml) and made up to volume with hot distilled water. After cooling for 20 min in a refrigerator the tea liquor was injected into the HPLC system.

HPLC equipment and conditions

The equipment consisted of an ACS 350/04 ternary pump (ACS, Macclesfield), a Rheodyne 7010 injector (Jones Chromatography, Cardiff) fitted with a 20-4 loop and a Pye Unicam PU 4020 UV/visible detector (Phillips Analytical, Cambridge) set at 380nm. The column and mobile phase gradient are described elsewhere (Bailey et al 1990).

RESULTS AND DISCUSSION

In this study the non-volatile tea constituents were monitored by HPLC (Bailey et a1 1990) at 380 nm (Fig 1) because the fermentation products of tea which are important to flavour and quality (theaflavins and thearubigins) absorb at this wavelength. This procedure gives a simpler chromatogram than would have been obtained using 280 nm. Recent work (Cattell and Nursten 1977; Bailey et al 1990; McDowell et a1 1990) has demonstrated that many of the constituents assumed to be thearubigins are flavonol glycosides. On this basis the major peaks of the chromatograms (Fig 1) were assigned as theaflavins (TF), flavonol glycosides (FG) or unknowns (X) (Table 2). The unknown peaks could be thearubigins.

While analysing the teas, small changes in the performance of the system were observed which may have been due to slight composition changes from one batch of solvents to another or to general ageing of the column. As a consequence the


Time (rnin)

Fig 1. A black tea chromatogram (Malawi; mAU=milliunits of absorption at 380 nm).

TABLE 2 Identities assigned (from Bailey et al 1990) and retention times of

the major HPLC peaks detected at 380 nm

Peak Approximate code retention

time (min)

~~

Identification

x1-4 x5-7 FG 1 FG2 FG3 FG4 FG5 F TF 1 TF2 TF3 TF4

16 19 20.5 21 22 25 26 37.5 38 40 41.5 42

A flavonol glycoside Unknown Rutin (quercetin rutinoside) Isoquercetin (quercetin rhamnoside) Quercetin glycoside Kaempferol glycoside Kaempferol glycoside A flavonol aglycone Theaflavin Theaflavin-3-gallate Theaflavin-3’-gallate Theaflavin-3,3’-digallate

integrator was in some cases unable to distinguish between closely eluting peaks which for the purpose of calculating correlation coefficients had to be combined, ie X1-4, XS-7, FG2-3 and F + FT1 (Table 2).

The data matrix corresponding to the 12 phenolic constituents (Table 2) observed in the chromatograms produced from the 77 tea samples involved in this study was submitted to two complementary methods of multivariate analysis, principal component analysis (PCA) and discriminant or canonical variate analysis (CVA).

Phenolic composition of black tea liquors 63 1

PCA was used to reduce the dimensionality of the data matrix by finding linear functions of the phenolic constituents which most powerfully summarised the overall variation among the tea samples. Correlations among the 1 1 phenolic constituents suggested that such a summary could be achieved in far fewer dimensions than the original 11. The new reduced representation can be interpreted by studying the relation between the principal component (PC) axes and the original phenolic constituents. At the same time this provides a simplified picture of the correlation structure of the phenolic constituents.

For discrimination between country (ie group differences), CVA is a more powerful tool than PCA. CVA is similar to PCA in creating new uncorrelated functions of the original variables but, whereas PCA maximises the overall variation explained by each successive component, CVA chooses functions which maximise variation between groups relative to within-group variation. Its application to the tea data aimed to find functions of the 12 phenolic constituents which would be maximally effective in discriminating one country's tea samples from another's. A statistical test is available for judging the power of the discrimination afforded by each successive discriminant function. Ideally, the power of the discrimination ought to be verified by applying the derived rules to new test samples.

Statistical analysis

Principal component analysis PCA suggested that a small number of components computed from the 12 original chemical constituents accounted for a large proportion of the total variation among the sampled teas. As Table 3 illustrates, almost 80% of the total variation is summarised by the first two PC.

