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Page 1: Chemometric investigation of the volatile content of young South African wines

Food Chemistry 128 (2011) 1100–1109

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Food Chemistry

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Analytical Methods

Chemometric investigation of the volatile content of young South African wines

Berhane T. Weldegergis 1, André de Villiers, Andrew M. Crouch ⇑Department of Chemistry and Polymer Science, University of Stellenbosch, Private Bag X1, Matieland 7602, Stellenbosch, South Africa

a r t i c l e i n f o a b s t r a c t

Article history:Received 7 April 2009Received in revised form 1 April 2010Accepted 28 September 2010Available online 6 October 2010

Keywords:Volatile compoundsWineChemometricsPinotageCultivar

0308-8146/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.foodchem.2010.09.100

⇑ Corresponding author. Present address: The FacultWitwatersrand, Private Bag 3, WITS 2050, South Afric+27 11 7176028.

E-mail address: [email protected] (A.M. C1 Present address: Laboratory of Entomology, W

Box 8031, 6700 EH Wageningen, The Netherlands.

The content of major volatiles of 334 wines of six different cultivars (Sauvignon Blanc, Chardonnay, Pin-otage, Shiraz, Cabernet Sauvignon and Merlot) and vintage 2005 was used to investigate the aroma con-tent of young South African wines. Wines were sourced from six different regions and various producers.Thirty-nine volatile components partially responsible for the flavour of wine were quantified. In order toinvestigate possible correlation between volatile content and grape variety and/or geographical origin,analysis of variance, factor analysis (FA), principal component analysis (PCA) and linear discriminantanalysis (LDA) were used. Significant differences in the levels of certain volatiles were observed as a func-tion of region and cultivar, with the latter factor proving to be more influential. A few volatile compoundswere identified as potential predictors of the white wine cultivars. Prediction for red wine cultivars waspoor, with the exception of Pinotage wines, for which three compounds (isoamyl acetate, isoamyl alcoholand ethyl octanoate) were identified as accurate predictors. The reasons for the importance of these threevolatile compounds in distinguishing young Pinotage wines are discussed, and possible reasons for theunique levels in wines of this cultivar are highlighted.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction acetate was reported as an important aroma constituent of

In an exceedingly competitive international market, wine pro-ducers need to invest in technology to improve production andproduct quality to remain competitive. As the market is the maindriving force behind wine research, it is essential to understandconsumer preferences. Human physiology and psychology is asso-ciated with the behavioural response of people, who may have cer-tain preferences regardless of their knowledge of the chemicalcomposition of wine. However, it is equally important to under-stand the relationship between the chemical nature and sensoryproperties of wines, and by extension the enologic and viticulturalpractices influential in determining the chemical content of wine.

Wine aroma is one of the most influential properties when itcomes to consumer preference, and is mainly determined by thevolatile compounds. Certain volatiles, referred to as impact odor-ants, are characteristics for particular wine varieties. For instance,norisoprenoid compounds contribute to the varietal character ofChardonnay wines (Sefton, Francis, & Williams, 1993), methoxy-pyrazines contribute to distinctive Sauvignon Blanc and CabernetSauvignon aroma (Allen, Lacey, Harris, & Brown, 1991), and isoamyl

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y of Science, University of thea. Tel.: +27 11 7176012, fax

rouch).ageningen University, P.O

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young Pinotage wines (van Wyk, Augustyn, de Wet, & Joubert,1979). Similarly, furaneol (4-hydroxy-2,5-dimethylfuran-3(2H)-one) has been reported as a caramel odour contributor to Merlotaroma (Kotseridis, Razungles, Bertrand, & Baumes, 2000), while asesquiterpene, rotundone, has recently been shown to be responsi-ble for the pepper aroma associated with Shiraz wines (Wood et al.,2008). Aside from these impact odorants, the common and univer-sal pattern of wine volatiles is dominated by the major fermenta-tion products such as alcohols, esters and fatty acids (Stashenko,Macku, & Shibamato, 1992).

The flavour of young wines results from a series of different bio-chemical and technological processes. Formation of volatile com-pounds begins in the grape, while during juice production,fermentation, maturation, ageing and storage the chemical compo-sition continues to change. The amount and type of chemicals thatinfluence wine flavour therefore depend on many factors includingthe origin of the grapes, grape varieties and ripeness, soil and cli-mate, yeast used during fermentation and a variety of other wine-making practices (Kotseridis & Baumes, 2000; Rapp, 1998;Spranger et al., 2004).

Taking into consideration the diverse factors that affect the le-vel of each volatile compound in wine, it is often difficult to mean-ingfully interpret volatile data and establish a relationshipbetween the chemical constituents and particular sensory proper-ties or manufacturing processes. Chemometric methods, in partic-ular multivariate data analysis methods, have proven particularlyuseful in studies involving the evaluation of food quality and/orauthenticity, and indeed their application to wine characterisation

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B.T. Weldegergis et al. / Food Chemistry 128 (2011) 1100–1109 1101

and classification has increased in recent years. Principalcomponent analysis (PCA) and discriminant analysis (DA) in partic-ular have extensively been applied to characterise wines based ontheir volatile content (Rapp, 1998; Spranger et al., 2004; Santoset al., 2004; Falqué, Fernández, & Dubourdieu, 2001, 2002). Clusteranalysis (CA) has been used to categorise wines based on their vol-atile composition (Herraiz, Reglero, Herraiz, Martin-Alvarez, & Cab-ezudo, 1990; Mateo, Jiménez, Pastor, & Huerta, 2001). Sivertsen,Holen, Nicolaysen, and Risvik (1999) have classified French redwines according to their geographical origin based on sensoryand chemical data. Analysis of variance (ANOVA), PCA, CA, andDA have been used to classify South African wines according to cul-tivar based on volatile (Tredoux et al., 2008) and non-volatile con-tent (de Villiers, Vanhoenacker, Majek, & Sandra, 2004; de Villierset al., 2005). Recently we have used the levels of major volatiles toclassify Pinotage wines according to vintage using PCA (Weldeger-gis & Crouch, 2008).

