Anthocyanins as parameters for differentiating wines by grape variety, wine-growing region, and wine-making methods

JOURNAL

OF FOOD COMPOSITION

AND ANALYSIS

3,54-66 (1990)

Anthocyanins as Parameters for Differentiating Wines by Grape Variety, Wine-Growing Region, and Wine-Making Methods M. L. GONZALEZ-SAN

JOSE,’ G. SANTA-MARIA,*

AND C. DIEZ~

*Institute Fermentaciones Industriales. CSIC, Juan de la Cierva 3. 28006 Madrid; and tlnstituto Estudios Avanzados lslas Baleares, CSIC, Km 7.850 Carretera Vallde moss, 07071 Palma de Mallorca Received August 16, 1989, and in revised form April 27, 1990 Applying factor analysis to the results of the analysis of anthocyanins of different varietal wines from Spain makes it possible to define a regional factor, a variety factor, and a winemaking method factor. Applying discriminant analysis makes it possible to group the samples according to wine-making method, grape variety, and wine-growing region. The grape variety and wine-growing region affect such anthocyanins more selectively than wine-making method. The behaviors of anthocyanins are different according to their molecular make-up. 8 1990 Academic Press, Inc.

INTRODUCTION

One of the problems still facing the wine-producing sector is the proper characterization and identification of wines. At present, certificates of origin merely contemplate a series of specifications and regulations that tend to be more directly concerned with growing conditions (type of soil, yield per hectare, pruning, etc.) than with the characteristics of the final product. Moreover, even with expert tasters, sensory analysis, which is frequently employed by regulatory bodies, is not always able to guarantee correct results. Consequently, there is a clear need to establish objective methods for characterizing wines on the basis of reproducible physicochemical parameters, with a view to standardizing control procedures. In an attempt to overcome these difficulties, attention has, in recent years, turned to statistical classification methods for differentiating wines according to grape variety and wine-growing region (Kwan and Kowalski, 1980; Marais et al., 198 1; Symonds and Cantagrel, 1982; Cabezudo et al., 1983; Van der Voet et al., 1984 and Moret et al., 1986). Multivariate analysis has enabled grape and wine constituents to be singled out for use in classifying wines according to geographical region of origin or grape variety. Such constituents range from conventional parameters such as alcohol content and pH, through amino acids, proteins, and volatile compounds, to trace substances and elements such as rubidium and lithium (Rapp and Guntert, 1985; Farina, 1986; Maarse et al., 1987 and Et&ant et al., 1988a). Phenolic compounds have also been studied with this end in mind. Both individual compounds (Estrella et al., 1984; Hemandez et al., 1986; Et&&ant et al., 1988b) and ’ To whom correspondence and reprint requests should be addressed. 0889-1575/90 $3.00 Copyright 0 1990 by Academic Press, Inc. All rights ofreproduction in any form reserved.

54

ANTHOCYANINS

AS

PARAMETERS

FOR

DIFFERENTIATING

WINES

55

families of phenols (Santa-Maria etal., 1986) have been used, and both methods have succeeded in distinguishing between grape varieties and classifying wines. To our knowledge, no single study yet published has jointly considered the three factors determining the physicochemical composition of wine: grape variety, winegrowing region, and wine-making method. This paper presents the results of applying discriminant analysis to analytical data for wines made from five different grape varieties in five separate Spanish wine-growing regions with denominations of origin (DO) and different edaphic and climatic conditions using various wine-making methods. Four of the five grape varieties considered (Tempranillo, Garnacha, Monastrell, and Bobal) are the most commonly used varieties in Spanish red wines. Principal components analysis was also applied in order to examine the relationships between wine anthocyanins and wine-growing region, grape variety, and winemaking method. MATERIALS

