J Sci Food Agric 1996,70, 173-180

Varietal Classification of Young Red Wines in Terms of Chemical and ColourParameters Luis Almela,** Sebastian Javalo y; Jose A Fernandez-Lopezb and Jose M L6pez-Roca"

Departamento de Quimica Agricola, Geologia y Edafologia, Facultad de Quimica; Departamento de Ingenieria Quimica, Escuela Politkcnica Superior ; ' Departamento de Tecnologia de Alimentos, Facultad de Veterinaria, Universidad de Murcia, E-30071 Murcia, Spain

(Received 14 February 1995; revised version received 19 July 1995; accepted 16 August 1995)

Abstract: Six varietal red wines produced in Murcia (Spain) were analysed for selected enological and colour parameters (31 variables in total). As regards acid content there were no important differences among the vinifications studied. The initial content of glucose and fructose was quite high in all the wines, which resulted in very high ethanol content (between 14.1% in Cinsault Noir wine and 16.4% in Tempranillo wine). The colour of the wines was determined by means of the Y x y coordinates and CIELAB parameters, but not one of them could significantly differentiate among vinifications. Principal component analysis and discriminant analysis were used to identify and to explain differences among samples. Discriminant analysis allowed a clear separation of the 80.7% of the cases analysed by a selection of only three variables (L*, a*, C*). With a selection of 10 variables including x coordinate, colour intensity 1 and 2, pH, ionised anthocyanins, total phenolics, percentage of yellow pigments, total anthocyanins, hue and L* was possible to characterise the 95% of the cases analysed, but 100% in Monastrell-1, Tempranillo Graciano and Cinsault Noir wines.

Key words : Spanish red wines, varietal wines, colour parameters, enological parameters, principal component analysis, discriminant analysis.

INTRODUCTION Factor analysis is a formal mathematical model that permits definition of latent variables in explaining the

The sensory characteristics of a wine are influenced by a linear relationships between experimental parameters. broad spectrum of factors such as the type of grape This type of mathematical treatment has been applied (varietal factor), soil, cultural and enological conditions previously to wine classification by several authors (production area factor) and climatic conditions (Frank and Kowalski 1984; Mbdina and Van Zeller (vintage factor). Wine characterisation and authentica- 1984; Moret et a1 1984; Forina et a1 1986; Tapias et a1 tion is usually performed by means of trained tasters 1987; Etikvant et al 1989; Garcia-Jares et al 1993). and chemical analysis. Chemical analysis is objective However, for multivariate statistical analysis to be and reproducible, but such a complex substance as wine applied to the classification of wines produced in a spe- is difficult to characterise with a limited number of cific region or with different types of grape, it is neces- chemical determinations. Moreover, of the great sary to carry out a study first to delimit which are the number of chemical analyses described for musts and most representative variables. wines it is necessary to select the most meaningful for The main aim of this investigation was to obtain a their characterisation. To help solve this last problem, multivariate mathematical model which allowed us to multivariate statistical analysis has proved its usefulness select a certain number of chemical variables to provide in determining the most representative factors, or those appropriate discrimination between young red varietal factors which best differentiate varietal wine samples. wines produced in the south-east of Spain, with special

emphasis on those parameters related to the objective * To whom correspondence should be addressed. determination of colour.

J Sci Food Agric 0022-5142/96/$09.00 0 1996 SCI. Printed in Great Britain 173

174 L Almela et a1

MATERIALS AND METHODS

Six varietal wines produced in the traditional fashion in the Experimental Winery located at Jumilla (Murcia, Spain) were chosen for this study. A wine made from Monastrell grape cultivated on unirrigated land was taken as reference (Monastrell-1), since this is the prin- cipal grape variety of this area, where viticulture is pre- dominantly a crop for dry farming. The remaining varietal wines were made from Tempranillo, Cabernet Sauvignon, Graciano and Cinsault Noir grape varieties cultivated in a trellis using a system of drip-irrigation. A sixth vinification was carried out with Monastrell grape (Monastrell-2) grown in the same hydric and cultivation conditions as the others varieties, to check the influence of these variables.