The present report aims to extend previous work on the volatilecontent of South African wine to a much larger and statisticallymore significant number of samples. The goal was to further inves-tigate the variation in volatile content of young South Africanwines as a function of specifically region of origin and grape vari-ety. To this end, 334 wine samples from six different cultivars (Sau-vignon Blanc, Chardonnay, Pinotage, Shiraz, Cabernet Sauvignonand Merlot) of vintage 2005 were analysed for their content of39 major volatile compounds using the method reported previ-ously (Weldegergis & Crouch, 2008). The choice of a single vintagefurther reduces the impact of wine age on volatile composition.ANOVA, factor analysis (FA) and multidimensional principal com-ponent analysis (MD-PCA) were used. In addition, the predictivemethod linear discriminant analysis (LDA) was used to identifypotentially influential compounds capable of differentiating be-tween wine samples of different cultivar.

2. Materials and methods

2.1. Wine samples

Three hundred and thirty-four young South African wines (65Sauvignon Blanc (SB), 45 Chardonnay (CH), 41 Pinotage (PI), 64Shiraz (SH), 60 Cabernet Sauvignon (CS) and 59 Merlot (M)) fromvintage 2005 were obtained from the South African Young WineShow. The wines originated from most of the important South Afri-can wine producing regions including Paarl (P), Stellenbosch (S),Worcester (W), Robertson (RO), Olifants River (OR), and Swartland(SW). Results for the Pinotage wines of vintage 2005 previously re-ported (Weldegergis & Crouch, 2008) are also included in this workfor comparison and correlation purposes.

2.2. Analytical procedure

A volume of 500 ll wine, 50 ll internal standard solution(1.7 mg/l of 4-methyl-2-pentanol in blank model wine (12% etha-nol, and 2 g/l tartaric acid in Milli-Q water)) and 1.5 g NaCl weretransferred to a 20 ml headspace vial. The volume was adjustedto 6 ml using a blank model wine, and a glass coated magnetic stir-rer was added to the mixture. A Twister™ stir bar (Gerstel�, Müll-heim a/d Ruhr, Germany) was suspended in the headspace using aglass insert (Gerstel), and the vial was sealed using a hand crimper.The mixture was stirred for 1 h at 1200 rpm and room temperature(23 ± 1 �C).

Following sampling and gentle drying using lint free tissue, thestir bar was placed in a TDS-A auto-sampler tray (Gerstel, Müll-heim a/d Ruhr, Germany). Desorption was performed in a TDS 2thermal desorption unit according to the following temperature

program: 30 �C for 1 min, ramped at 20 �C/min to 260 �C, and heldfor 10 min. Analytes were trapped in a programmed temperaturevaporising (PTV) inlet at �100 �C using liquid nitrogen prior toinjection. The PTV was operated in splitless mode for 2 min andheated for injection from �100 to 270 �C at 12 �C/s and kept for10 min.

An Agilent 6890 GC coupled to a 5973 N MS (Agilent Technolo-gies, Palo Alto, CA) equipped with an HP-INNOWax column(30 m � 0.250 mm i.d. � 0.5 lm df, Agilent Technologies) was usedfor volatile separation using helium as carrier gas at a flow rate of1 ml/min in constant pressure mode. The GC oven temperaturewas initially held at 30 �C for 2 min, ramped to 130 �C at a rateof 4 �C/min and then at 8 �C/min to 250 �C, where it was kept for5 min. Spectra were recorded in electron impact ionisation mode(EI) at 70 eV. The MS transfer line, source and quadrupole wereset to 250, 230, and 150 �C, respectively.

For compound identification the MS was operated in scan modewith a scan range of 30–350 amu at 4.45 scans/s. Compound iden-tification was based on comparison with Wiley 275 and NIST 98mass spectral libraries, and retention times of authentic standardsfor all compounds. Experimentally calculated linear retention indi-ces (LRI) were used as additional identification criterion.

For quantification the MS was operated exclusively in the se-lected ion monitoring (SIM) mode using three ions (one quantita-tive and two qualitative ions) with dwell time of 50 ms for eachcompound. For further details on the analytical method, the readeris referred to (Weldegergis & Crouch, 2008).

2.3. Statistical analysis

The measured concentrations of 37 volatile compounds in eachwine were used for multivariate data analysis following standard-ization of all variables to 0 mean and 1 standard deviation. Analysisof variance (both one-way and main effects ANOVA), factor analy-sis, and discriminant analysis were performed using STATISTICA v8(StatSoft, Inc., Tulsa, OK, USA).

Factor analysis (FA) is a statistical method used to reduce thenumber of variables of complex multivariate data sets by maximis-ing the information presented using a limited number of factors.Similarly, principal component analysis (PCA) involves transforma-tion of set of variables to a new coordinate system, in which thenew axes are the principal components, following the directionof highest variance in the data set. These principal componentsare orthogonal to one another and constructed in such a mannerthat the amount of residual variation decreases with increasingnumber of principal components. PCA bi-plots were constructedusing a statistical package written in-house in the R statistical pro-gramming language (BiplotGUI).

Linear discriminant analysis (LDA) is a statistical technique usedfor prediction purposes that examines the set of variables associatedwith a given object and assigns the object to a group or class basedon similarities and differences between variables. In LDA, a lineardiscriminant function is formulated that describes the importanceof the independent variables in differentiating objects of knowngroup membership (i.e. cultivar). A 5% significance level (p = 0.05)was used as a guideline for determining significant differences.

3. Results and discussion

3.1. Wine analysis

The validated analytical method provided limits of detection(LODs) and limits of quantification (LOQs) in the range of 0.050–281 ng/l and 0.180–938 ng/l, respectively. Repeatability for themethod was between 6% and 20% (Weldegergis & Crouch, 2008).