AND

METHODS

Samples Forty-four young red wines from the 1987 vintage were analyzed. Two replications of all analyses were performed. Grape variety samples comprised Tempranillo grapes, 11 samples: Garnacha grapes, 6; Monastrell grapes, 11; Bobal grapes, 10; and Cabernet-sauvignon grapes, 6. Regions of origin (with denominations of origin) were Rioja, 10 samples: La Mancha, 9; Tierra de Barros, 5; Utiel-Requena, 10; and Jumilla, 10. Wine-making methods employed were traditional red wine-making method, 27 samples; carbonic maceration, 7; claret wine-making methods, 6; and “doble pasta” (double pomace) wine-making methods, typical of the Utiel-Requena denomination of origin region, 4. This latter method consists of fermenting a mixture of must and the solid matter from the grapes, in the proportion of 2: 1 solids to must (weight by volume), hence the term “double pomace.” Traditional red wine-making method consists of fermenting crushed red grape berries; carbonic maceration consists of fermenting whole red grape berries in a closed container and claret wine-making methods consists of fermenting a mixture of crushed red grape berries and white grape must.

ChemicalAnalysis Total anthocyanins were analyzed according to variations in color with changing pH in the test medium (Paronetto, 1977). Tonality and colorant density were measured by optical density at 420,520, and 620 nm (Glories, 1986). Individual anthocyanins were analyzed by reversed-phase HPLC with wines injected directly (GonzilezSan Jose et al., 1987) using a Waters liquid chromatograph equipped with two Model 540 pumps, a U6K injector, a dual-channel, fixed wavelength, uv-visible light detector, and a 15 cm long, 0.45 cm i.d. Novapak Cl8 column. A linear elution gradient system was applied for 25 min, starting with 11% methanol in water:formic acid (90: 10) and ending with 36% methanol in water:formic acid (90: lo), followed by 15 min of an isocratic system in these final solvent conditions. Detection was carried out at 3 13 and 546 nm. Anthocyanins were quantified and expressed as mg/liter of malvidin-3glucoside (Gonzalez-San Jose et al., 1988).

56

GONZALEZ-SAN

JOSE, SANTA-MARIA,

AND DIEZ m

P

- 313nm

7 2

2

:

h 0

5

10

-546 nn n 15

20

25

30

35

40

45

L

FIG. 1. HPLC analysis ofgrape ( Vitis viniferu) anthocyanins I, 2,3,4, and 5 are 3-monoglucoside derivatives ofdelphinidin, cyanidin, petunidin, peonidin, and malvidin; 6,8, 10, 12, and 13 are acetyl derivatives of delphinidin, cyanidin, petunidin, peonidin, and malvidin; 14, 16, 17, 18, and 19 are p-coumaryl derivatives of delphinidin, cyanidin, petunidin, peonidin. and malvidin; 7 is petunidin 9 is peonidin; I I is malvidin; 15 is malvidin-3-(6-caffeyl)-glucoside.

Statistical Analysis

Principal components analysis was performed on all samples combined, using program 4M of the BMDP (Biomedical Computer Programs) statistical package (Frane et al., 1983). The factor matrix was estimated from correlation matrix, and the factors were rotated using the Varimax method to facilitate the interpretation of results. Factor loadings lower than an absolute value of 0.25 were set to zero. Only factors greater than eigenvalues of unity were used. Discriminant analysis was carried out by calculating classification functions based on the linear relationships between selected variables. These functions took the form Zi = CfJi+ 2 (Cj;.X,), where Z; is the discriminating score of the classification function, Coi is a constant, Cj; is a weighting coefficient, and Xj is the discriminating variable.

ANTHOCYANINS

AS

PARAMETERS

FOR

TABLE

DIFFERENTIATING

WINES

57

1

UNROTATE FACTOR LOADINGS FOR ANALYSIS TO PRINCIPAL COMFONENTS Variables

Factor I

Dp-3gl u cy-3g1 u Pt-3gtu m-3gIu tlv-3gI u Dp3(6acet)gl” Pt Cy3(6acet)glu m Pt3(6acet)gl” MV mb(6acet)gl” Mv3 (6acet) gl ” Dp3 (6pcoun) gl” Mv3(6caf)glu Q’3 (~PCOUII) gl” Pt3(6pcoun)glu m3(6pcoun)gl” Mv3 (6pcoun) gl ” Tonal 1ty Colarant D Total Acy sun

square

II

III

IV

0.66 0.09 0.66 0.44 0.69 0.43 0.59 0.55 0.54 0.60 0.31 0.62 0.54 0.65 0.76 0.16 0.76 0.54 0.75 -0.47 -0.06 0.39