Fermentations were conducted in 400 litres stainless- steel tanks where the temperature was controlled in 22 k 2°C. The musts were sulfited with 10 g of pot- assium metabisulphite (k,O,S,) per hectolitre of must. Maceration was on skins in the traditional fashion and, when alcoholic fermentation had ended, three rackings

were made at 20, 35 and 90 days after the final pressing of the grapes.

Samples were taken daily during fermentation and then every 3 days until the end of the first month. During the following months sampling was fortnightly. All the samples were frozen immediately and kept at -24 k 1°C until analysed.

The analysed variables, units, code names and analysis methodology are itemised in Table 1.

Data processing was performed with the Systat and Statgraphic statistical analysis softwares.

RESULTS AND DISCUSSION

Changes in chemical and colour parameters

Table 2 shows the changes undergone by the main parameters considered during the sampling period. Since it is impossible to detail all the analytical determi- nations performed, we present the results corresponding

TABLE 1 Variables analysed, codes, units and methods used

Variable Code Units Determination

Tartaric acid Succinic acid Malic acid Lactic acid Titratable acidity Volatile acidity PH Glucose Fructose Total Sugars Glucose/Fructose Ethanol Glycerol Colour intensity 1 Colour intensity 2 Chemical age 1 Chemical age 2 Total anthocyan. Jonised anthocyan. Total phenolics so2 x coordinate y coordinate %D red colour % yellow colour % blue colour hue L* value a* value b* value C* value

T S M L TA VA PH GL FR TS GL/FR ET GY I1 I2 Q1 4 2 AN IA TP so* X

Y % R %Y Yo B YIR L* a* b* C*

g litre- ' g litre-' g litre- ' g litre-' g litre-' (tartaric acid) g litre- ' (acetic acid) units g litre- ' g litre-' g litre-' units Yo (v/v) g litre-' units (1 cm path light) units (1 cm path light) units units mg litre-' mg litre- ' abs. units mg litre- ' units units units units units units units units units units

Almela et al (1993) Callul et al (1992) Callul et al(l992) Callul et al (1992) Amerine and Ough (1976) Amerine and Ough (1976)

Amerine and Ough (1976) Amerine and Ough (1976)

-

-

- Amerine and Ough (1976) Amerine and Ough (1976) Glories (1984) Glories (1984) Somers and Evans (1977) Somers and Evans (1977) Somers and Evans (1977) Somers and Evans (1977) Somers and Evans (1977) Somers and Evans (1977) OIV (1978) OIV (1978) Glories (1984) Glories (1984) Glories (1984) OIV (1978) CIE (1986) CIE (1986) CIE (1986) CIE (1986)

TABL

E 2

Mai

n va

lues

cal

cula

ted

for e

ach

win

e in

the

thre

e st

ages

sele

cted

" ~ ~

-~

Win

e Ph

ase

T S

M

L T

A

VA

G

L

FR

E

T G

Y

12

Ql

Q2

AN

IA

T

P

L* a*

b*

Mon

astr

ell- 1

Mon

astr

ell-2

Tem

pran

illo

Gra

cian

o

Cab

erne

t Sau

vign

on

Cin

saul

t Noi

r

I I1

I11 I I1

I11 I I1

111 I I1

111 I I1

I11 I I1

11

1

6.63

2.

82

0.20

6.49

3-

62

0.56

6.08

3-

16

0.5

6-15

2.

83

0.32

6.68

3.54

0.

82

4.38

3.30

0.

95

0.05

2.

00

0-57

1-

51

0-55

0.

26

0.06

2.

00

0.25

0-

72

0.92

0.

80

0.09

2.

2 0.7

5 2.7

8 0.

63

2.00

0.02

2.

81

0.83

0.

95

0.85

0.

55

0.10

3.

06

0.73

0-

98

0.59

0.

61

0-03

4.

50

0.55

0.

62

0.35

0.

55

0.85

1.

50

2.19

1.20

2.7

7 1.