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All 39 volatile components studied were identified in most of thewines, with the exception of trans- and cis-oak-lactones. In thesamples where these two isomers were identified and quantified,their mean values were observed to vary between 0.980 and1.13 mg/l. The variable levels of these compounds are presumablyrelated to differences in levels of wood contact between the wines(Jarauta, Cacho, & Ferreira, 2005). Since these two compounds werenot quantified in all samples, they were excluded from the statisti-cal analysis.

3.2. Analysis of variance (ANOVA)

As a first step, main effects ANOVA for white and red wine cul-tivars separately was performed using the measured concentra-tions of the 37 quantified volatile compounds for samplesgrouped according to region of origin. Main effects ANOVA was ap-plied since variation in volatile content amongst wines from differ-ent regions could potentially be overshadowed by the effect ofgrape variety. Table 1 presents ANOVA results of the volatile com-pounds between white wines (SB & CH) from the six different re-gions. With the exception of acetic acid, 2-phenethyl acetate and2,6-dimethoxy phenol, none of the compounds showed significantdifferences.

Table 1Main effects ANOVA results for the mean values (mg/l) of volatile compounds for white w

No. Compounds Regionsa

Mean ± SD (mg/l)b

P (n = 22) S (n = 17) W (n

1 Ethyl acetate 115 ± 32.5 130 ± 23.6 112 Ethyl butyrate 0.438 ± 0.113 448 ± 96.8c 0.453 1-Propanol 25.7 ± 15.1 25.1 ± 9.25 31.4 Isobutanol 23.2 ± 14.5 30.0 ± 10.4 26.5 Isoamyl acetate 3.72 ± 3.08 2.87 ± 3.14 4.76 n-Butanol 6.95 ± 3.54 7.50 ± 3.37 7.47 Isoamyl alcohol 120 ± 12.9 128 ± 14.4 128 Ethyl hexanoate 0.610 ± 0.151 0.580 ± 0.133 0.559 Hexyl acetate 80.0 ± 28.0c 91.7 ± 38.3c 82.

10 Acetoin 36.9 ± 16.8 44.0 ± 15.6 36.11 Ethyl-D-lactate 11.9 ± 7.50 19.0 ± 13.9 30.12 1-Hexanol 0.596 ± 0.344 0.454 ± 0.325 0.5213 Ethyl octanoate 97.3 ± 23.9c 89.1 ± 20.4c 86.14 Acetic acid 335 ± 211 241 ± 76.0 5315 Furfural 12.0 ± 6.01 13.1 ± 6.27 12.16 Propionic acid 13.9 ± 6.34 15.0 ± 7.14 11.17 Isobutyric acid 1.20 ± 0.703 1.63 ± 1.63 1.118 5-Methylfurfural 0.452 ± 0.187 0.294 ± 0.227 0.3619 n-Butyric acid 3.22 ± 1.63 3.51 ± 1.38 3.220 Ethyl decanoate 104.5 ± 72.4c 12.3 ± 9.00c 10.21 Isovaleric acid 1.52 ± 0.508 1.46 ± 0.613 1.422 Diethyl succinate 4.65 ± 0.368 4.43 ± 0.291 4.623 n-Valeric acid 1.68 ± 0.580 1.41 ± 0.0911 1.524 2-Phenethyl acetate 0.444 ± 0.224 0.467 ± 0.165 0.3525 Hexanoic acid 4.86 ± 1.16 4.77 ± 0.926 4.626 Guaiacol 0.238 ± 0.142 209 ± 91.0c 0.2627 2-Phenylethyl alcohol 8.57 ± 3.82 8.71 ± 2.27 8.628 o-Cresol 818 ± 64.1c 790 ± 41.2c 8129 Phenol 0.626 ± 0.456 0.448 ± 0.260 0.6130 4-Ethylguaiacol 349 ± 8.31c 346 ± 4.81c 3531 Octanoic acid 2.46 ± 0.678 2.81 ± 0.951 2.432 p-Cresol 255 ± 51.0c 229 ± 16.9c 2533 Eugenol 598 ± 59.8c 574 ± 41.4c 6034 Decanoic acid 0.799 ± 0.151 0.796 ± 0.129 0.7935 2,6-Dimethoxy phenol 4.78 ± 1.50 2.42 ± 1.02 6.036 5-(Hydroxymethyl)furfural 6.40 ± 4.01 4.93 ± 1.44 5.337 Vanillin 11.1 ± 11.9 7.84 ± 3.83 11.

n: Number of samples involved in the analysis from each region.a Paarl (P), Stellenbosch (S), Worcester (W), Robertson (RO), Olifants River (OR), and Sb Mean ± standard deviation.c Values in lg/l.

* Significance difference (p 6 0.05) amongst regions.

In a similar fashion, main effects ANOVA was also carried out forthe four red wine cultivars (PI, SH, CS, & M) of the same regions(Table 2). Here over one-third of the volatiles showed significantdifferences, including acetate esters (ethyl acetate, hexyl acetateand 2-phenethyl acetate), ethyl esters (ethyl butyrate, ethyl hexa-noate and ethyl octanoate), higher alcohols (isoamyl alcohol, 1-hexanol and 2-phenylethyl alcohol), acids (acetic acid, isobutyricacid, isovaleric acid and hexanoic acid) and 5-(hydroxymethyl)furfural.

