Food Chemistry 127 (2011) 1391–1396

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Food Chemistry

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Analytical Methods

Discrimination of sweet wines partially fermented by two osmo-ethanol-tolerantyeasts by gas chromatographic analysis and electronic nose

Teresa García-Martínez a, Andrea Bellincontro b, María de las Nieves López de Lerma c,Rafael Andrés Peinado c, Juan Carlos Mauricio a, Fabio Mencarelli b, Juan José Moreno c,⇑a Departamento de Microbiología, University of Córdoba, Spainb DISTA, Laboratorio Postraccolta, Tuscia University, Italyc Departamento de Química Agrícola y Edafología, University of Córdoba, Spain

a r t i c l e i n f o

Article history:Received 1 July 2010Received in revised form 29 December 2010Accepted 27 January 2011Available online 3 February 2011

Keywords:Sweet winePartial fermentationOsmotolerant yeastsVolatile compoundsPolyolsElectronic nose

0308-8146/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.foodchem.2011.01.130

⇑ Corresponding author. Tel.: +34 957 218636; fax:E-mail address: qe1movij@uco.es (J.J. Moreno).

a b s t r a c t

Some special sweet wines are obtained by partial fermentation of musts from off-vine dried grapes con-taining large amounts of sugars. This process is very slow and subject to serious stop problems that canbe avoided by using osmo-ethanol-tolerant yeasts. Musts containing 371 g/l of sugars were partially fer-mented with selected Saccharomyces cerevisiae strains, X4 and X5, to 12% (v/v) and the wines obtainedwith X5 exhibited a higher volatile acidity but lower concentrations of higher alcohols, carbonyl com-pounds and polyols than those obtained with X4. A principal component analysis (PCA) of the data pro-vided by an electronic nose (E-nose) afforded discrimination between fermented and unfermented musts,but not between wines obtained with X4 or X5. The PCA applied to the major volatile compounds andpolyols shows similar results, but a clear discrimination between wines is obtained by removing the poly-ols glycerol and 2,3-butanediol from the PCA.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction 2004b) and others have examined gene expression in response to

Musts from off-vine dried grapes contain large amounts of sug-ars and are used to obtain special sweet wines in some semi-aridviticultural areas around the world. Alcoholic fermentation in thesewines is very slow and subject to serious stop problems that can beavoided by using yeasts tolerant of high sugar and ethanol concen-trations. Off-vine dried grapes of the Pedro Ximenez variety providespecial sweet wines of a high quality. Musts from such grapes aredark brown in colour, highly dense and viscous, and possess a typ-ical aroma and sugar content in the region of 400 g/l. Such high su-gar content alters yeast activity and can substantially delay or evenstop alcoholic fermentation through the adverse effects of a highosmotic pressure and ethanol toxicity on the viability of yeasts,which exhibit a decreased activity in glucose transfer under theseconditions (Salmon, Vincent, Mauricio, Bely, & Barre, 1993). Fer-mentation in sugar-rich media is known to lead to wines with ahigh volatile acidity (Caridi, Crucitti, & Ramondino, 1999) and im-paired quality, as a result.

Saccharomyces cerevisiae strains exhibit an altered fermentationactivity under stressing conditions (Zuzuarregui & Del Olmo,2004a). Some authors have proposed strain selection proceduresbased on tolerance of such conditions (Zuzuarregui & Del Olmo,

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high concentrations of glucose (Erasmus, Van Der Merwe, & VanVuuren, 2003). In any case, the response of yeasts to such specialconditions is rather complex and influenced by a number of fac-tors, so selecting the optimum strain for each purpose entails usingappropriate winemaking tests.

