Journal of the Science of Food and Agriculture J Sci Food Agric 80:497±501 (2000)

Multivariate classification of wines from sevenclones of Monastrell grapesEncarna Gomez-Plaza,* Rocıo Gil-Munoz and Adrian Martınez-CutillasCentro de Investigacion y Desarrollo Agroalimentario, Consejeria de Medio Ambiente, Agricultura y Agua, Ctra La Alberca s/n, E-30150 LaAlberca, Murcia, Spain

(Rec

* Code EE-ma

# 2

Abstract: The enological characteristics of different clones of Monastrell grapes have been studied.

The wines obtained from different clones were subjected to chemical analyses, including the deter-

mination of organic acids, colour characteristics and mineral elements. Signi®cant differences were

found among clones. To check if these analytical parameters could correctly classify the wine samples,

multivariate statistical analysis was done. Cluster and principal component analysis classi®ed

correctly samples belonging to the same clone.

# 2000 Society of Chemical Industry

Keywords: red wine; colour characteristics; mineral elements; organic acids

INTRODUCTIONDifferent clones from one grape variety can differ in

their productive characteristics and their ability to

produce wines with different organoleptic characteris-

tics.1

Monastrell is the second largest red grape variety in

Spain. Owing to its economic importance, a clonal and

disease-free selection of this variety has been accom-

plished2 and now the enological characteristics of the

different selected clones are being studied. To charac-

terise wines, it is necessary to analyse various par-

ameters. The characterisation of wines by means of

their chemical composition has been extensively used

to differentiate among varieties3±7 and geographical

origin.8±10 Owing to the large number of parameters

that can characterise a wine, individually, each

analytical variable gives little information on the

membership of the group we want to characterise, so

the use of multivariate statistical methods is necessary.

These types of mathematical treatments have been

applied previously to wine classi®cation.11±14

The main aim of this investigation was to compare

the colour characteristics, organic acids and mineral

elements among wines from different clones of

Monastrell grapes and to obtain a multivariate

mathematical method which allows us to differentiate

among wines from different clones.

EXPERIMENTALSeven clones from Monastrell grapes were cultivated

in south-east Spain. Vines were planted at 3m�1.5m

spacing and trained to a two-wire trellis, with the lower

eived 14 June 1999; revised version received 29 September 1999; ac

rrespondence to: Encarna Gomez-Plaza, Unidad de Tecnologia de Aspinardo, E-30071 Murcia, Spainil: encarnag@fcu.um.es

000 Society of Chemical Industry. J Sci Food Agric 0022±5142/2

wire at about 0.4m and the upper wire at about 0.8m

from the ground. Vines were trained to a bilateral

cordon on the lower wire and spur-pruned to six two-

node spurs per vine. Supplemental irrigation was

provided by a drip system. The trial was established in

spring of 1990 as a planting of certi®ed rootings of

1103 Paulsen (Vitis berlandieri � Vitis rupestris).Rooting was ®eld-budded to Monastrell clones in

early spring of 1991. The trial was designed as a

randomised complete block with 10 vine experimental

units and three replicates. Grapes from the different

clones were harvested the same day and their charac-

teristics are shown in Table 1.

Vini®cations were carried out in an Experimental

Winery located in Jumilla (Murcia, Spain). Three

replicates of the vini®cation of each clone were done by

splitting 300kg of grapes of each clone into three lots.

Fermentation was conducted in 100l stainless steel

tanks where the temperature was controlled at 25°C.

No selected yeasts were added. Potassium metabisul-

phite was added at the beginning of fermentation

(50mgkgÿ1 grapes). Maceration was done in the

presence of skins, and when alcoholic fermentation

was ®nished, three rackings were made at 7, 14 and 45

days after the ®nal pressing of the grapes. Wines

underwent spontaneous malolactic fermentation and

then cold stabilisation (4°C, 1month). Samples were

taken and frozen immediately at ÿ24°C until ana-

lysed.