58

0.90

1.

74

2-20

0.83

2.71

2-

41

1.22

1.

64

2.45

0.61

3.

31

2.29

3.80

0-

05

6.63

0.40

5.

62

0.80

5.70

0.

00

6.70

0.

15

5.66

0.

60

6.03

0.

00

6.63

0.

20

5-55

0.

70

5.70

0.

00

7.72

0.

50

6.00

0.

90

5.21

0.00

6.80

0.2

5 6.

00

0.75

6.63

0.

00

6-15

0.

10

5.45

0.

60

131-

8 11

.5

1.3

117.

6 0-

2 0-

5

170.

3 14

.5

0.2

179.

5 18

-4

1 .o

227.

0 12

-5

1.2

171.

8 0.

4 0.

2

128.

8 98

.0

3-1

121-

7 5.

5 0.

5

157-

8 45

.0

0-3

166.

8 50

.7

1 -2

215.

7 46

.3 1 *4

166-

2 4.

1 0.

2

0.5

9.9

16-0

1 -4

15

.0

15.0

0.5

13.7

16

-4

0.7

11.1

15

.3

0.5

13-3

15

.3

1.2

14.4

14

.1

1.22

11

.50

12.7

0

0.50

11

-20

9.20

0.50

12

.30

12.5

0

0.50

3.

92

7.70

0.50

10

.80

7.30

1.10

8.

40

8.10

9.00

12

.50

16.0

0

10.4

0 11

-70

10.8

0

10.3

0 13

.20

10.8

0

15.7

0 16

.40

15.2

0

4.40

15

.10

13.1

0

9.75

3.55

5.

29

0.08

0-

02

0-31

0.

11

0.56

0.

22

0.34

0.

11

0.35

0.

10

0-60

0.

23

0.35

0-

09

0.54

0-

13

0.76

0.

33

0.20

0.

06

0.42

0.

11

0.60

0.

24

0.25

0.

06

0.48

0.1

3 0.

71

0.28

0.60

0.

22

0.55

0.

10

0.68

0.30

225.

0 35

1.0

256-

0

259,

8 43

3.0

171.

0

293.

0 39

9.0

100.

0

400.

0 55

0.0

233.

0

326.

0 46

3.0

176.

0

83-0

16

0.0

58.1

105.

0 94

.8

70.4

70.7

10

1.0

43.0

60-0

56

-0

25.0

114.

0 99

.0

63.0

8.5

82.0

44

-0

16.5

14

.2

16.4

28.0

72

.8 43

.3 -3

.5

45.8

64

.9

42.5

0.

5 73

-2

58.3

40

.7

5.5

35.0

71

.5

29.5

5.

2 51

.1

75.0

28

.4

0.6

41.1

69

.8

28.8

3.

9

42.2

71

.9

23.4

4.

2 63

.3

65.2

27

.1

4.9

50.1

72

.1

23.0

11

-5

41.6

58

.0

51.4

-1

.9

59.0

57

.7

46.9

5.

4 48

.7 61

.0

39.3

8.

7

32.1

88

.5

3.6

5.1

62.8

61.1

39

.6

4.8

44-1

65

.3

34.9

5.

3

22.9

80

.0

-19.

1 1.3

31

.9

89.6

8-

9 2.8

30

.2 85

.0

12.8

4.

3

Mea

n fr

om tr

iplic

ate

sam

ples

. Var

iabl

e cod

es a

nd u

nits

are

as i

n T

able

1.

TA

BL

E 3

M

ultip

le ra

nge

anal

ysis

of t

he a

cid,

suga

r and

alc

ohol

ic p

aram

eter

s at

the

end

of t

he s

ampl

ing

perio

d in

the

diff

eren

t win

es a

naly

sed"

Tart

aric

Su

ccin

ic

Mal

ic

Lact

ic

Titr

atab

le

Vol

atile

G

luco

se

Fruc

tose

Et

hano

l G

lyce

rol

acid

ac

id

acid

ac

id

acid

ity

acid

ity

Mon

astre

ll- 1

0.20

f 0.