A simplified picture of the overall variation between the 77 tea samples is provided by Fig 2 which is a plot of the PC scores for the first two principal components. This plot effectively summarises in two dimensions very nearly 80% of the overall sample variation, and suggests that there are certain characteristic differences in chemical composition between the samples drawn from different

TABLE 3 Principal component analysis results for 11 chemical components measured on 77 tea samples: the proportion of the overall sample variation accounted for by

principal components 1-1 1

Principal Variance Cumulative component (Yo) variance

("/.I 1 58.45 58.45 2 20% 1 79.26 3 6.66 85.92

4-1 1 14.08 100~00

I McDowell, J Feakes, C Gay

M

M M

M B M M

B M M B A N B A B

B N M M B 4,\\&'BA S S B B A A A B A B S

M 5 BA

N B P P

A A A

S

T K T

K

K K

T K

M

I l l I I I I I I I I I I I I I I I -35 - 1.5 0.5 2.5 4.5

Component 1

Fig 2. Scatter plot of the first two principal components of the sample scores data. Country codes as in Table 1.

tea-producing regions. Tea samples drawn from the same region tend to cluster more closely together than teas drawn from different regions. This is more noticeable for some regions than it is for others; for example, there is less obvious regional distinction between the geographically juxtaposed regions of Assam, Bangladesh and northern India, and likewise for Kenya and Tanzania. The PC plot also suggests the presence of relatively large intra-regional variation among the Malawi and Sri Lanka teas, and relatively small variation among the Assam and Bangladesh teas. The Malawi teas remain quite distinct from the other teas despite their larger internal variability.

Figure 3 is a plot of the loadings for the 11 original chemical constituents on the first two PC. This plot may be used to interpret the first two PC in chemical terms, and at the same time affords a two-dimensional summary of the correlations among the 11 original chemical constituents. The loading plot broadly confirms


I + 0

g 8 0.12- E

- m CL U C L

u

.-

.- a

c 0.02- : ln

-0.08 - 1

-0.18 -

/ 'F4

FG1 ,2 /---

I t

-0-13 007 0.27 0.47 067 First principal component

Fig 3. Plot of the loadings of the 11 chemical constituents on the first two principal components. Country codes as in Table 1.

the grouping of the constituents suggested by their chemical classification, the FG, the T F and the X exhibiting similar orientations within their respective groups. FG1,2 is the peak most closely associated with the first PC axis and X5,7 is the most heavily loaded on the second PC.

Canonical variate analysis Plotting the 77 tea samples on the first two PC axes suggested that there were certain distinct differences between teas associated with the region from which they were drawn. CVA was used to pursue the possibility of establishing a set of rules by which to discriminate between the. teas produced in different regions. Since within-regional variation is calculated as a pooled average of the variation within all seven regions, the analysis is most meaningful if the within-regional variation is uniform across regions. To the extent that the tea samples in this study were random and representative of the regions from which they were drawn, the plot of the 77 tea samples on the first two PC axes (Fig 2) gives the impression that this assumption might not hold entirely true. However, it is close enough to the truth to suggest that CVA might still provide a practically useful framework for discrimination. In any case, the integrity of the discrimination rule ought to be tested before being used in any important application.

Individual one-way analysis of variance (ANOVA) to assess the between-regional variation separately for each of the 1 1 phenolics indicated statistically significant