Mineral element determinationAtomic absorption spectrometry with a Phillips PU

9400� instrument was used for the determination of

cepted 4 November 1999)

limentos, Faculated de Veterinaria, Universidad de Murcia, Campus

000/$17.50 497

Table 1. Physicochemical characteristics of grapes from different clones�standard deviation (n=3)

Characteristic

Clones

21 373 263 35 188 118 231

Soluble solids (°Beaume) 11.52�0.81a 11.30�0.52a 11.92�0.51a 11.93�0.37a 12.02�0.80a 11.79�0.31a 12.00�0.61a

Berry weight (g) 2.22�0.17c 1.88�0.09ab 2.12�0.17bc 1.93�0.18ab 1.82�0.38bc 1.97�0.13abc 1.96�0.24abc

Yield (kg per vine) 7.97�1.09a 7.89�1.00a 9.05�1.05ab 9.36�0.71b 9.41�1.00b 8.80�0.20ab 9.42�0.78b

Titratable acidity (mglÿ1) 5.02�0.37ab 5.53�0.51bcd 5.21�0.39abc 5.99�0.32d 5.71�0.38cd 4.93�0.55a 5.36�0.50abcd

Different superscript letters within the same row denote signi®cant differences at P<0.05 according to the LSD test.

E GoÂmez-Plaza, R Gil-MunÄoz, A MartõÂnez-Cutillas

mineral elements. Iron was measured without any

sample dilution at 248nm using an air/acetylene ¯ame.

Calcium and magnesium were measured as the

absorptions at 422.7 and 285.2nm respectively after

a sample dilution of 1:25. Sodium and potassium were

measured as the emissions at 589 and 766nm

respectively after a 1:25 dilution of the samples.

Quanti®cation was done, preparing calibration curves

for each mineral element.

Colour determinationAbsorbance measurements were done on a Hitachi

2000 spectrophotometer (Tokyo, Japan) with 0.2cm

path length glass cells in the wavelength range between

420 and 630nm. The samples were cleaned and CO2

was eliminated using ultrasound and vacuum.

Colour de®nition was made using the CIELAB

space, using the C illuminant and a 2° observer.

Colour density was calculated as the sum of

absorbances at 520 and 420nm, and tint as the ratio

of absorbance at 420nm to absorbance at 520nm.15

Organic acid determinationOrganic acids were determined using HPLC under

isocratic conditions. A Hewlett-Packard HPLC sys-

Table 3. Concentration of organic acids (n=3) in wines from seven clones of Mona

Organic acid 21 373 263

Citric acid 0.25�0.05ab 0.20�0.02a 0.25�0.01ab 0

Tartaric acid 1.17�0.05a 1.34�0.07ab 1.57�0.09bc 1

Succinic acid 0.05�0.01a 0.05�0.02a 0.06�0.01ab 0

Lactic acid 2.42�0.06c 2.48�0.12c 2.84�0.22d 2

Different superscript letters within the same row denote signi®cant differences at P

Table 2. Concentration of mineral elements (n=3) in wines from seven clones of M

Element

C

21 373 263

Fe 1.10�0.03a 1.08�0.13a 1.60�0.03bc 1.57

Na 31.66�2.66a 30.66�1.85a 31.13�1.56a 32.23

K 808�7.37f 775�10.69e 727�28.37d 651

Ca 61.20�9.75c 48.20�3.62ab 60.03�2.65c 55.60

Mg 116.90�2.74d 110.30�2.34bc 121.00�1.63e 108.96

Different superscript letters within the same row denote signi®cant differences at P

498

tem equipped with an a ION 300 polymeric column

(Interaction Chemicals Inc; 300mm�7.8mm) and a

UV-vis detector was used. The mobile phase was

0.005N sulphuric acid at a ¯ow rate of 0.5mlminÿ1,

and the column temperature was 65°C. Organic acids

were detected at 210nm.

To isolate the organic acids from the wine, 1ml of

wine was passed thought a Sep-Pak C18 cartridge

(Lida, Wisconsin, USA). The cartridge was activated

by passing 3ml of methanol and 5ml of water. The

organic acids were then eluted with 2ml of 0.08N

sulphuric acid; 20ml were injected on the HPLC.