One-way ANOVA was used to identify differences in volatilecontent between wines of different cultivars. Results for the meansamongst the six cultivars (two white and four red) showed signif-icant differences for all compounds, with the exception of n-buta-nol, n-butyric acid, acetoin, and furfural (results not shown). Inorder to obtain more meaningful information, one-way ANOVAwas performed for the white wines and red wines separately (Ta-ble 3). More than half the variables displayed significant differ-ences between the two white wine cultivars. Amongst the fuselalcohols, isoamyl alcohol concentrations were higher in SauvignonBlanc (SB) wines, whereas 1-propanol, isobutanol and n-butanollevels were higher in Chardonnay (CH) wines. According to Nykä-nen (1986), higher levels of isoamyl-alcohol are formed underanaerobic fermentation conditions, so that different fermentation

ines from the six different regions.

p-value

= 25) RO (n = 36) OR (n = 6) SW (n = 4)

5 ± 29.5 126 ± 39.0 122 ± 37.9 115 ± 34.2 0.1372 ± 0.120 0.473 ± 0.103 0.460 ± 0.153 0.462 ± 0.107 0.5188 ± 18.3 26.5 ± 28.7 26.8 ± 10.1 17.7 ± 13.1 0.7668 ± 14.1 25.7 ± 16.4 24.0 ± 5.79 28.3 ± 16.5 0.6391 ± 3.07 4.85 ± 2.86 5.76 ± 1.76 3.32 ± 4.22 0.5647 ± 3.65 6.95 ± 3.94 4.28 ± 3.13 7.84 ± 4.09 0.4823 ± 14.5 127 ± 12.7 124 ± 15.4 133 ± 29.2 0.1804 ± 0.131 0.613 ± 0.146 0.515 ± 0.155 521 ± 72.5c 0.2286 ± 33.5c 68.1 ± 31.1c 71.1 ± 27.4c 67.3 ± 39.5c 0.4507 ± 20.6 42.1 ± 20.9 35.4 ± 18.0 46.2 ± 18.0 0.5575 ± 50.3 17.7 ± 27.2 32.7 ± 33.6 12.6 ± 16.5 0.3618 ± 0.376 0.677 ± 0.355 0.476 ± 0.292 0.490 ± 0.586 0.3905 ± 18.1c 97.7 ± 24.3c 84.6 ± 27.9c 74.7 ± 22.8c 0.1482 ± 181 340 ± 270 375 ± 229 359 ± 156 0.006*

4 ± 5.92 13.6 ± 6.04 15.8 ± 5.80 11.7 ± 7.36 0.7588 ± 9.38 9.10 ± 6.59 14.3 ± 9.01 10.3 ± 1.34 0.2430 ± 0.955 1.31 ± 1.34 1.30 ± 0.483 0.808 ± 0.275 0.8509 ± 0.226 0.401 ± 0.258 0.533 ± 0.288 0.298 ± 0.289 0.2666 ± 1.56 3.10 ± 1.56 3.05 ± 1.40 3.95 ± 0.631 0.8651 ± 5.67c 9.49 ± 4.99c 10.9 ± 6.90c 6.99 ± 5.39c 0.8975 ± 0.300 1.48 ± 0.398 1.35 ± 0.147 1.44 ± 0.0780 0.9673 ± 0.585 4.53 ± 0.348 4.47 ± 0.321 4.53 ± 0.563 0.6784 ± 0.346 1.57 ± 0.402 1.49 ± 0.183 1.43 ± 0.111 0.3503 ± 0.191 0.287 ± 0.140 0.263 ± 0.120 0.464 ± 0.408 0.005*

9 ± 0.870 5.18 ± 1.29 4.73 ± 1.00 5.19 ± 1.57 0.6245 ± 0.160 0.238 ± 0.143 0.230 ± 0.128 0.171 ± 0.116 0.8627 ± 1.55 8.93 ± 2.56 7.74 ± 2.27 10.7 ± 4.34 0.6662 ± 56.6c 806 ± 52.2c 801 ± 78.2c 790 ± 28.0c 0.6391 ± 0.343 0.564 ± 0.404 0.463 ± 0.425 0.439 ± 0.373 0.6561 ± 8.44c 349 ± 7.01c 348 ± 7.71c 346 ± 5.00c 0.7503 ± 0.739 2.63 ± 0.857 2.47 ± 1.19 2.63 ± 0.826 0.8855 ± 39.1c 251 ± 45.1c 245 ± 42.2c 231 ± 25.9c 0.5073 ± 55.4c 590 ± 56.1c 584 ± 73.5c 570 ± 69.8c 0.7423 ± 0.133 0.775 ± 0.137 0.751 ± 0.121 721 ± 70.9c 0.8508 ± 2.70 4.61 ± 2.10 7.76 ± 8.12 7.21 ± 3.26 0.001*

2 ± 4.26 5.42 ± 3.61 6.67 ± 6.08 4.60 ± 1.91 0.4843 ± 5.40 7.03 ± 10.1 12.4 ± 16.8 8.17 ± 8.13 0.183

wartland (SW).

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Table 2Main effects ANOVA results for the mean values (mg/l) of volatile compounds for red wines from the six different regions.

No. Compounds Regionsa p-valueMean ± SD (mg/l)b

P (n = 50) S (n = 46) W (n = 58) RO (n = 41) OR (n = 15) SW (n = 14)

1 Ethyl acetate 142 ± 33.0 121 ± 37.6 126 ± 38.1 126 ± 38.8 116 ± 35.3 144 ± 40.1 0.015*

2 Ethyl butyrate 234 ± 61.2c 215 ± 60.8c 260. ± 81.7c 194 ± 51.1c 268 ± 59.1c 291 ± 74.6c 0.000*

3 1-Propanol 39.6 ± 33.4 34.7 ± 27.3 42.2 ± 21.0 31.5 ± 16.9 28.8 ± 10.7 43.2 ± 35.8 0.3514 Isobutanol 73.9 ± 26.5 77.4 ± 31.9 73.5 ± 24.7 88.9 ± 26.7 76.9 ± 25.8 63.3 ± 29.7 0.0915 Isoamyl acetate 2.20 ± 1.65 2.50 ± 2.07 2.48 ± 1.67 1.80 ± 1.06 2.46 ± 2.04 2.37 ± 1.90 0.5016 n-Butanol 6.80 ± 3.54 6.82 ± 3.96 7.74 ± 3.81 5.91 ± 4.28 7.02 ± 4.47 6.03 ± 3.40 0.3507 Isoamyl alcohol 194 ± 41.0 210 ± 38.5 202 ± 37.1 221 ± 42.4 211 ± 23.6 181 ± 27.8 0.009*