Identification sensors capable of detecting metabolic changesduring fermentation, or in end-products, such as wine, can behighly useful for oenological research. Thus, an electronic nose(E-Nose) is an effective compendium tool for studying volatilecompounds, with wide adoption by the food industry (Di Nataleet al., 1999; Schaller, Bosset, & Escher, 1998). Especially with wine,E-noses have been used for vintage or variety discrimination(Aleixandre et al., 2008; Buratti, Benedetti, Scampicchio, &Pangerod, 2004; Di Natale, Davide, D’Amico, Nelli, & Sberveglieri,1995; García, Aleixandre, Gutiérrez, & Horrillo, 2006; Lozanoet al., 2006), vineyard recognition (Di Natale et al., 1996), qualitycharacteristic discrimination in combination with an electronictongue (Di Natale et al., 2004) or near infrared spectroscopy(Cozzolino et al., 2006) and the detection of Brettanomycescontamination (Cynkar, Cozzolino, Dambergs, Janik, & Gishen,2007). Comparatively less attention has been paid to the use ofE-noses for discriminating sweet wines obtained by fermentationwith various types of yeasts or unfermented musts from driedgrapes of the same variety, fermentation processes for sweet winesor the monitoring of metabolic changes in grape musts and wines.

0 h

124 h

140 h

165 h

189 h

Hoursof fermentation

1154 g/l

1062 g/l

1085 g/l

1078 g/l

1062 g/l

1154 g/l

1135 g/l

1083 g/l

1075 g/l

1058 g/l

1058 g/l

1120 g/l

1135 g/l

1154 g/l

All experiments were conducted in triplicate

6.33.106

cell/ml6.33.106

cell/ml

Fig. 1. Experiments designed to study partial fermentation with two S. cerevisiaeosmo-ethanol-tolerant strains.

1392 T. García-Martínez et al. / Food Chemistry 127 (2011) 1391–1396

Nevertheless, the last application has been used recently to studythe postharvest dehydration process of wine grapes (Bellincontroet al., 2009; Santonico, Bellincontro, De Santis, Di Natale, &Mencarelli, 2010).

This paper reports the results obtained by using an E-nose incombination with gas chromatography, to identify changes infermentation processes effected by pure starter cultures of osmo-ethanol-tolerant S. cerevisiae yeasts (strains X4 and X5) added tomusts from off-vine dried Pedro Ximenez grapes.

2. Material and methods

2.1. Must, prefermentative corrections and fermentation conditions

The must used, obtained from Pedro Ximenez partially driedgrapes, had a density of 1154.3 ± 1.5 g/l, equivalent to a reducingsugar content of 373 ± 4 g/l and a 22% (v/v) ethanol potential, inaddition to a titratable acidity of 3.31 ± 0.07 g/l (expressed as tar-taric acid), a volatile acidity (as acetic acid) of 0.07 ± 0.01 and apH of 4.30 ± 0.02.

Prior to fermentation, the must was corrected by addition oftartaric acid to a pH of 3.8, and supplied with potassium metab-isulphite to a concentration in SO2 of 50 mg/l. The available volumeof must was split into nine fractions (750 ml each) that wereplaced in 1 l cylinders, 3 of the 9 samples being subjected to no fer-mentation by adding ethanol up to 10% v/v and used as controls.The other six samples were split into two batches of three forapplication of the two strain yeasts. All cylinders were pluggedwith hydrophobic cotton and immersed in a thermostatted waterbath at 24 �C.

2.2. Yeasts and starter cultures

Fermentation tests were conducted with S. cerevisiae strains X4(CECT13014) and X5 (CECT13015) previously isolated during spon-taneous fermentation of musts from partially dried Pedro Ximenezgrapes. These strains were selected on the grounds of theirtolerance of high osmotic pressures and ethanol contents byGarcía-Martínez, Maestre, Peinado, Moreno, and Mauricio (2007).The starter cultures were prepared by growing each strainseparately in YPD medium at 28 �C for 2 h, which was followedby centrifugation and washing with distilled water. The yeast

population inoculated to each must sample contained 6.33 �106 cells/ml and each fermentation run was performed in tripli-cate. Samples of the same must, fortified with wine alcohol to10% (v/v) ethanol content, were used as controls. Fig. 1 depictsthe experimental procedure.