Identi®cation of organic acids was done by means of

pure standards, and external standard calibration was

done for quanti®cation, obtaining the calibration

curve for each organic acid.

Statistical data treatmentStatistical analyses were performed using Statgraphics

2.0 Plus.

RESULTS AND DISCUSSIONTables 2±4 show the mineral element content, organic

acid concentration and colour characteristics respec-

tively of wines from seven clones of Monastrell grapes.

strell grapes�standard deviation (g lÿ1)

Clones

35 188 118 231

.23�0.01ab 0.30�0.01bc 0.30�0.01bc 0.34�0.02c

.66�0.04c 1.75�0.09c 1.75�0.05c 1.50�0.10bc

.07�0.01ab 0.09�0.01b 0.11�0.01c 0.06�0.01a

.63�0.07c 2.06�0.07ab 2.15�0.09b 1.91�0.07a

<0.05 according to the LSD test.

onastrell grapes�standard deviation (mglÿ1)

lones

35 188 118 231

�0.17bc 1.83�0.25c 1.34�0.07ab 2.90�0.21d

�1.75ab 35.33�2.81bc 33.03�1.74ab 36.60�1.07c

�5.56a 714�8.38cd 699�9.50bc 677�1.07b

�4.45abc 47.13�1.23a 59.03�2.46bc 58.66�7.16bc

�1.72b 113.13�0.15c 107.23�2.87ab 104.83�2.11a

<0.05 according to the LSD test.

J Sci Food Agric 80:497±501 (2000)

Figure 1. Dendogram obtained from cluster analysis, following method ofaverage linkage between groups (Euclidean distances).

Table 4. Colour characteristics of wines from seven clones of Monastrell grapes�standard deviation (n=3)

Characteristic

Clones

21 373 263 35 188 118 231

Colour density 5.90�0.14ab 8.03�0.49c 6.77�0.48abc 5.65�0.41a 7.48�0.23c 7.41�0.36c 7.19�0.69bc

Tint 0.79�0.00b 0.69�0.00a 0.69�0.01a 0.69�0.00a 0.66�0.02a 0.68�0.02a 0.67�0.03a

L* 24.07�2.16bc 16.23�0.49a 24.20�2.98bc 26.79�2.12c 21.70�2.01abc 21.08�1.21abc 18.59�1.69ab

a* 47.86�2.83b 41.66�1.37a 47.74�0.80b 51.91�2.18c 51.77�0.92c 47.74�0.81b 42.71�1.69a

b* 34.83�2.74a 37.05�3.54ab 42.35�1.22c 35.04�0.78a 44.77�1.85c 41.15�2.85bc 34.50�2.68a

Different superscript letters within the same row denote signi®cant differences at P<0.05 according to the LSD test.

Multivariate classi®cation of wines from seven clones

Data from the vini®cations were subjected to ANOVA

to determine if signi®cant differences exist among

wines.

Regarding mineral elements, iron concentration was

low compared with data found in the literature.

Amerine and Ough16 and Arroyo17 considered that

normal values were in the range of 10mglÿ1. Higher

levels are reported in other wines.18 Iron plays an

important role in wine stability. An excess of iron can

cause turbidity and precipitation.19 The low values

found in these wines can be attributed to the use of

stainless steel tanks and pipes throughout the vini®ca-

tion process. Values of potassium ranged from

651mg lÿ1 for clone 35 to 808mg lÿ1 for clone 21.

The latter clone was the one that suffered the highest

loss of tartaric acid, probably owing to the high

concentration of potassium detected in this clone.

The concentration of sodium in wines is generally low,

except for wines made from grapes grown near the sea

or for wines stabilised with cationic exchange columns.