-0.16 -0.16 0.24 0.64 0.40 -0.56 -0.62 -0.66 -0.57 -0.59 -0.07 0.16 -0.39 0.43 -0.19 0.22 0.39 0.72 0.62 0.23 -0.23 -0.41

-0.10 0.33 0.00 0.27 -0.04 0.57 0.06 -0.22 0.04 -0.26 0.47 -0.17 -0.46 -0.11 -0.13 0.49 0.04 -0.10 -0.13 -0.37 0.65 0.69

0.49 0.71 0.29 0.02 -0.05 0.17 0.22 0.10 -0.44 -0.27 -0.56 -0.17 -0.16 0.05 0.17 -0.05 0.01 -0.x) -0.06 0.13 -0.02 -0.02

-0.14 -0.21 -0.16 0.41 -0.02 0.05 0.29 0.29 -0.29 0.00 -0.35 -0.11 0.43 -0.09 -0.25 -0.26 -0.15 0.23 0.06 -0.03 0.27 0.27

7.9663

4.5900

2.7556

1.6931

1.24%

V

Note. Dp, delphinidin; Cy, cyanidin; Pt, petunidin; Pn, peonidin; Mv, malvidin; Acet, acetyl; Caf, caffeyl; Pcoum, p-coumaryl; Glu, glucoside; Acy, anthocyanins; D, density.

The values Of Coi and Cji were estimated using program 7M of the BMDP statistical package (Jennrich and Sampson, 1983), by maximizing the intergroup variance:intragroup variance ratio. Stepwise selection of variables was employed. At each step the variable with the greatest discriminating power, as measured by an Fstatistic, was selected. Program 7M also furnished a canonical analysis of the selected variables, enabling the differences between groups of samples to be represented in the plane formed by the first two canonical coordinates. Moreover, using program 7M it was also possible to classify the samples not used in calculating the classification functions and to obtain an estimate of the percentage of correct classifications. Programs 4M and 7M were run on a CYBER 155/855 computer (Control Data Corporation). RESULTS AND

DISCUSSION

A total of 19 anthocyanins, derivatives of cyanidin, delphinidin, malvidin, peonidin, and petunidin were separated by HPLC analysis (Fig. 1). The principal components applied to all the sample data combined yielded five factors with eigenvalues greater than unity, accounting for 83.02% of the variance (Table 1). Table 2 sets out the rotated factors, showing that most of the compounds analyzed were correlated with factors I and II. The pcoumaryl derivatives and monoglucosides were generally correlated with a single factor; in contrast, the acetylated derivatives, anthocyanidins, and the only

58

GONZALEZ-SAN

JOSE,

SANTA-MARIA, TABLE

AND

DIEZ

2

ROTATEFACTORLOADINGSFORPRINCIPALCOMPONENTSANALYSISTOALLTHESAMPLEDATA; ROTATEFACTOR LOADINGLESSTHAN OREQUALTOANABSOLUTEVALUEOF 0.25 SET ~00.00 Variables Factor I Hv3 (S~COV~) g I ” llv-3-glu Dp3 (bpcovn) g I ” m3(6pcovn)glu Pt3 (6pcoun) g I” Pt-3-glu m-3-g lu Cy3(6acet)gl” Hv3(6acet)glu Pt PtJ(6acet)glu Tonal 1ty m3(6acet)glu Colourant D Total ACY Dp3(6acet)gl u nv m cy-3-gl” Dp-J-gl u tlv3(6caf)glu Cy3(6acet)glu x Real