TABLE 4 The coefficients of the first four linear discriminant functions

of the phenolic components

Non-volatile Function component I 2 3 4

X1,4 FG3 X8 x5,7 FG1,2 FG4 FG5 F,TF1 TF2 TF3 TF4

Constant

- 0'0078 - 0.0029

0.0035 0.0002 0.0074

- 0.007 1 0.023 1 0.0168 0.0008 0.0108 0~0010

- 7.6127

- 0.0054 - 0.0005 - 0.0101 - 0.00 1 7 - 0.0048 -0.0127

0.0137

0.00 14 0.0033 0.0028

3.7232

- 0.0007

- 0.0085 - 00022

0.001 3 - 0.0027

0.0052 - 0.01 29 -0.0113

0.0177 0.0047

- 0'0034 -0.0215

4420

0.0047 - 0.0065

OQ05 1

0.007 1 - 0.0030

-0.0140 - 0.0034

0.0066 0.0067 00107 0.0079

- 3.6412

differences in each case. FG5 was associated with the most emphatic differences, followed by F and TF1, FG1,2 and FG4. However, some phenolics, even those associated with the greatest statistical significance, may tend to emphasise further the aspects of inter-regional difference emphasised by other phenolics. Thus, less significant variables, which emphasise different aspects of inter-regional difference, might have an important contribution to make. CVA was used to sift and extract all the useful contributions to inter-regional discrimination from among all 11 variables and combine them in the form of a set of simple linear discriminant functions (DF).

In view of the highly significant individual ANOVAs it was no surprise to find that CVA derived a number of highly significant DF. A simple x2 test for the significance of discrimination indicated that the first four linear D F were highly significant. The coefficients defining the first four D F are shown in Table 4.

Plots of the sample scores corresponding to the values of the first four D F illustrate the extent of the separation of the regional tea samples achieved by the discriminant analysis. Figures 4(a) and 4(b) are plots of the samples against canonical variate axes 1 and 2, and 3 and 4, respectively. DF 1 and 2 seem to effectively separate the Malawi teas from the other teas. They also highlight the Tanzania and Kenya teas as a distinct group, and likewise for the North India, Assam and Bangladesh teas, but within these groups there is considerable overlap between regions. D F 3 and 4 serve to distinguish the Tanzania from the Kenya teas and to some extent the Bangladesh from the Assam teas.

The relative contribution of the 11 phenolics to each of the four main D F can be assessed by the DF coefficients in Table 4. This shows the relative weighting of the respective phenolics in each of the DF. Consideration of these coefficients in conjunction with the results of the individual ANOVAs gives some justification for ascribing to FG5 and F,TF1 a large part of the inter-regional discrimination associated with the first two canonical variate axes depicted in Fig 4(a). The


2 4j. A

S S

M M S

M M M

K T

K K K T

K T

-0 -+< -3 0 First 3 discriminant 6 function 9 12

Fig 4a. Scatter plot of the sample scores on the first and second canonical variate axes. Country codes as in Table 1.

more subtle aspects of discrimination, corresponding to axes 3 and 4, would seem to be particularly associated with non-volatiles TF3 and FG4.

The DF derived using CVA may be used to predict the regional origin of an unknown tea based on its phenolic chemical constitution. This prediction can be gauged by applying the DF as an allocation rule to the 77 sample teas included in this study. The CVA was repeated with 19 teas withheld in order to assess realistically the predictive capability. The DF fitted to the remaining 58 peak samples were then used to classify the withheld samples. This exercise was repeated for other non-overlapping subsets of 19, 19 and 20 withheld samples, so that all 77 teas were classified by independently derived DF. As Table 5 reveals, the overall classification success rate was 77%, although the rate of success clearly varies between regions. The teas from Malawi, for example, seem to be associated with teas of relatively distinct quality, which are relatively easy to classify correctly. By contrast, the North India teas were difficult to classify, being easily confused with teas from Assam and Bangladesh.

Analysis of tea ‘value’ Price information was available for 35 of the 77 tea samples. Only the scores as judged by an experienced tea buyer, which reflect the quality of the teas, were available for the remaining 42 samples.

Inter-regional variation with respect to both ‘value’ variables was investigated


A A

A A

M A N A N

t T T

c -33 s

1 l 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -39 -1 9 0.1 2.1 41

Third discriminant function

Fig 4b. Scatter plot of the sample scores on the third and fourth canonical variate axes. Country codes as in Table 1.

TABLE 5 Prediction of origin by the discriminant functions derived

using CVA

Actual Predicted region Total region A B N M T K S

A 1 2 5 0 0 0 0 1 1 8 B 4 1 8 0 0 0 0 1 23 N 2 2 1 0 0 0 0 5 M 0 0 0 18 0 0 0 18 T 0 0 0 0 2 1 0 3 K 0 0 0 0 1 4 0 5 S 0 0 0 0 1 0 4 5

using one-way ANOVA. Very clear inter-regional variation was indicated in each case. The regional mean values, for both price and score, are plotted in Figs 5(a) and (b) respectively. Ninety-five per cent confidence intervals, based on the pooled estimate of variance obtained from the ANOVA, for the regional means are also plotted.