01 a

0.

55 f 0.

02 b

0.

26 f 0.

02 a

2.

19 f 0.

24 b

5.

62 f 0.

15 a

0.

80 f 0.

02 c

1.

3 f 0.

60 b

3.

1 1-

40 c

16.0

0.

2 c

12.7

f 0.

3 d

Mon

astre

ll-2

0.56

f 0.

50 a

bc

0.92

f 0.

01 e

0.

80 f 0-

15 c

1.58

f 0-

22 a

5.66

f 0.

15 a

0.

60 f 0.

01 a

0.

5 f 0.

05 a

0.

5 f 0.

10 a

b 15

.0 f 0.

4 b

9.2

_+ 0

.1 c

Te

mpr

anill

o 0-

50 f 0-

03 a

b 0-

63 f 0.

01 c

2-

00 k 0.

10 d

2.

20 f 0.

05 b

5.

55 k 0-

05 a

0.70

& 0

.01

b 0.

2 &

0.0

1 a

0.3 f 0-

12 a

b 6.

4 f 0.

1 d

12.5

f 0.

2 d

Gra

cian

o 0.

32 f 0.

12 a

0.85 f 0.

05 d

0.

55 f 0.

02 b

2.

41 &

0.0

5 b

6-00

f 0.

25 b

0.

90 k 0.

07 d

1.

0 k 0.

20 b

1-

2 k 0

.11

ab

15.3

k 0.

1 b

7.7 f 0.

6 ab

C

aber

net S

auvi

gnon

0-

82 _+

0.0

7 bc

0-

59 f 0.

06 b

c 0.

61 _+

0.0

6 b

2-45

f 0.

10 b

6.

00 f 0.

25 b

0.

75 f 0.

04 b

c 1-

2 f 0.

10 b

1.

4 f 0-

30 b

15

.3 f 0.

2 b

7-3 f 0.

3 a

Cin

saul

t Noi

r 0-

95 f 0.

19 c

0.

35 f 0.

01 a

0.

55 f 0.

01 b

2-

29 k 0.

05 b

5.

45 f 0.

15 a

0.

60 f 0-

05 a

0.

2 f 0-

10 a

0.2

& 0

.20

a 14

.1 f 0.

1 a

8.1 f 0-

2 a

~

Mea

n va

lues

( f SD

) with

in a

col

umn

deno

ted

by a

diff

eren

t fol

low

ing l

ette

r are

sig

nific

antly

diff

eren

t at P

< 0

-05.

TA

BL

E 4

M

ultip

le ra

nge

anal

ysis

of t

he c

olou

r-re

late

d pa

ram

eter

s at

the

end

of t

he s

ampl

ing

perio

d in

the

diff

eren

t win

es c

onsi

dere

d"

Tota

l Io

nise

d To

tal

Colo

ur

Chem

ical

Ch

emic

al

L* a*

b*

an

thoc

yani

ns

anth

ocya

nins

ph

enol

ics

inte

nsity

2

age

I ag

e I1

~~

~

Mon

astre

ll-1

256-

0 f 48

.0 d

70

-4 f 1

.3 e

73

.2 &

2.4

e

16.0

f 1

.1 d

0.

56 &

0.0

1 a

0.22

f 0.

01 a

58

.3 f 0.

1 a

40.7

f 1

-3 e

5.5 k 1

.1 b

M

onas

trell-

2 17

1.0

rf: 1

6.0

c 43

.0 _+

4.5

c

41.1

_+ 1

.0 b

10

.8 f 1

.4 b

0.

60 f 0-

03 a

0-

23 _+

0-0

3 a

69-8

k 1.

8 bc

28

4 f 1

.2 c

3-

9 f 0.

4 a

Gra

cian

o 23

3-0 f 6.

0 d

63.0

f 4.

3 d

48.7

k 1

.5 d

15

.2 f 1

.9 c

d 0.

60 &

0.0

3 a

0.24

f 0-

01 a

b 61

.0 f 3.