0.96 0.94 0.92 0.66 0.64 0.63 0.74 O.c0(.02) 0.00(.21) 0.00(.06) O.oo(. IO) 0.00(.05) 0.52 -0.27 0.5! O.oo(-.06) O.oo(. 14) 0.00(.03) O.OO(-.07) 0.43 0.47 O.OOC.22)

II 0.00(.03) 0.00(.2!) O.oo(. 15) O.oo(-.ov) 0.00(.09) O.OOC.23) O.OO(-.12) 0.93 0.66 0.65 0.63 -0.61 0.59 0.00(.02) O.OO(-. 15) 0.50 O.cxJ(.O2) 0.62 O.oo(-.03) 0.44 0.46 -0.26

III O.OD(-.03) O.c0(.02) O.oo(-.ov) 0.00(.07) O.co(.Ol) O.oo(-.03) 0.46 O.oo(-.02) O.oo(-. 15) 0.27 O.oo(-. 1.3) -0.37 O.OO(-.14) 0.67 0.76 0.56 0.26 O.co(-.05) O.co(-.OV) O.OD(-.13) O.oo(-. 19) 0.31

IV 0.00(.09) O.OO(. 16) O.cKl(.ll) O.OO(-. 10) C.O0(.22) 0.00(.40) O.oO(-.23) O.OO(-.04) O.oo(-.12) O.oo(-.06) 0.34 -0.43 0.36 O.c0(.20) O.OO( .07) 0.29 0.61 0.72 O.oo(-.ov) O.co(-.Ol) 0.26(-.Ol) 0.36

V O.oo(-.09) 0.00(.06) O.oo(. 14) -0.32 O.oO(. 16) 0.44 O.OO(-. 13) O.OO(. (7) O.CO(-.24) 0.31 O.co(-.03) O.oo(-,201 0.00(.07) 0.00(.04) 0.00(.04) 0.47 O.OO(-. 13) 0.00(.01) 0.61 0.62 0.42 0.00(.16)”

value.

No&. Dp, delphinidin; Cy, cyanidin; Pt, petunidin; Pn, peonidin; Mv, malvidin; Acet, acetyl: Caf. caffeyl; Pcoum, p-coumaryl; Glu, giucoside; Acy, anthocyanins; D, density.

caffeic acid-esterified derivative (Mv-3-(6-caf)-glu) were correlated with more than one factor. Factor I was basically correlated with the 3-monoglucoside derivatives of the major anthocyanidins delphinidin, petunidin, peonidin, and malvidin (Dp-3-mglu, Pt-3mglu, Pn-3-glu, and Mv-3-glu) as well as with their p-coumaric acid derivatives (Dp, Pt, Pn and Mv -3-(6-pcoum)-glu) (Table 2). Factor II was correlated mainly with the five acetylated derivatives (Dp, Cy, Pt, Pn and Mv-3-(6-acet)-glu) and with the anthocyanidins petunidin (Pt) and peonidin (Pn) (Table 2). Factor III was correlated chiefly with colorant density and total anthocyanins, but also, to a lesser extent, with delphinidin-3-(6-acetyl)-glucoside and peonidin-3-glucoside (Table 2). Distribution of the samples in the planes delimited by factors I and II, and factors II and III (Figs. 2a and 2b) shows the relationship between these factors and grape variety, wine-growing region, and wine-making method. However, there were differences: regional influences predominated over varietal influences in factor I, while in factor II the converse was true. Distribution of the wines from a given region around factor II in all cases resulted in distinct grouping according to variety. Distribution around factor I, on the other hand, did not lead to those same results. For this reason, factor II can be defined as the varietal factor, albeit with the influence of wine-growing region. Distribution of the wines from a single grape variety around factor I produced grouping of samples according to wine-growing region. Consequently, this factor can be regarded as the regional factor, influenced by variety.

ANTHOCYANINS FI

AS PARAMETERS

FOR DIFFERENTIATING

WINES

59

I

3.

2.

04R

l-

G4R

o. ‘l-ii!&

T& T4A T2A

Ea.

-1.

II

3-

2-

l-

o-

- l-

-2.