The evidence of Figs 5(a) and (b) concerning regional variation in tea sample


Region R e g i o n

Fig 5a. Mean regional prices and 95% confidence intervals (based on one-way ANOVA).

Fig 5b. Mean regional scores and 95% confidence intervals (based on one-way ANOVA).

‘values’, coupled with the earlier evidence of the multivariate analysis concerning regional variation in phenolic chemistry, poses an interesting question: to what extent are the differing regional tea ‘values’ the product of the evident differences between them in phenolic chemistry? Multiple regression of ‘value’ on the chemical data would seem to offer a powerful tool for investigating this question, but such an approach is unfortunately fraught with potential hazards; the dangers of using regression to infer ‘cause/effect’ relationships are well documented (Sokal and Rohlf 1969). These dangers are particularly prominent in this case because the sample sets from each region were not collected, nor were their ‘values’ evaluated so as to eliminate all other possible aspects of tea character and tradition, and the phenolic composition could vary between regions. Thus it could well be that some other important regional trait on which we have no information is the true, or most important, cause of the variation in ‘value’; the non-volatile chemical constituents seeming to explain ‘value’ only in so far as they are associated with this other trait. It could be that a substantial proportion of the inter-regional price differences is dependent on the phenolics, such as the thearubigins or catechins, not determined here. The volatile composition may make an important contribution to price differences together with any traditionally held preconceptions or market notions which work to the advantage of the teas from one or more of the regions included in this study.

The realisation that phenomena of this kind almost certainly exist is easily appreciated by considering, as an example, the very clear separation, on ‘value’ grounds, between the Assam and the Bangladesh teas. Figures 5(a) and (b) show that the 95% confidence intervals for means of both price and score are separate for these two regions. In fact, even the lowest valued Assam samples had higher prices/scores than the highest valued Bangladesh samples whereas no such clear distinction is possible between these regions on grounds of the best linear discriminant functions derived from CVA on the chemical data.


TABLE 6 General regression models chosen by stepwise regression

Component Coe@cient t-Value

For prices (adjusted R 2 = 86.0%) *** *** TF2 0.39 7.26

FG5 - 0.25 - 6.47 F,TF1 0.23 4.97 ***

For scores (adjusted R2 =41.3%) 4.56 *** TF4 0.05

FG5 0.06 3.26 ** ~~ ~ ~ ~~

Note that in Table 6 and in all subsequent tables the symbols ***, **, * and NS denote statistical significance at 0.1%, 1% and 5% and not significant, respectively.

One way to overcome the possible confounding of the phenolic data with other variables, at any rate those that may vary between regions, is to look for a regression on the chemical data which is effective in explaining the intra-regional variation in tea ‘values’. This intra-regional regression is equivalent to a regression of ‘value’ on the chemical data after eliminating the inter-regional variation; that is to say, having included separate mean effects for each region in the model.

Stepwise regression, using an F-ratio of 4.0 as an exclusion/inclusion criterion for all potential regressors, suggested the general multiple linear regression models shown in Table 6. The selected models for both ‘value’ measures have clear significance in purely statistical terms, but their practical potential for inferring a cause/effect relationship must be subject to the doubts already expressed above. The fact that the two models are quite different from one another, in terms of the phenolic components selected (Table 6) to explain variation in ‘value’ between samples, further complicates the argument. Furthermore, the model selected for one definition of ‘value’ performs significantly poorly for the other definition when compared with the performance using its own model. This casts some doubt over whether the two ‘value’ measures are actually measuring the same thing, if not over the models themselves.

The two regression models in Table 6 fitted intra-regionally (ie after fitting separate regional intercepts to allow for mean regional differences in ‘value’) gave the coefficients shown in Table 7.