0 a

39.3

f 1

.3 e

8.

7 _+

0.5

c

Cab

erne

t Sau

vign

on

176.

0 f 10

-2 c

44-0

_+ 2

.5 c

44

.1 f 0.

1 c

13.1

f 1

.7 b

c 0.

71 f 0-

03 b

c 0-

28 f 0.

02 b

c 65

.3 _

+ 2.

1 b

34-4

f 2.

0 d

5-3 f 0.

3 b

Cin

saul

t Noi

r 58

-1 f 5.

6 a

16.4

f 1

-1 a

30.2

_+

0-4

a 5.

3 f 0.

4 a

0.68

k 0.

06 b

0.

30 f 0.

04 c

d 85

.0 f 5.

1 d

12.9

f 0.

2 a

4.3 f 0.

6 ab

Tem

pran

illo

100.

0 f 1

5.1

b 25

.0 k 3-

9 b

50.1

k 1.

8 d

10.8

f 2.

0 b

0.76

f 0.

05 c

0.

33 f 0.

01 d

72

.1 f 1

.7 c

23

.0 &

3.1

b

11.5

f 0.

6 d

Mea

n va

lues

( f SD

) with

in a

col

umn

deno

ted

by a

diff

eren

t fol

low

ing

lette

r are

sig

nific

antly

diff

eren

t at P

< 0.

05.

Classijication of varietal red wines 177

TABLE 5 Percent variance accounting by factor and accumulated variance of the correlation matrix

% variance YO variance Highly loaded variables accounting by factor accumulated

PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8

25.56 23.96

7 6 9 9.22 8.34 5.03 3,74 3.30

25.56 49.52 57.21 66.43 74.77 79.81 83.54 86.84

11, 12, L*, a*, IA, x , TP, AN, %Y, Y / R ET, GL/FR, FR, GL, GY, T, S, VA

4 2 , AN, elapsed time b*, wine TA

Y

%B, %R

so2

to three significant stages during wine production: after pressing (phase I), end of fermentation (phase 11) and end of sampling period (phase 111). The entire data set is available from the authors upon request.

As regards acid content at the end of the sampling period (Table 3), there were no significant differences between the different vinifications studied. Titratable acidity (TA) was not very high (5-6 g litre-' expressed as tartaric acid) these values being in agreement with the normal content found in Spanish wines (Mareca 1983; Tapias et al 1986; Polo et al 1987). However, these results are not comparable with those concerning the acidity of wines elaborated with the same grape varieties in colder climates (Vogt et a1 1986; Clark et al 1988), which confirms the close dependence between wine acidity and the production area and vintage factors. Wines made from Graciano and Cinsault Noir showed the highest acidity, whereas irrigation did not significantly influence the titratable acidity in the Monastrell wines. The final content in tartaric acid (T) was low as a consequence of the high alcoholic content reached, since the stability of tartrates depends on the pH, temperature and alcoholic degree (Clark et al 1988; Almela et a1 1993). Only Cinsault Noir wine reached a level near 1 g litre-', the rest of the wines showed a tartaric acid content lower than 0.8 g litre-*, this being the minimum value in Spanish wines (De Gorostiza et a1 1982).

As regards to malic-lactic transformation during the fermentation stage, there was a degradation of malic acid, which was low in Monastrell-1 and Tempranillo wines (ca 30%); marked in Monastrell-2, Graciano and Cabernet (64-67%) and extreme in Cinsault (86%). This decrease may be attributable to the activity of yeasts that would turn it into ethanol and CO, (Castellari et a1 1992) or to the activity of endogenous enzymes (Davis et a1 1985). In the post-fermentation stage, Monastrell-2, Graciano and Cinsault did not increase the lactic acid content, while in Monastrell-1 and Tempranillo the level of malic-lactic transformation (56%) was lower than the stequiometric factor value (67%). Nevertheless, the parallel increase in volatile acidity in values higher

than 0.2 g litre-' reveals the simultaneous presence of other bacterial activities besides lactic bacteria. The vin- ification with Cabernet Sauvignon showed an increase in lactic acid higher than the decrease of malic acid which reveals the presence of heterolactic organisms that produce lactic and acetic acid from organic acids and hexoses. The final content in succinic acid ( S ) was significantly different between wines (with the exception of Cabernet) and was in accordance with those reported in the bibliography for Spanish wines (Marce et al 1990).