2.3. Analytical methods

2.3.1. Cell countsCell counts were obtained with Z2 Coulter equipment from

Beckman and the fermentation kinetics monitored via the densitymeasures.

2.3.2. Oenological analysesEthanol, titratable acidity, pH and volatile acidity were deter-

mined in accordance with the European Union Official Methods(CEE, 1990).

2.3.3. Major volatile aroma compounds and polyolsMethanol and major higher alcohols (propan-1-ol, isobutanol,

isoamyl alcohols and 2-phenyl-ethanol), carbonyl compounds(acetaldehyde and acetoin), various esters (ethyl acetate, ethyl lac-tate and ethyl succinate) and the polyols glycerol and 2,3-butane-diol were quantified with a Model 6890 gas chromatograph fromAgilent (Palo Alto, CA) using the method of Peinado, Moreno,Muñoz, Medina, and Moreno (2004). A CP-WAX 57 CB capillary col-umn (60 m long � 0.25 mm i.d., 0.4 lm film thickness) from Varian(Palo Alto, CA) was used, and 0.5 ll aliquots from 10 ml samples(previously supplied with 1 ml of 1 g/l 4-methyl-2-pentanol asinternal standard) were injected into the split/splitless injector ofthe GC instrument. Tartaric acid in the wine was removed by pre-cipitation with 0.2 g of calcium carbonate and centrifugation at4100g and 4 �C. Quantitation was based on the response factorsfor standard solutions of each compound. A split ratio of 30:1, anFID type detector, and a temperature programme involving an ini-tial temperature of 50 �C (15 min), a 4 �C/min ramp and a finaltemperature of 190 �C (35 min) were used. The injector and detec-tor temperatures were 270 and 300 �C, respectively. The flow rateof carrier gas (helium) was initially set at 0.7 ml/min (16 min) andfollowed by a 0.2 ml/min ramp to the final value (1.1 ml/min),which was held for 52 min.

2.3.4. Electronic noseThe E-nose used was designed, developed and assembled at the

University of Rome Tor Vergata. The nose uses an array of eightquartz microbalances (QMBs), each QMB being an electromechan-ical resonator, the resonant frequency of which changes in propor-tion to the material adsorbed onto its sensor surface. The sensorswere AT-cut quartz plates oscillating at a resonance frequency of20 MHz in the thickness shear mode. Quartz drives electronic oscil-lators whose frequency is barely similar to the mechanical reso-nance frequency. QMBs were functionalised by deposition ofsolid state layers of metalloporphyrins. Metalloporphyrins havebeen widely investigated as fundamental blocks for artificialolfactory receptors (Di Natale, Paolesse, & D’Amico, 2007). Allmetalloporphyrins were metal complexes of 5,10,15,20-tetrakis-(4-butyloxyphenyl)porphyrin; the metals, which differed betweensensors, included cobalt, zinc, iron, tin, copper, manganese, ruthe-nium and chromium. Sensing layers were deposited onto bothfaces of each QMB by spray-casting of the metalloporphyrins dis-solved in a suitable solvent (10�3 M in CHCl3). In order to controlthe amount of metalloporphyrin film deposited, the resonance fre-quency of each QMB was measured on-line with a high-stabilityfrequency counter during deposition. A frequency variation ofca. 60 kHz was obtained for all deposited layers. The array was con-trolled by software run on a computer that was used to acquire and

1040

1060

1080

1100

1120

1140

1160

0 50 100 150 200 250

g /L

Hours

Kinetics of fermentation

Control

S. cerevisiae X4

S. cerevisiae X5

Fig. 2. Density (g/l) during the testing of control (unfermented) must andfermentation with two selected osmo-ethanol-tolerant S. cerevisiae yeast strainsX4 and X5.