8 Ethyl hexanoate 144 ± 53.9c 128 ± 40.2c 0.217 ± 0.207 132 ± 50.3c 187 ± 56.4c 188 ± 58.0c 0.001*

9 Hexyl acetate 7.96 ± 5.04c 10.3 ± 8.15c 10.9 ± 7.32c 7.49 ± 3.52c 9.66 ± 4.62c 9.83 ± 3.41c 0.032*

10 Acetoin 45.6 ± 37.9 56.7 ± 43.1 41.7 ± 32.2 51.4 ± 35.0 31.6 ± 15.9 49.5 ± 25.5 0.12811 Ethyl-D-lactate 233 ± 79.1 230 ± 92.2 235 ± 76.6 204 ± 76.2 257 ± 69.3 261 ± 88.3 0.32012 1-Hexanol 0.609 ± 0.646 0.635 ± 0.353 0.864 ± 0.505 1.09 ± 0.799 0.629 ± 0.424 0.762 ± 0.848 0.002*

13 Ethyl octanoate 17.6 ± 8.39c 16.1 ± 7.59c 22.0 ± 13.5c 15.6 ± 7.48c 23.2 ± 11.2c 23.5 ± 9.42c 0.001*

14 Acetic acid 553 ± 283 555 ± 293 424 ± 301 585 ± 370 535 ± 244 608 ± 296 0.020*

15 Furfural 13.9 ± 5.21 13.3 ± 6.00 12.1 ± 6.70 10.6 ± 6.30 10.9 ± 6.44 12.5 ± 6.87 0.16016 Propionic acid 10.5 ± 8.19 12.9 ± 8.16 11.5 ± 8.08 9.90 ± 8.76 10.9 ± 5.61 13.2 ± 7.99 0.62517 Isobutyric acid 1.97 ± 1.21 2.20 ± 1.29 1.91 ± 1.12 3.06 ± 1.79 2.30 ± 1.20 1.67 ± 1.50 0.002*

18 5-Methylfurfural 0.443 ± 0.228 0.417 ± 0.229 0.339 ± 0.234 0.312 ± 0.232 0.390 ± 0.209 0.431 ± 0.238 0.06119 n-Butyric acid 3.19 ± 1.43 2.96 ± 1.55 2.87 ± 1.61 2.53 ± 1.46 2.92 ± 1.64 2.96 ± 1.64 0.56020 Ethyl decanoate 4.16 ± 1.99c 4.74 ± 2.20c 5.01 ± 2.23c 4.12 ± 2.76c 5.15 ± 2.98c 5.42 ± 2.22c 0.11021 Isovaleric acid 1.82 ± 0.308 1.91 ± 0.342 1.82 ± 0.306 2.10 ± 0.460 1.84 ± 0.215 1.69 ± 0.231 0.001*

22 Diethyl succinate 10.8 ± 2.38 10.3 ± 2.69 10.5 ± 2.87 10.2 ± 3.56 12.0 ± 3.16 11.6 ± 2.31 0.10123 n-Valeric acid 1.52 ± 0.151 1.54 ± 0.151 1.59 ± 0.238 1.56 ± 0.161 1.56 ± 0.159 1.55 ± 0.148 0.46024 2-Phenethyl acetate 156 ± 98.6c 0.286 ± 0.169 176 ± 95.9c 0.203 ± 0.140 180 ± 89.8c 177 ± 84.5c 0.000*

25 Hexanoic acid 2.95 ± 0.551 3.07 ± 0.579 3.28 ± 0.666 3.19 ± 0.701 3.32 ± 0.545 3.39 ± 0.664 0.028*

26 Guaiacol 0.339 ± 0.189 0.350 ± 0.201 0.321 ± 0.210 0.341 ± 0.205 0.271 ± 0.158 0.284 ± 0.233 0.46027 2-Phenylethyl alcohol 34.0 ± 20.1 38.9 ± 19.0 27.9 ± 16.5 43.7 ± 21.8 30.3 ± 13.8 27.8 ± 13.3 0.002*

28 o-Cresol 805 ± 42.4c 819 ± 46.6c 814 ± 45.1c 811 ± 49.0c 805 ± 34.1c 811 ± 48.0c 0.69829 Phenol 0.689 ± 0.413 0.745 ± 0.461 0.678 ± 0.466 0.763 ± 0.440 0.615 ± 0.291 0.645 ± 0.457 0.61430 4-Ethylguaiacol 352 ± 9.12c 353 ± 9.31c 0.351 ± 9.74c 352 ± 9.14c 353 ± 13.7c 349 ± 11.3c 0.76931 Octanoic acid 1.38 ± 0.228 1.49 ± 0.334 1.46 ± 0.371 1.38 ± 0.322 1.51 ± 0.276 1.56 ± 0.306 0.16732 p-Cresol 259 ± 33.8c 274 ± 43.4c 272 ± 39.5c 0.264 ± 34.7c 261 ± 28.0c 262 ± 39.3c 0.33833 Eugenol 603 ± 71.4c 612 ± 71.8c 599 ± 69.1c 606 ± 76.5c 586 ± 61.3c 577 ± 97.7c 0.45534 Decanoic acid 694 ± 59.5c 705 ± 86.7c 0.703 ± 0.111 697 ± 55.1c 708 ± 57.9c 713 ± 56.5c 0.91235 2,6-Dimethoxy phenol 6.23 ± 4.80 7.07 ± 5.39 6.14 ± 5.35 7.16 ± 4.27 8.02 ± 9.59 7.51 ± 6.19 0.61036 5-(Hydroxymethyl)furfural 1.95 ± 2.34 4.72 ± 4.59 4.35 ± 3.46 3.40 ± 2.57 4.55 ± 3.44 3.50 ± 2.36 0.000*

37 Vanillin 27.1 ± 13.5 33.4 ± 21.2 24.6 ± 19.8 33.5 ± 19.9 28.9 ± 16.2 30.1 ± 27.0 0.053

n: Number of samples involved in the analysis from each region.a Paarl (P), Stellenbosch (S), Worcester (W), Robertson (RO), Olifants River (OR), and Swartland (SW).b Mean ± standard deviation.c Values in lg/l.