As consequence of the high level of sun in this area, the initial content of glucose (GL) and fructose (FR) was quite high in all the vinifications, which resulted in very high ethanol (ET) and glycerol (GY) contents at the end of fermentation. The concentration of glycerol and its percentage with respect to the ethanol content were higher than in wines of a lower alcoholic degree (Caputi et a1 1992; Sanchez-Muiioz et all992).

Total anthocyanin content (AN) increased during the fermentation stage as a consequence of the extraction by the ethanol. At the end of this stage, the vinification with Graciano grape showed the highest content in total anthocyanins but the degree of ionisation was lower than in Monastrell vinifications. Cinsault Noir wine showed the lowest anthocyanin and total phenolic contents. In the postfermentation stage total and ionised anthocyanins (IA) decreased as consequence of the precipitation of the colouring matter in a propor- tion of 75% in Tempranillo wine, 27% in Monastrell-1 wine and about 60% in the rest of wines; but showed no correlation with colour intensity (12). This would imply the contribution of new components in wine coloration. Therefore, monomeric or oligomeric antho- cyanin forms (absorbance 520 nm) are polymerising with phenolic compounds, with a hypsochromic change in the absorbance maximum towards 420 nm. This transformation is revealed by the increase in chemical age indexes (Ql, 4 2 ) from phase I to phase 111.

To determine the colour objectively, we initially used the Y x y colour space, whose coordinates (not shown in the table) did not differentiate between the vinifications

178

I 24%

L Almela et a1

PC t

x % X

xx PC 1

X X X

. x r.' + +

* Mnnnstrelll -k Monastrellt x Temprnnillu x Grnciano Cnbernet Snuv. + Cinsnult N o h

Fig 1. Distribution of (A) the chemical and colour parameters, and (B) the wine samples in the plane defined by the first two principal components.

with the exception of Cinsault Noir. For this reason the three dimensional colour space L*a*b* (CIE 1986) was used. With respect to the multiple range analysis of the colour-related parameters (Table 4), taking as reference the traditional wine elaborated with Monastrell grape grown in dry farming conditions, the greatest colour dif- ferences in the L* value (lightness) occurred with the Cinsault Noir vinification, which also showed the most pronounced differences in the other chromatic param- eters analysed. The pairs of values, L* and a* differen- tiated between the colour of the varietal wines studied, except between Monastrell-1 and Graciano. The param- eter b* did not show significant differences between Monastrell-1, Cinsault Noir and Cabernet Sauvignon samples. These observations agree with data reported previously (Almela et a1 1995).

Multivariate statistical analysis

Principal component analysis (PCA) For this study all results were obtained from the rotated matrix. Table 5 presents the percentage of variance accounting by factor, and the accumulated percentage, considering only those variables whose eigenvalue was higher than 1, and the parameters involved in the rotated matrix which had a coefficient higher than 0.600.

The projection of the sample configuration on the first two principal components of the PCA is given in Fig l(A). Note the great loading of the variables related with the colour on the first factor (PC I), and the loading of the variables related with acids and fermenta- tion process on factor 2 (PC 2). The projection of the

Classijication of varietal red wines 179

sample configuration in the plane defined by PC 3 and PC 4, and respect other components of lower variance did not show characteristic grouping.