T. García-Martínez et al. / Food Chemistry 127 (2011) 1391–1396 1393

process the sensor signals. The sampling protocol for must andwines measurement involved the following steps: a volume of5 ml of sample was collected in a flask to which 5 ml of saturatedCaCl2 solution was added; the flasks were then placed in a thermo-statted bath at 20 ± 2 �C, under continuous stirring provided by asmall magnetic bar for 30 min; then, the equilibrated headspacewas extracted using a stream of air filtered through a trap filledwith granular anhydrous calcium chloride and delivered into theE-nose sensor cell. Finally, a pure nitrogen stream was used toclean the sensors for establishment of the reference signal. Sensorsignals were calculated as the resonant frequency shifts betweenthe two steady conditions corresponding to sensors exposed topure nitrogen and to the sample diluted in filtered air. The bodyof sensor signals formed a pattern (fingerprint) encoding the globalcomposition of the headspace. Three different samples collectedfrom the three cylinders prepared for each treatment (one controland two yeast strain applications) were analysed in triplicate. Themean values of the obtained results were successively used forprincipal component analysis (PCA) calculations.

2.3.5. Statistical analysisEach sample was measured in triplicate and the resulting mean

used for analysis. The data provided by both the E-nose and GCanalysis were subjected to PCA, using The Unscrambler� v. 9.2 soft-

Table 1Chemical analysis of wines obtained by fermentation with osmo-ethanol-tolerant yeast st

Fraction or compound Control S. c

Mean SD HG Me

Titratable acidity (g/l) 4 0.2 aVolatile acidity (g/l) 0.32 0.06 apH 3.83 0.01 aDensity (g/l) 1127 7 b 105Ethanol% v/v 9.9 0.2 a 1Methanol (mg/l) 124 9 a, b 161-Propanol (mg/l) ND 0 a 5Isobutanol (mg/l) ND 0 a 2Isoamyl alcohol (mg/l) 4 0.5 a 142-Phenylethanol (mg/l) 13 2 a 2Acetaldehyde (mg/l) 10 0.3 a 7Acetoin (mg/l) 121 4 b 8Ethyl acetate (mg/l) 19 2 a 22,3-Butanediol m + l (g/l) 0.14 0.01 aGlycerol (g/l) 2.24 0.06 a 1

ND = not detected. H.G.: homogeneous groups among three wine types for each compoun

ware (CAMO ASA, Oslo, Norway). In data matrices employed formultivariate statistical applications, the variable sets wererepresented by the eight averaged measurements of different sen-sors for each sample, and by the 13 averaged quantifications of vol-atile compounds for each sample, respectively, for E-nose and GCanalyses. Data coming from E-nose detection were linearly norma-lised before PCA application. Results were validated by full internalcross validation.

3. Results and discussion

Figs. 1 and 2 show the procedure followed for the partial fer-mentation of the musts and their fermentation kinetics. As canbe seen, the musts supplied with strain X5 exhibited an increasedfermentation rate; thus, their density amounted to 1078 g/l (versus1083 g/l with X4) after 140 h.

Fermentation was stopped by adding wine alcohol to 15% (v/v)when the density of each must fell to about 1075 g/l. This took140 h with strain X5 and 165 h with X4. The amounts of ethanolproduced, until then, by both yeasts were identical: 12% (v/v).

The addition of ethanol reduced the density to 1055 g/l. At thatpoint, the content in sugars of the musts was 170 g/l. Followingaddition of the alcohol, the density exhibited a small decreasedue to settling of suspended particles in the bottom of the musts.

The control tests involved the addition of ethanol to the initialunfermented must to a content of 10% (v/v), as is the traditionalmethod used for the elaboration of these special wines, avoidingtheir alcoholic fermentation. This reduced the density, initially to1135 g/l and then to 1127 g/l by effect of settling of solid particlesduring the test.