* Significance difference (p 6 0.05) amongst regions.

B.T. Weldegergis et al. / Food Chemistry 128 (2011) 1100–1109 1103

practices between SB and CH may be responsible for these varia-tions. The average concentration of ethyl lactate was two timeshigher in CH than SB wines, an observation that is probably relatedto different mean levels of lactic acid between South African winesof these cultivars (de Villiers, Lynen, Crouch, & Sandra, 2003; Tre-doux et al., 2008). CH wines contained higher amounts of wood-derived compounds such as 4-ethyl guaiacol, eugenol, 2,6-dime-thoxy phenol and vanillin, although some discrepancies were ob-served. These variations could be related to the higher incidenceof wood maturation of CH compared to SB wines which is commonpractice in South Africa (Tredoux et al., 2008). On the contrary,mean levels of furan derivatives were observed to be higher inSB wines.

Except for four compounds (n-butanol, acetoin, n-butyric acid,and decanoic acid) all of the variables showed significant differ-ences amongst the four red wine cultivars (PI, SH, CS, and M).The first three compounds contain a common C4-skeleton with dif-ferent constituents, and are derived from pyruvate in the presenceof Acetyl CoA through different biosynthetic routes (Papoutsakis,1984). The absence of significant difference of these volatiles mightbe related to their similar formation and persistence in red wines.The observed significant differences with regard to the rest of thevolatiles amongst the red cultivars can primarily be attributed to

the Pinotage variety, as the mean levels of most volatile constitu-ents measured in this cultivar were higher. Most of the fermenta-tion products such as alcohols, esters and acids were present inhigher levels in Pinotage wines. The exception is isoamyl alcohol,where lower levels in this cultivar might be linked to the higher le-vel of isoamyl acetate, due to the esterification of the former com-pound (Cordente, Swiegers, Hegardt, & Pretorius, 2007). Thisobservation correlates with previous reports on the contributionof isoamyl acetate to the characteristic fruity character of youngPinotage wines developed during fermentation (van Wyk et al.,1979). In addition, the wood related compounds eugenol, 2,6-dimethoxy phenol, 5-hydroxymethyl furfural (5-HMF) and vanillinwere quantitatively higher in Pinotage wines. Volatile phenols likeeugenol are formed as by-products of lignin breakdown duringwood toasting (Arapitsas, Antonopoulos, Stefanou, & Dourtoglou,2004), implying higher incidence of wood contact for young Pino-tage wines.

3.3. Factor analysis (FA)

FA was performed following varimax normalisation of thequantitative volatile data. Based on the scree test method proposedby (Cattell, 1966), a total of four factors were selected as optimal

Page 5: Chemometric investigation of the volatile content of young South African wines

Table 3One-way ANOVA results for two white cultivars and four red cultivars obtained from volatile data (mg/l).

No. Compounds White cultivarsa p-value Red cultivarsc p-valueMean ± SD (mg/l)b Mean ± SD (mg/l)b

SB (n = 65) CH (n = 45) PI (n = 41) SH (n = 64) CS (n = 60) M (n = 59)

1 Ethyl acetate 113 ± 29.3 134 ± 34.9 0.001 146 ± 40.0 133 ± 32.7 106 ± 28.7 135 ± 39.4 0.0002 Ethyl butyrate 406 ± 92.4d 529 ± 89.7d 0.000 302 ± 88.7d 227 ± 53.0d 198 ± 57.3d 236 ± 56.1d 0.0003 1-Propanol 21.7 ± 10.7 34.8 ± 27.5 0.001 57.8 ± 33.5 39.5 ± 27.6 29.5 ± 15.9 28.5 ± 15.7 0.0004 Isobutanol 24.8 ± 12.5 28.0 ± 16.1 0.253* 56.0 ± 22.9 81.6 ± 24.3 83.0 ± 31.4 79.8 ± 24.8 0.0005 Isoamyl acetate 3.29 ± 3.36 5.71 ± 1.75 0.000 4.49 ± 2.33 2.07 ± 0.984 1.53 ± 1.16 1.76 ± 0.816 0.0006 n-Butanol 6.85 ± 3.73 7.32 ± 3.59 0.512* 7.59 ± 3.47 6.48 ± 4.41 6.40 ± 2.95 7.19 ± 4.42 0.356*

7 Isoamyl alcohol 130 ± 13.7 117 ± 11.8 0.000 160 ± 21.9 199 ± 32.7 223 ± 30.5 224 ± 36.4 0.0008 Ethyl hexanoate 0.559 ± 0.146 0.622 ± 0.125 0.021 208 ± 94.8d 140 ± 43.9d 172 ± 202d 148 ± 47.2d 0.0229 Hexyl acetate 86.8 ± 31.5d 64.2 ± 30.2d 0.000 15.7 ± 10.3d 9.87 ± 3.98d 7.50 ± 3.40d 6.17 ± 2.10d 0.000