T2A

c -1

0

i

2

3

FII

(b) FIG. 2. Distribution of wines (standardized data) in the two-dimensional coordinate system defined by factors I and II (a), and factors II and III (b), selected in principal components analysis of the all sample wines. Grape varieties: M, Monastrell; G, Gamacha; S, Cabernet-Sauvignon; T, Tempranillo; B, Bobal. Wine-making method: 2, claret: 3, carbonic maceration; 4, traditional red wine making; 5, “doble pasta” (double pomance) wine making. Wine-growing region: R, Rioja; L, La Mancha; A, Tierra de Barros; U, Utiel-Requena; J, Jumilla.

As pointed out above, factor III exhibited high correlations with colorant density and total anthocyanins (Table 2). This factor caused grouping of samples according to wine-making method and was thus defined as the wine-making method factor. Correlation between this factor and individual anthocyanins was practically negligible, suggesting that wine-making methods affect such compounds in a less selective

60

GONZALEZ-SAN

JOSE, SANTA-MARIA,

AND DIEZ

T3R

2

T4R T3R M4J M4J

1

T3R T4R

M4J

0 G4R G4R

-1

G41

-2

.~

I IV 0 ; ii -2 -1 2 FIG. 3. Distribution of wines (standardized data) in the two-dimensional coordinate system defined by factors, IV and V selected in factor analysis of all sample wines. Grape varieties: M, Monastrell; G, Gamacha; T, Tempranillo; S, Cabernet-Sauvignon; and B, Bobal. Wine-making method: 2, claret; 3, carbonic maceration; 4, traditional red wine-making; and 5, “doble pasta” (double pomance) wine-making. Winegrowing region: R, Rioja; L, La Mancha; A, Tierra de Barros; U, Utiel-Requena; and J, Jumilla.

manner than do grape variety and wine-growing region. This is readily explained in that wine-making methods affect all the anthocyanins equally, because they all present similar solubility and extractability levels and are all located in the same parts of the berry. The only anthocyanin that appeared to behave differently was peonidin-3glucoside, which tends to accumulate in the epidermal layers near the pulp and is consequently the main anthocyanin pigment in the pulp in red wine varieties, (Bakker and Timberlake, 1985). Factor IV was basically correlated with malvidin and peonidin and hence may be associated with hydrolysis. Distribution of the samples in the plane formed by factor IV and factor V (Fig. 3) highlighted the tendency of the samples of wines from a single wine-growing region to group according to grape variety. The behavior of factor IV was, thus, similar to that of factor II and was dependent upon grape variety and wine-growing region, with the influence of grape variety prevailing. It would therefore appear that hydrolysis is mainly dependent upon grape variety. In fact, the percentage of the anthocyanidine P9 and Pl 1 in the total anthocyanins was higher in the Garnacha (2.06%) and Monastrell(2.00%) grapes than in the other varieties: Tempranillo (0.83%), Bobal (1.37%), and Cabernet-Sauvignon ( 1.48%). Factor V was correlated with delphinidin-3glucoside and cyanidin-3-glucoside, and to a lesser extent with petunidin-3-glucoside, delphinidin-3-(6-acetyl)-glucoside,

ANTHOCYANINS

AS PARAMETERS

FOR

TABLE VARIABLES

GROUPED

Vat-lableS (X,)

61

3

ACCORDING

Colourant Cy-3glu

D

m-39iu Cy3(6acet)gI” Pt3(6acet)glu nv Mv3(6acet)glu Pn3 (6pcoun)

g I”

%J

Cy, cyanidin; D, density;

Pt, petunidin; ~j,

TO WINE-MAKING

Classlficatlcn RED WINE-MKIffi

Note.