All completed tests were finished 211 h after the starter cul-tures – or ethanol in the control test – were added. Then, the sam-ples were subjected to non-destructive analysis with E-nose anddestructive analysis with chemical methods. Table 1 shows the re-sults obtained with the chemical and gas chromatographic analy-sis, as well as those of a multiple comparison procedure toidentify any means significantly different from the others by usingBonferroni’s method.

The average contents of titratable and volatile acidity allowedthe three homogeneous groups, in accordance with the three typesof musts, to be accurately discriminated at the 95% confidence le-vel (CL). By contrast, the ethanol content and density only distin-guished between fermented and unfermented musts, and pHvalues were essentially identical for all must types. The volatile

rains X4 and X5, and a control consisting of unfermented must.

erevisiae X4 S. cerevisiae X5

an SD HG Mean SD HG

7.1 0.2 c 6.13 0.02 b1.2 0.0 b 1.8 0.3 c3.8 0.09 a 3.86 0.04 a5 4 a 1050 10 a4.7 0.5 b 15.2 0.2 b3 25 b 113 10 a7 3 c 17 2 b9 1 b 27 2 b6 2 b 146 4 b8 1 c 20 1 b6 2 c 56 3 b2 12 a 70 2 a9.4 0.5 b 30 2 b2.4 0.2 c 1.9 0.1 b2 1 c 9 1 b

d. Different letters denote significant differences at the 95% level in Bonferroni’s test.

(o) Scores Plot

C2C3

X41

X42

X43

X51

X52X53

C1

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25

PC1 (95.91%)

PC

2 (2

.81%

)

Fig. 3. Plot of sample scores in the principal components obtained with theelectronic nose data C1; C2; C3: control sweet wines (unfermented). X41; X42; X43:sweet wines obtained by must fermented with S. cerevisiae strain X4. X51; X52;X53: sweet wines obtained by must fermented with S. cerevisiae strain X5.

Biplot: (o) scores; (+) loads

C2

X41X42

X43

X52

X53

+1+2+3+4+5+6+7+811+ 01+9+

C3

X51

+12

+13

C1

-1

-0.5

0

0.5

1

1.5

-1.5 -1 -0.5 0 0.5 1 1.5PC1 (99.63%)

PC

2 (0

.37%

)

Fig. 4. Biplot of sample scores and variable loads obtained by principal componentsanalysis of all the compounds quantified by GC-analysis. C1; C2; C3: control sweetwines (unfermented). X41; X42; X43: sweet wines obtained by must fermentedwith S. cerevisiae strain X4. X51; X52; X53: sweet wines obtained by mustfermented with S. cerevisiae strain X5.

1394 T. García-Martínez et al. / Food Chemistry 127 (2011) 1391–1396

compounds studied are known to be produced during alcoholic fer-mentation and related to the particular yeast species or race used(Moreno, Millan, Ortega, & Medina, 1991; Ribéreau-Gayon,Dubourdieu, Donèche, & Lonvaud, 2000). In fact, 7 of the 11 vola-tiles exhibited significant differences in content (95% CL) betweenthe unfermented must and the two fermented musts. The metha-nol contents in the control musts can be grouped (95% CL) in eachhomogeneous group formed by every fermented must. Three com-pounds (1,1-diethoxyethane, ethyl lactate and diethyl succinate)exhibited no significant differences in this context and theyshowed no detectable contents in any samples, so they are notshown in the Table 1. The musts fermented with the two yeaststrains exhibited significantly different contents of four volatiles(namely methanol, propanol, 2-phenylethanol and acetaldehyde),but statistically identical contents of isobutanol, isoamyl alcoholsand acetoin. Especially prominent among carbonyl compoundswere acetoin in the control must, in accordance with the resultsobtained by Franco, Peinado, Medina, and Moreno (2004), studyingthe grape drying process of Pedro Ximenez grapes and those ob-tained by Bellincontro et al. (2009) and Chkaiban et al. (2007). Incontrast, the fermented musts exhibited increased contents ofacetaldehyde and typical levels for acetoin; both compounds wereproduced to a slightly greater extent by the X4 yeast strain. Finally,the mean contents of the polyols, 2,3-butanediol and glycerol, dif-fered significantly between musts and formed three homogeneoussample groups at CL = 95%. As is known, the osmo-tolerant yeasts,are able to synthesise and to retain high quantities of polyols, par-ticularly glycerol (Nevoigt & Stahl, 1997) and some yeasts evenpossess pumps for the active reception of glycerol of the media(Myers, Lawlor, & Attfield, 1997).