10 Acetoin 38.4 ± 18.9 42.1 ± 19.1 0.309* 51.5 ± 40.9 47.4 ± 34.3 43.7 ± 30.1 47.7 ± 39.0 0.752*

11 Ethyl-D-lactate 15.1 ± 10.2 27.7 ± 45.8 0.034 280 ± 95.6 226 ± 79.8 214 ± 72.7 220 ± 68.9 0.00012 1-Hexanol 0.545 ± 0.352 0.618 ± 0.374 0.297* 0.613 ± 0.504 0.995 ± 0.795 0.808 ± 0.577 0.630 ± 0.413 0.00213 Ethyl octanoate 90.0 ± 25.5d 95.4 ± 19.0d 0.224* 30.2 ± 14.6d 15.0 ± 6.28d 16.0 ± 7.29d 18.0 ± 6.77d 0.00014 Acetic acid 280 ± 165 499 ± 247 0.000 744 ± 303 434 ± 224 448 ± 250 561 ± 369 0.00015 Furfural 13.0 ± 6.19 12.9 ± 5.81 0.958* 14.6 ± 6.89 12.4 ± 4.01 12.3 ± 5.94 11.1 ± 7.60 0.04716 Propionic acid 14.8 ± 7.42 7.77 ± 5.74 0.000 17.4 ± 8.53 8.69 ± 5.37 6.92 ± 4.62 14.5 ± 9.22 0.00017 Isobutyric acid 1.41 ± 1.36 1.07 ± 0.692 0.124* 1.56 ± 0.653 2.11 ± 1.30 2.40 ± 1.72 2.55 ± 1.40 0.00318 5-Methylfurfural 0.366 ± 0.241 0.427 ± 0.232 0.191* 0.424 ± 0.260 0.454 ± 0.192 0.352 ± 0.240 0.307 ± 0.225 0.00219 n-Butyric acid 3.25 ± 1.54 3.26 ± 1.45 0.977* 3.03 ± 1.77 3.00 ± 1.02 2.96 ± 1.58 2.65 ± 1.77 0.530*

20 Ethyl decanoate 11.5 ± 7.10d 8.34 ± 4.77d 0.010 5.60 ± 2.29d 3.52 ± 1.65d 5.23 ± 2.59d 4.57 ± 2.33d 0.00021 Isovaleric acid 1.46 ± 0.408 1.48 ± 0.446 0.806* 1.59 ± 0.173 1.80 ± 0.288 2.00 ± 0.307 2.07 ± 0.397 0.00022 Diethyl succinate 4.43 ± 0.318 4.74 ± 0.470 0.000 9.48 ± 2.40 10.5 ± 2.23 12.0 ± 3.71 10.1 ± 2.29 0.00023 n-Valeric acid 1.57 ± 0.439 1.52 ± 0.313 0.554* 1.53 ± 0.121 1.51 ± 0.192 1.55 ± 0.218 1.62 ± 0.138 0.00724 2-Phenethyl acetate 0.375 ± 0.188 0.353 ± 0.212 0.565* 203 ± 130d 204 ± 0.135d 232 ± 155d 159 ± 79.5d 0.02125 Hexanoic acid 4.84 ± 1.04 5.03 ± 1.22 0.378* 3.42 ± 0.536 2.89 ± 0.528 3.05 ± 0.645 3.38 ± 0.654 0.00026 Guaiacol 0.204 ± 0.122 0.285 ± 0.145 0.002 0.405 ± 0.223 0.241 ± 0.155 0.253 ± 0.166 0.448 ± 0.181 0.00027 2-Phenylethyl alcohol 8.72 ± 2.92 8.84 ± 2.32 0.817* 15.9 ± 6.34 30.8 ± 17.3 44.5 ± 18.3 41.5 ± 18.1 0.00028 o-Cresol 807 ± 60.8d 806 ± 45.8d 0.975* 835 ± 50.2d 792 ± 40.9d 800 ± 37.8d 829 ± 38.0d 0.00029 Phenol 0.554 ± 0.387 0.566 ± 0.376 0.873* 0.860 ± 0.522 0.596 ± 0.340 0.555 ± 0.351 0.862 ± 0.463 0.00030 4-Ethylguaiacol 347 ± 6.94d 351 ± 7.27d 0.003 357 ± 10.1d 346 ± 8.69d 350 ± 9.57d 356 ± 7.21d 0.00031 Octanoic acid 2.78 ± 0.858 2.27 ± 0.675 0.001 1.59 ± 0.348 1.33 ± 0.276 1.50 ± 0.368 1.40 ± 0.231 0.00032 p-Cresol 242 ± 43.0d 257 ± 38.1d 0.064* 282 ± 46.0d 253 ± 37.4d 255 ± 26.9d 283 ± 30.6d 0.00033 Eugenol 573 ± 46.9d 616 ± 58.2d 0.000 629 ± 83.0d 569 ± 61.9d 581 ± 66.0d 638 ± 59.5d 0.00034 Decanoic acid 819 ± 145d 733 ± 96.1d 0.001 698 ± 76.1d 697 ± 75.6d 721 ± 97.2d 687 ± 64.6d 0.119⁄35 2,6-Dimethoxy phenol 4.04 ± 1.87 6.16 ± 3.76 0.000 10.1 ± 7.75 5.36 ± 2.96 5.49 ± 3.14 7.17 ± 6.47 0.00036 5-(Hydroxymethyl)furfural 6.39 ± 3.92 4.35 ± 3.01 0.004 5.70 ± 5.17 4.32 ± 3.44 2.16 ± 1.79 3.11 ± 2.22 0.00037 Vanillin 5.99 ± 5.12 14.0 ± 11.8 0.000 37.4 ± 20.5 25.4 ± 13.5 22.0 ± 16.0 35.1 ± 23.2 0.000

n: Number of samples involved in the analysis.a Sauvignon blanc (SB) and Chardonnay (CH).b Mean ± standard deviation.c Pinotage (PI), Shiraz (SH), Cabernet Sauvignon (CS) and Merlot (M).d Measurement in lg/l.

* No significant difference (p > 0.05) between cultivars.

1104 B.T. Weldegergis et al. / Food Chemistry 128 (2011) 1100–1109

for explaining the total variability in the volatile data. In addition, aparallel analysis, where simulated and re-sampled data are plottedwith the actual data, was also used to identify the important fac-tors (O’Connor, 2000). Based on these two approaches, four factorswere selected which explained 58% of the total variation in thedata. During successive steps of FA, several loadings were elimi-nated because they did not contribute to a simple factor structureand failed to meet a minimum criteria of an absolute loading valueof 0.3 or above.