WINES

SELECTEDANDTHECLASS~FICATIONFUN~IONSFORTHEDISCRIMINANTANALYSISFOR ALL THE WINES,

glucoside;

DIFFERENTIATING

Funct (ai j) CAREf4IC MACERAT I ON

49.40

64.72 -7.61 6.45 -7.92 6.15 23.65 1.59 -6.68 -59.80

Pn, peonidin;

-5.67 4.48 -7.54 14.00 Il.53 0.37 -5.51 -41 .Ol

Mv,

malvidin;

METHOD

non “CC&E PASTA”

214.46 -45.09 12.41 14.21 -24.85 70.37 3.32 -t5.90 -342.62

Acet,

acetyl;

a.ARET

21.14 1.74 2.16 -3.04 1.59 a.79 0.71 -2.57 -9.72

Pcoum,

p-coumaryl;

Glu,

constant.

and malvidin-3-(6-caffeyl)-glucoside. This factor was, therefore, related to anthocyanin biosynthesis, since, as Harborne (1967) pointed out, cyanidin is the first anthocyanin pigment to appear in nature, and from it delphinidin and pelargonidin are derived biogenically, with the rest of the anthocyanins derived from these last two. The samples did not display any distinct grouping behavior for factor V, and hence it was not possible to establish a relationship between this factor and the three characteristics considered in the present study. From the foregoing discussion it appears, then, that the selected factors relating anthocyanin composition in the wines to the three characteristics, i.e., grape variety, wine-growing region, and wine-making method, were themselves related to more than one of these characteristics. Only factor III was clearly related to wine-making method. Nonetheless, it has been seen that factor I was primarily related to winegrowing region, while factors II and IV were mainly related to grape variety. The behavior of the anthocyanins was different according to their molecular makeup. The p-coumaric acid derivatives (factor I) were more closely related to region than to variety. The acetylated derivatives (factor II) were related both to variety and wine-growing region. This would, at first glance, seem to contradict the findings of Roggero et al. ( 1988), who characterized French varietal wines on the basis of the percentage of acetylated derivatives, among other parameters. However, the difference is explicable in that the samples used by those workers all came from a single wine-growing region. Discriminant analysis for all the samples combined, grouped according to winemaking method, grape variety, and wine-growing region, produced classification functions for differentiating the wines according to these same three characteristics. Table 3 summarizes the variables selected and the classification functions for samples grouped according to wine-making method. Of the 22 variables considered, discriminant analysis selected 8, with colorant density selected first, i.e., presenting the greatest discriminating power (F(3,40) = 169.52). This is consistent with the fact that colorant density was the variable that exhibited the highest correlation with the winemaking factor, factor III. One hundred percent of the claret and double pomace wine samples were correctly classified using this variable alone. On the other hand, only 58.6% of the red wines

62

GONZALEZ-SAN

JOSE, SANTA-MARIA,

AND DIEZ

c2 4

2

0

-2

-4

-8 -8

-6

-4

-2

0

2

4

Cl

FIG. 4. Distribution of wines in the two-coordinate system defined by two canonical variables with the highest discriminating power. Wines grouped according to wine-making method. C 1, canonical variable 1; C2, canonical variable 2; 2, claret: 3, carbonic maceration; 4, traditional red wine making; 5, “doble pasta” (double pomance) wine making

made by conventional methods and 42.9% of red wines made by carbonic maceration were classified correctly. Including various anthocyanins in the discriminant function improved these classification rates considerably, reaching 100% correct classification in all cases. The anthocyanins with the greatest discriminating power were the acetylated derivatives of petunidin-3-glucoside (F(3,39) = 6.84) and malvidin-3glucoside (F(3,38) = 6.17). The concentration of these compounds was higher in wines made by traditional methods or by carbonic maceration than in double pomace wines and clarets. In contrast, the double pomace wines presented colorant density values three times higher than those for the rest of the wines. The largest differences between conventionally made red wines and wines made by carbonic maceration involved peonidin-3-glucoside, and its p-coumaric acid derivative concentration, which was higher in the conventionally made red wines. When the classification functions in Table 3 were applied to 44 wines that came from the same regions of origin and had the same characteristics as those used to estimate the discriminant function, 100% correct classification for all the wines was also achieved, and the smallest distance between groups was between conventionally made red wines and wines made by carbonic maceration. Figure 4 shows this quite plainly by representing the samples in the plane formed by the two canonical coordinates with the greatest discriminating power, which had correlation coefficients of 0.985 and 0.795, respectively, with the variables selected and accounted for 97.50% of the variance. Applying discriminant analysis to the wines grouped according to grape variety produced classification functions representing linear combinations of the variables set out in Table 4. All the malvidin and p-coumaryl derivatives contributed to these functions.