Figure 1(B) shows the distribution of the varietal wine samples in the plane defined by the first two principal components (PC 1 and PC 2). If we compare the dis- tribution of the variables in the same plane (Fig l), it is clear that in the case of the Cinsault Noir wine, it is the L*, Y / R and %Y are the parameters which make the highest contribution to its location. In the Graciano wine (the wine with the highest colour) the parameters making the highest contribution were colour intensity, a*, total anthocyanins and phenolic compounds. Tem- pranillo wine is more influenced by parameters related with the fermentation stage (ethanol, glycerol, succinic acid and lactic acid). Similar results were obtained for the rest of the wines. This suggests that Cinsault Noir wine differs from the other wines in L*, Y J R and % Y ; Graciano in 11,12, AN, PC and a*, and Tempranillo in the ethanol and glycerol contents. The Carbernet Sau- vignon and Monastrell wines would not be so clearly differentiated as the Cinsault, Tempranillo and Gra- ciano wines, and therefore it is necessary to tackle them by means of discriminant analysis.

Discriminant analysis Factor analysis allowed us to detect a logical grouping of variables and an incipient characterisation of the varietal wines studied. In an attempt to improve these results a discriminant analysis was carried out to iden- tify the discriminant function (linear combination of variables) that best predicted and classified our data.

Several criteria were studied to select a limited number of variables which allowed an accurate classi- fication of the wines. The first criterion considered those variables related to the chromatic characteristics %R, %Y, %B, Y / R , x and y (chromatic criterion). Others criteria were L*, a*, C* (CIELAB criterion); T, M, L, S , pH, VA and TA (acidity criterion); AN, IA, TP, 42, YJR, %Y, %R and %B (phenolic criterion) and the last one considered those 10 variables which allowed the best classification, these variables were: 11, 12, TP, AN, IA, %Y, Y/R, x, L* and pH (10-variables criterion). The chromatic criterion achieved a low classification with the Graciano and Cabernet Sauvignon wines. The CIELAB criterion, which only uses three variables of very easy determination, achieved to classify the 80.7% of all the samples considered. Acidity and phenolic cri- teria accurately classified the 79.8% and 82.6% of the samples, respectively but they use a higher number of variables. The 10-variables criteria correctly classified the 95% of the samples. If we considered the ratio between the percentage of correctly classified samples and the number of variables of each criterion, it can be observed that the CIELAB criterion could easily serve as differential criterion in the wines analysed. A similar conclusion was made by Garcia-Jares and Medina

(1993) in comparing genuine rose wines with blended white and red wines.

CONCLUSIONS

In our study the main parameters of higher enological interest showed no statistical differences among Spanish varietal red wines. The differences found with the same varietal wines reported in the bibliography would show the influence that the production area and vintage factors have on their chemical characteristics.

Discrimination between six different varietal wines was achieved using three standard analytical param- eters (L*, a* and C*) with a level of classification that exceeded 80%. The variables that allowed the highest discrimination of cases (95%) also included colour parameters (x coordinate, colour intensity, anthocyanin content, colour tint, % of yellow pigments, L* and pH).

ACKNOWLEDGEMENTS

The authors are grateful to the staff of the Experimental Winery of Jumilla for providing wine samples.

REFERENCES

Almela L, Lazaro I, Lopez-Roca J M, Fernandez-Lopez J A 1993 Tartaric acid in frozen musts and wines. Optimization of Rebelein's method and validation by HPLC. Food Chem

Almela L, Javaloy S, Fernandez-Lopez J A, Lopez-Roca J M 1995 Comparison between the tristimulus measurements Y x y and L*a*b* to evaluate the colour of young red wines. Food Chem 53 321-327.

Amerine M A, Ough C S 1976 Analisis de Vinos y Mostos. Acribia, Zaragoza, Spain.

Callul M, Marc6 R M, Borrull F 1992 Determination of car- boxylic acids, sugars, glycerol and ethanol in wine and grape must by ion-exchange high-performance liquid chro- matography with refractive index detection. J Chromatogr

Caputi A Jr, Christensen E, Biedenweg N, Miller S 1992 Liquid chromatographic method for determination of glyc- erol in wine and grape juice: Collaborative study. J AOAC Int 75 379-383.

Castellari L, Simoni M, Grazia L, Tini V, Zambonelli C 1992 Fermentazione malolattica e maloalcolica. Vini d'ltalia 34

CIE 1986 Colorimetry (2nd edn, Publication CIE no 15.2). Central Bureau of the Commission Internationale de I'Eclairage, Vienna, Austria.