The null intra-regional model, that is the model allowing for mean regional differences only, without any modelling based on the chemical data, has an adjusted R 2 of 76.7% for the price model and 66.6% for the score model.

These statistics demonstrate the large proportion of inter-regional, relative to intra-regional, variation in the total inter-sample variation and provide a baseline against which to judge the contribution of the phenolic components to the explanation of intra-regional variation. The contribution is not huge, but statistically significant in the cases of F,TF1 (price model) and FG5 (score model). Furthermore, the signs of the coefficients for these regressors are in agreement with those of the general regression model.


TABLE 7 Selected general regressions used to model intra-regional

variation

Component CoefJicient t-Value

For prices (adjusted R 2 = 89.0%) TF2 0.16 1.87 NS FG5 -0.14 - 1.42 NS

F,TF1 0.19 3.35 **

TF4 0.02 1.36 NS FG5 0-03 2.75

For scores (adjusted R2 = 74.7%)

**

TABLE 8 Intra-regional regression models chosen by a stepwise

procedure

Component CoefJicient t-Value



F,TF1 0.16 3.53 **

** X8 - 0.03 - 2.89

A more complex stepwise regression analysis of the intra-regional modelling problem, again using an inclusion/exclusion F-ratio of 4.0, produces the results exhibited in Table 8. It is interesting to note that, if the inclusion criterion were relaxed to allow readier inclusion of regressors in the model, then the intra-regional price model would involve exactly the same phenolics as the general model.

A possible method of corroborating the intra-regional regression models is to fit separate models for the tea samples within each region and look at the consistency of the regression coefficients. This exercise is complicated by the small numbers of samples from each region. Indeed three regions were not even represented in the score data, and a further region with only one priced sample was excluded from the analysis. The individual intra-regional regression model coefficients in Table 9 were obtained for the best stepwise models already determined.

To look at the consistency of the regression coefficients between regions the analyses of variance in Table 10 (based on an extra sums of squares approach) may be constructed.

Tables 6 to 10 indicate that while all regions do not demonstrate closely similar intra-regional regression coefficients for the stepwise selected regressors (one region even contradicts the sign of the pooled estimate of the coefficient in each case), there is no statistically significant evidence, based simply on these data, to refute the hypothesis of a common regression within each region.

640 1 McDowell, J Feakes, C Gay

TABLE 9 Selected intra-regional regressions fitted to individual regions

~ ~ ~ ~ ~ ~ ~~~~

Region No of samples Coeficient t-Value

A B M T K S

Pooled

A B N M

Pooled

For price (coefficient of F,TF1) 8 0.66 I - 0.40 6 0.0 1 5 0.23 6 0.14 I 0.05

34 0.16

For score (coefficient of X8) 10 - 0.05 16 0-02 4 -0.10

12 - 0-03 42 - 0.03

** 3.74 - 1.45 NS

0.02 NS 3.83

2.32 NS 0.42 NS 3.53

*

**

- 0.41 NS 0-93 NS

- 4.29 NS - 2.88 - 2.89

* **

TABLE 10 An analysis of variance for the regional consistency of the chosen

intra-regional regression models

Source df ss MS F

Between regions Pooled F,TFl Separate F,TF1 Residual Total

Between regions Pooled X8 Separate X8 Residual Total

5 1 5

22 33

3 1 3

34 41

For prices 26 511

1999 1326 3 013

32 849

For scores 538.6 44.4 33.2

163.6 779.8

5 302 38.71 *** 1 999 14.59 ***

265 1.94 NS 137

119.6 31.33 *** 44.4 9.23 ** 11.1 2.30 NS 4.8

There are two aspects of the regression results which support the hypothesis that phenolic chemistry plays a real role in the determination of tea ‘values’. The first is that similar phenolics appear to be capable of explaining intra-regional as well as inter-regional variation in ‘value’. Secondly, the hypothesis of a consistent regression within all regions could not be rejected. The fact that different phenolics seem to be important, depending on whether price or score is the ‘value’ measured, indicates that the definition and derivation of the ‘value’ data require further investigation.


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phenolic composition of black tea liquors as a means of predicting price and country of origin

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