ANTHOCYANINS

AS PARAMETERS

FOR

TABLE VARIABLES

SELECTED

AND THE CLASSIFICATION

ALL THE WINES, VWlableS (Xi)

cr-3e1u m-3alu Mv-3&i NV Mv3(6acet)glu '3~3(6~cu'n)glu Mv3(6caf)gl" PtJ(Bpm#n)gl” m3(6pcoun)gl" Mv3(6p~o~n)gl" Total Acy

Note. coumaryl;

Cy, cyanidin; Glu,

glucoside;

ACCORDING

FOR THE DISCRIMINANT TO GRAPE

-50.84 9.21 1.21 -46.60 -10.13 14.02 7.73 26.53 -54.57 6.06 0.16 -133.33

Functton (ai j) CSRMCHA lKXN.STRELL

ANALYSIS

FOR

VARIETY

Classification TEMPRANI LLO

%J

63

WINES

4

FUNCTIONS

GROUPED

DIFFERENTIATING

-21.37 2.00 1.01 18.60 -5.50 9.23 1.93 2.10 -7.35 -0.81 0.02 -49.51

-12.66 3.26 -0.08 -25.67 -0.59 5.55 1.93 5.06 -7.47 -1.16 0.15 -18.41

Pt, petunidin; Pn, peonidin; Mv, malvidin; Acy, anthocyanins; +j, constant.

BSAL

-86.53 16.82 0.32 -80.53 -3.40 -7.61 3.34 12.01 -40.62 -1.65 0.45 -99.81

Acet,

acetyl;

CSEERET SALWIW34

21.14 -12.19 -1.36 -32.60 27.36 -63.41 27.95 16.46 21.98 -1.82 0.23 -233.15

Caf,

c&yl;

Pconm,

p-

The variable with the greatest discriminating power selected in the first step of the discriminant function estimation procedure, was malvidin-3-(6-acetyl)-glucoside (F(4,39) = 50.36), which agreed with the finding of Et&ant et al. (1988b) for French varietal wines. This variable was one of the variables most highly correlated with factor II, which, as has already been pointed out, was primarily related to grape variety. Classifications based on this variable alone were correct for fewer than 50% of the wines, except for the wines made from Cabernet-Sauvignon and Monastrell grapes, for which correct classification rates of 100% and 9 1%, respectively, were achieved. The percentage of correct classification rose to 100% for all wines when the functions listed in Table 4 were applied. Tempranillo grapes contained the highest concentrations of malvidin derivatives, while Monastrell grapes usually had the lowest concentration of anthocyanins, except for cyanidin-3-glucoside. In all cases Cabernet-Sauvignon grapes presented the greatest differences from the other varieties (F( 11,29) > 100). The least significant difference was between Gamacha and Monastrell grapes (F( 11,29) = 23.42). Figure 5 represents the samples in the plane formed by the two canonical coordinates with the highest discriminating power and correlations of0.994 and 0.982, respectively, with the variables selected, accounting for 84.13% of the variance. Of the 22 variables for grouping according to wine-growing region analyzed, discriminant analysis selected 10, and the resulting classification functions appear in Table 5. The variable peonidin-3-(6-p-coumaryl)-3-glucoside had the highest discriminating power (F(4,39) = 34.99). This was consistent with the fact that this variable was one of the variables most highly correlated with factor I, previously defined as the wine-growing region factor. The percentage of correct classifications obtained using this variable alone was 0% for Rioja and Almendralejo wines and was higher than 60% only for the La Mancha wines (86%). However, when the classification functions appearing in Table 5 were used, the percentage of correct classifications rose to 100%.