Clark J P, Fugelsang K C, Gump B H 1988 Factors affecting induced calcium tartrate precipitation from wines. Am J End Vitic 39 155-161.

Davis C R, Wibowo D, Eschenbruch R, Lee T H, Fleet G H 1985 Practical implications of malolactic fermentation: A review. Am J End Vitic 36 290-301.

De Gorostiza E F, Cabezudo M D, Martin P, Suarez M A 1982 Modeles lineaires pour differencier des vins, premiere partie: vins de table rouges et blancs. OIV FV 744 1048.

47 357-361.

590 215-222.

19-24.

180 L Almela et a1

Etievant P, Schlich P, Cantagrel R, Bertrand A, Bouvier J C 1989 Varietal and geographic classification of French red wines in terms of major acids. J Sci Food Agric 46 421-438.

Forina M, Armanino C, Castino M, Ubigli M 1986 Multi- variate data analysis as a discriminating method of the origin of wines. Vitis 25 189-201.

Frank I E, Kowalski B R 1984 Prediction of wine quality and geographic origin from chemical measurements by partial least-squares regression modeling. Anal Chim Acta 162 241- 251.

Garcia-Jares C, Medina B 1993 Research on white and red wine blending in the production of rose wines by means of the partial least squares method. J Sci Food Agric 63 349- 354.

Garcia-Jares C, Rozes N, Medina B 1993 Caracterisation et differenciation des vins roses d’Appelation d’Origine et de table, franqais et espagnols. J Int Sci Vigne Vin 27 35-51.

Glories Y 1984 La couleur des vins rouges. 2 partie: Mesure, origine et interpretation. Conn Vigne Vin 4 253-271.

Marce R M, Callul M, Borrull F, Rius F J 1990 Determi- nation of major carboxylics acids in wine by an optimized HPLC method with linear gradient elution. Am J Enol Vitic

Mareca I 1983 Origen, Composicibn y Evolucion del Vino. Alhambra, Madrid, Spain, pp 3-1 1.

Mkdina B, Van Zeller A L 1984 Differentiation of wines from three French regions. Conn Vigne Vin 18 225-235.

Moret Y, Di Leo F, Giromini V, Scarponi G 1984 Multiple discriminant analysis in the analytical differentiation of

41 289-294.

Venetian white wines. 4: Application to several vintage years and comparison with the nearest-neighbour classi- fication. J Agric Food Chem 32 329-333.

OIV 1978 Recueil des Mithodes Internationales d’dnalyse des Vim. Office International de la Vigne et du Vin, Paris, France.

Polo M C, Caceres I, Gonzalez-Raurich M, Martin-Alvarez P, Cabezudo M D 1987 Composicion de 10s mostos de las principales variedades de uva espaiiolas. Ado Food Techno1 3 1747-1758.

Sanchez-Muiioz G, Garcia-Romero E, Perez-Coello M S 1992 Determination of glycerol in wines by high-performance liquid chromatography: Comparison with enzymatic method. J Chromatogr 623 43-47.

Somers T C, Evans M E 1977 Spectral evaluation of young red wines: Anthocyanin equilibria, total phenolics, free and molecular SO,, ‘chemical age’. J Sci Food Agric 28 279- 287.

Tapias R M, Larrechi M S, Guasch J, Rius F 1986 Enological parameters and pattern recognition methods in the geo- graphic differentiation of Spanish red wines. Am J Enol Vitic 37 195-201.

Tapias R M, Callao P, Larrechi M S, Guasch J, Rius F 1987 Application de I’analyse multidimensionnelle des donnees a la reconnaissance des vins rouges de Rioja. Conn Vigne Vin 21 43-57.

Vogt E, Jakob L, Lempere E, Weiss E 1986 El Vino: Obtencibn, Elaboracibn y Analisis. Acribia, Zaragoza, Spain, pp 24-33.