In summary, the results of the chemical analysis show thatstrain X4 produces wine with a decreased volatile acidity and in-creased contents of propanol, 2-phenylethanol, 2,3-butanedioland glycerol relative to strain X5; also, the former strain exhibitsslower fermentation kinetics.

The data values obtained by the sensors of the E-nose, for bothfermented musts yeast strains, and also those for the control must(unfermented) were all very similar. The multiple sample compar-ison procedure reveals no significant differences, for each sensor,between different samples, and a greater difference between sen-sor values within each sample type.

The wines obtained were subjected to blind tasting by expertsfrom the collaborating winemaker; the tasters successfully dis-criminated the three types of wine and gave the highest score tothat obtained by partial fermentation with strain X5.

Both the results obtained with the E-nose and those provided bygas-chromatographic analysis suggest the need to use advanced sta-tistical procedures to distinguish the three types of wine, with a viewto approaching the discriminating sensory ability of the tasters.

We chose to use PCA of linearly normalised data for this pur-pose. Normalising the data ensured efficient suppression ofquantitative effects on the multivariate data. The efficiency ofthis procedure is a result of the linear dependence of the sensorsignal on the amount of molecules in air penetrating through theE-nose. In order to illustrate the procedure, let us consider thatsensors are exposed to a single gas whose concentration is cand that each sensor responds with a frequency shift – Dfi,where i is related to the array sensor; if the linearity assumptionholds, then each sensor will possess a characteristic sensitivity Ki

to the gas in question. In this way, the signal of the i-th sensorcan be defined asDfi ¼ Ki � c

Linear normalisation here involves calculating a reduced vari-able by dividing each sensor signal into the sum of the signalsfor all sensors in the array:

DfNi¼ DfiP

jDfi¼ Ki � cP

jDfi � c

¼ KiP

jDfi

where the summation is extended to the – j – sensors. As a result ofthe normalisation, the variable DfN is no longer dependent on theconcentration. The efficiency of this procedure has been demon-strated with an experiment involving the identification of gasesand vapours at different concentrations (Di Natale et al., 1999).Since linear normalisation reduces the influence of the concentra-tions of volatiles in the sample, it also reduces correlation betweensensor responses. As a consequence, applying PCA to the normalised

Biplot: (o) normalised scores; (+) loads

C1C2C3

X41

X42

X43

X51X53

+1+2+3

+4

+5

+6

+8

01+9++11+13

X52 +7

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-1.5 -1 -0.5 0 0.5 1 1.5PC1 (99.65%)

PC

2 (0

.35%

)

Fig. 5. Biplot of sample scores and variable loads obtained by principal componentsanalysis of all the compounds quantified by GC-analysis excluding 2,3-butanediol(+12) and glycerol (+13). C1; C2; C3: control sweet wines (unfermented). X41; X42;X43: sweet wines obtained by must fermented with S. cerevisiae strain X4. X51;X52; X53: sweet wines obtained by must fermented with S. cerevisiae strain X5.

T. García-Martínez et al. / Food Chemistry 127 (2011) 1391–1396 1395

data results in the number of significant principal componentsexceeding that for a PCA of the original responses.