Values found in Rioja wines17 and French wines20 are

similar to ours, where the average sodium concentra-

tion is 32.94mg lÿ1. Magnesium concentration seems

to be related to edaphoclimatic characteristics. Gali-

cian wines have low values,21 while wines from other

areas of Spain have values closer to ours.17,22

Regarding organic acids, tartaric acid undergoes a

strong decrease from must to wine owing to the

precipitation of potassium bitartrate,23 the stability of

tartrates depending on pH, temperature and alcoholic

degree.24 Signi®cant differences could be found

among clones, clone 21 being the one with the lowest

content of tartaric acid and clones 35, 188 and 118

presenting the highest contents. The average content

of tartaric acid in these wines was higher than that

found by Javaloy et al25 in wines from the same area.

Malic acid could not be found in the wines, since

malolactic fermentation occurred. Therefore lactic

acid was detected in these wines, with values ranging

from 1.91g lÿ1 for clone 231 to 2.84g lÿ1 for clone

263.

Colour density was found to be low in clones 21 and

35, while the highest colour density was found in clone

373. Tint values were low, since these are young wines

with a high content of free anthocyanins (therefore

high values of absorbance at 520nm) and a low

content of polymeric compounds (therefore low values

of absorbance at 420nm). The CIELAB space was

J Sci Food Agric 80:497±501 (2000)

also used to achieve a better characterisation of wine

colour. L* (lightness) was signi®cantly lower in clone

373 (the one with the highest colour density), while the

highest value was found in clone 35, but with no

signi®cant differences from clones 21, 263, 118 and

188. The values of a* (redness) ranged from 41.66 for

clone 373 to 51.91 for clone 35, higher values than

those found by Almela et al13 in wines from Murcia.

Yellowness, represented by b* , had it highest value in

clone 188.

Multivariate statistical analysisTo check if the different chemical compounds

analysed in these wines can achieve a separation

among samples, a cluster analysis was carried out,

following the method of average linkage between

groups. As proximity measures we used Euclidean

distances. The samples were thus divided into seven

groups. Samples belonging to the same clone were

correctly classi®ed (Fig 1). The localisation of wine

samples from clone 21, separated from the rest of the

clones, con®rms that this wine, with high potassium

content, low tartaric acid content and high tint value,

differs from the rest of the wines.

In addition, principal component analysis (PCA)

was done. Cluster analysis gives information on the

similarity of the different samples, whereas PCA can

also show which variables account for most of the

variability in the data. For this study, all results were

obtained from the rotated matrix. The projection of

the sample con®guration on the ®rst two principal

components of the PCA is given in Fig 2. Greater

loadings of colour parameters L* and a* on PC2 can

499

Figure 2. Chemical and colour parameter loadings for first and secondprincipal components (percentage of variance explained on each principalcomponent).

E GoÂmez-Plaza, R Gil-MunÄoz, A MartõÂnez-Cutillas

be observed, while tartaric, succinic and citric acids

and iron and sodium possessed greater loadings on

PC1.

Fig 3 shows the distribution of the different clones in

the plane de®ned by the ®rst two principal compo-

nents. We can see that with this statistical analysis a

clustering of the clones was achieved, and this group-

ing is consistent with the classi®cation obtained from

the cluster analysis. Wines from clones 231, 373, 21,

263 and 35 are correctly grouped and separated from

each other. Clones 188 and 118 overlap, so they must

be very similar regarding their chemical composition,

as also shown in their proximity in the cluster analysis.

If we compare the distribution of the variables in the

same plane (Fig 2), it is clear that tartaric and succinic

acids and L* and a* are the parameters that make the

highest contribution to the localisation of clones 118,

188 and 35, while clone 231 is highly in¯uenced by

colour density as well as clone 373.

CONCLUSIONMultivariate statistical techniques applied to the

colour characteristics and organic acid and mineral

element concentrations in wines from seven different

clones allowed classi®cation of the samples into seven

consistent groups. The results of the analysis of

organic acids, colour and mineral elements together

Figure 3. Wine factor scores for first and second principal components(percentage of variance explained on each principal component).

500

with principal component analysis showed that clones

188, 118 and 35 provide wines with good colour

characteristics and a high content of tartaric acid.

These results for wine composition, together with the

high yield of these vines, make them the most

interesting clones for the elaboration of Monastrell

wines.

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