64

GONZALEZ-SAN

JOSE, SANTA-MARIA,

S sss s

AND DIEZ

G

s G

-20

-15

-10

0

-5

GGG

5

10

1%

5. Distribution of wines in the two-coordinate system define by two canonical variables with the highest discriminating power. Wines group according to varieties. C 1, canonical variable 1; C2, canonical variable 2; M, Monastrell; S, Cabernet-Sauvignon; T, Tempranillo; G, Gamacha; B, Bobal. FIG.

The smallest distance was between the La Mancha (L) and Tierra de Barros (A) wines (F( 10,30) = 13.34). This is readily discernible in Fig. 6, which presents the canonical representation of the wines not used to estimate the classification functions, in which the two canonical coordinates with the highest discriminating power, which had correlations of 0.984 and 0.974, respectively, with the variables selected, accounted for 82.47% of the variance.

TABLE 5 VARIABLES

SELECTED

AND THE CLASSIFICATION

ALLTHE

WINES,

GROUPED

FUNCTIONS

ACCORDING

Classification

Variables

(X,1

m-*tu

Pt CyJ(6acet)gl” nv Mv3(6acet)gl” LIpS(Bacet)glu m3(6pcovn)*l” WJ

ANALYSIS

FOR

REGION

Function calJ)

RICLJA

Tonal ity Dp-3glU cy-%lU

FOR THE DISCRIMINANT

TO WINE-GROWING

329.60 13.89 -0.70 -10.53 -34.89 -13.10 95.43 6.51 -17.67 -1.31 -176.34

LA

p(ANcHA

366.16 1.80 35.95 -6.28 -3.62 -7.90 70.97 5.66 -2.27 13.88 -166.61

TIERRA CEBNROS

439.49 5.60 39.06 -13.31 -25.42 -22.74 93.39 10.54 -3.30 4.72 -233.70

UrlEL RECLEFH

333.16 -0.26 0.87 7.01 22.12 -1.47 29.53 0.25 -17.61 -6.27 -140.80

MILLA

479.12 -2.96 71.54 -6.23 -3.03 13.50 43.46 2.02 -0.34 5.72 -223.04

Dp, delphinidin; Cy, cyanidin; Pt, petunidin; Pn, peonidin; Mv, malvidin; Acet, acetyl; Caf, caffeyl; Pcoum, p-coumaryl; Glu, ghrcoside; ao,, constant.

ANTHOCYANINS

AS PARAMETERS

FOR

DIFFERENTIATING

65

WINES

c2 J J

J

J

J

J

JJ

A

J

A* .A LL

A

A L

1

L L

U U UUU U

u U

-5

FIG. 6. Distribution highest discriminating C2, canonical variable

R

u

R

I?

u (I

0

5

10

Cl

of wines in the two-coordinate system defined by two canonical variables with the power. Wines grouped according to wine-growing region. C 1, canonical variable 1; 2; J, Jumilla; R, Rioja; U, Utiel-Requena; L, La Mancha; A, Tierra de Barros.

CONCLUSIONS

Factor analysis demonstrated the difficulty in achieving clear-cut separations between the effects of grape variety and wine-growing region on the anthocyanin composition of wines. A set of samples encompassing wines from a number of winegrowing regions made from different grape varieties according to the traditional methods for each region is therefore required. However, discriminant analysis enabled classification functions for differentiating between wines according to grape variety, wine-growing region, and wine-making method to be developed. The acylated derivatives, e.g., the p-coumaric acid esters, were mainly related to wine-growing region, whereas the acetic acid esters were primarily related to grape variety, and both were affected by the wine-making method employed. Anthocyanin hydrolysis during wine making was basically dependent upon grape variety, and all the anthocyanin pigments were affected equally by wine-making method. REFERENCES BAKKER, J., AND TIMBERLAKE, C. F. (1985). The distribution of anthocyanins in grape Port wine cultivards as determined by HPLC. J. Sci. Food Agvic. 36, 13 1 S- 1324. CABEZUDO, for solving

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GONZALEZ-SAN

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