As can be seen in Fig. 3 for the PCA of sensor data, principalcomponent 1 (PC1) explained 95.91% of the overall variance andthe sensor numbered 5 had the strongest effect on it. In addition,PC2 accounted for only 2.81% of the variance and sensor 3 wasits greatest contributor.

Fig. 4 shows results of the PCA for the gas chromatographicanalysis, using, as variables: acetaldehyde (1); ethyl acetate (2);methanol (4); 1-propanol (5); isobutanol (6); isoamyl alcohol (7);acetoin (8); 2-phenylethanol (11); 2,3-butanediol (meso + levoforms) (12); glycerol (13). This figure shows that PC1 explains99.63% of the overall variance and the variables numbered 12and 13 were its most significant contributors. On the other hand,PC2 accounted for only 0.37% of the variance.

The score plots of the samples in relation to the PCA results forthe E-nose and gas chromatographic analysis are shown in Figs. 3and 4. Based on Fig. 3, it seems difficult to properly discriminatebetween wines obtained by partial fermentation of must in thepresence of yeast strains X4 and X5, even though distinction ofthe control (unfermented) wine from the two fermented wines isindeed possible with the E-nose. This is also the case with thegas chromatographic method, with which only the 2,3-butanedioland glycerol, among volatiles, contribute significantly to the vari-ance, whereas other volatiles are completely overlapped.

Taking into account the similarity in the groups (established byPCA of the GC and E-nose data for the must types), we can drawsome conclusions. Regarding the results obtained from the PCAof GC-quantified compounds, elimination of glycerol and 2,3-butanediol, the compounds with the highest values of explainedvariance in PC1, provides a clear discrimination between the twostrains and between these and the control (Fig. 5). The same resultis obtained by excluding each compound (glycerol or 2,3-butane-diol) one at a time. Close to strain X5 we found volatile compoundnumber 7 (isoamyl alcohol) while, in the same quadrant of strainX4, volatiles 4, 5 and 8 are localised (methanol, 1-propanol, andacetoin); 2,3-butanediol and glycerol, as polyols, are strong osmo-tic compounds and their presence in an osmotic strain is natural;their effects in terms of compounds quantified by GC was alreadyevident. Indeed, PCA completely separated the two compounds in

Fig. 4, where no elimination was done. They were localised far fromeach other and also even from the other volatile compounds. Whenthe elimination of the two polyols was performed, the higher con-centrations of the compounds methanol, 1-propanol, and acetoinin wine produced from the X4 strain determined their presencein the same quadrant of this yeast strain, while isoamyl alcoholconcentrations are similar in the two strains but, in the PCA, movewith X5 strain. This result is important because it emphasises thedifference between analyses performed by GC and by the E-nose.The latter is based on pattern recognition, and is unable to discrim-inate single compounds, regardless of the concentration.

4. Conclusions

Partial fermentation of musts from off-vine dried grapes with S.cerevisiae strains X4 and X5, selected by their tolerance of highsugar and ethanol contents, revealed that X4 had slower fermenta-tion kinetics, and produced less volatile acidity, but more 2-phenylethanol, 2,3-butanediol and glycerol, than X5. However,the sweet wines obtained with X5 were better scored in a blindsensory test. The results of a PCA of the data obtained with anE-nose and those of major volatile compounds and polyols quanti-fied by gas chromatography reveal that distinguishing of fer-mented and unfermented musts is possible. A discriminationbetween wines produced by the two yeast strains is obtained byexcluding 2,3-butanediol and glycerol from the PCA of thecompounds quantified by GC.

Acknowledgements

This work was funded by Spain’s Ministry of Science andInnovation (INIA, Project RTA 2008-00056-C02-02), FEDER, theSpanish–Italian Integrated Action HI 2007-0017 and by an FPUscholarship of the Ministry of Education (announcement 2008).The authors are also grateful to Cooperativa San Acacio(Montemayor, Córdoba, Spain) and Bodega Alvear S.A. for theirkind cooperation.

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