The first factor explains the highest percentage of the variabilityin the data set. More than half the variables were associated withFactor 1 (F1). These compounds were mainly volatile fermentationproducts, including some compounds potentially responsible orvarietal aroma like isoamyl acetate. Some compounds associatedwith wood ageing also showed relatively high loadings on this fac-tor (5-HMF and vanillin), although lower than the alcohols and es-ters. Ethyl and acetate esters, partially responsible for the fruityflavour of wines (Saerens et al., 2008), showed high negative corre-lation with F1. On the contrary, the organic acid-derived esters(ethyl lactate and diethyl succinate) as well as fusel alcohols (iso-butanol, isoamyl alcohol, and 2-phenylethyl alcohol) displayedhigh positive correlation with F1. The branched aliphatic acids

(isobutyric and isovaleric acids) showed positive correlation withF1, while C6, C8, and C10 straight chain acids were negatively corre-lated to this factor. F1 therefore seems to describe primarily theinfluence of fermentation on the levels of volatile compounds stud-ied here.

Factor 2 (F2) was associated with compounds that are releasedfrom wood during fermentation or maturation in barrels (Jarautaet al., 2005), including the volatile phenols (guaiacol, o-cresol, phe-nol, 4-ethyl guaiacol, p-cresol, eugenol, and 2,6-dimetoxy phenol),vanillin and 5-hydroxymethyl furfural. All these compounds pre-sented high positive loadings on F2. C2, C3, and C5 straight chainaliphatic acids and acetoin, both formed during fermentation, werealso positively associated with this factor. F2 therefore seems tomainly describe the influence of wood contact on the major vola-tile composition.

Most acids (C4–C10) as well as 2-phenylethyl acetate and 2-phenylethyl alcohol were positively associated with F3. Ethyl ace-tate and 1-propanol showed negative correlation with F3. Furanderived compounds such as furfural, 5-methylfurfural, and 5-hydroxymethyl furfural were negatively correlated with Factor 4,whereas isoamyl alcohol and 2-phenylethyl alcohol showed posi-tive correlation with this factor. Note that cross loading of some

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B.T. Weldegergis et al. / Food Chemistry 128 (2011) 1100–1109 1105

variables (loading of a variable in more than one factor) was ob-served. For instance, the branched C4 and C5 acids displayed posi-tive association with both F1 and F3, whereas C6, C8, and C10 acidsshowed negative correlation with F1 and positive correlation withF3. Similar trends were observed for other volatiles as well.

In conclusion, FA indicates that the principal variation in majorvolatile composition appear to be due to fermentation practicesand wood ageing. This seems reasonable, considering the originof most of these compounds, and indeed is in agreement withthe previous report dealing with major volatile composition of asmaller number of South African wines of different vintages (Tre-doux et al., 2008).

3.4. Principal component analysis (PCA)

Visualisation of PCA results may be performed as a score plot ofthe cases (samples) or a loading plot of the variables (volatile com-pounds). Both scores and loadings may be visualised together in aPCA bi-plot. In this form of presentation, multidimensional obser-vations are displayed by points in the two-dimensional space byinterpolation, whereas variables are represented as bi-plot axes,with a separate axis for each variable. A PCA bi-plot constructedusing the measured values of the different variables in 334 SouthAfrican young wines of vintage 2005 is shown in Fig. 1.

Only variables with R2 > 0.5 are considered as correctly mea-sured in the bi-plot. Thus, 15 of the 37 variables are representedin Fig. 1, each with its own independent axis. The calibration ofthese axes in the scale of the original measurements (i.e. mg/l)allows the graphical determination for any data point of valuesfor each variable, by its projection onto the different axes. Thewhite wine cultivars are clearly grouped on the right-hand sideof the PCA bi-plot, and are separated from the red wine cultivarson the left-hand side. Slight separation between the two white cul-tivars is observed in this figure. A similar but more pronouncedseparation of Pinotage wines from the other red cultivars is alsoevident, which is in agreement with the ANOVA results presentedin Table 3.

In a PCA bi-plot, the correlation of variables with objects (casesor groupings), and between variables themselves, depends on

Dimensio

Gua

iaco

lp-

Cre

sol

4-E

thyl

guai

acol

Eug

enol

o-C

reso

l

Pro

pion

icac

id

Isobutanol

Isoamyl alcohol

Vanillin

Dim

ensi

on

2 (

34%

)

Fig. 1. Cultivar based PCA bi-plot constructed using volatile data with interpolated mChardonnay (CH), Pinotage (PI), Shiraz (SH), Cabernet Sauvignon (CS), and Merlot (M)).overlap amongst/between groups. The variables used in the construction of the bi-plot

many factors. PCA bi-plots may be interpreted using different ap-proaches, including trends in the magnitude of the variables, an-gles and distances between variables as well as the distancebetween the data points. In this report only the former approachis considered to elucidate patterns as the angles and distances be-tween variables and the data points were not measured.

Variables with axes in close proximity are highly correlated. Thedegree of their correlation mainly depends on the size of the anglebetween the axes (i.e. the smaller the angle the higher the magni-tude of the correlation): when two axes are perpendicular, theircorrelation is zero. The correlation could be negative or positive.For instance, ethyl esters of C4, C6, and C8 acids and hexyl acetateare highly positively correlated. Isoamyl alcohol is negatively cor-related with these esters and positively correlated with isobutanol.C6 and C8 acids showed high positive association with each otherand negative correlation with branched C4 and C5 alcohols. It is alsoapparent from Fig. 1 that ethyl esters of C4, C6, and C8 acids, as wellas C6 and C8 acids, showed positive correlation to a varying degree.These compounds were found to exist at higher concentrations inthe white wines. On the contrary, the levels of higher alcohols (iso-butanol and isoamyl alcohol) were higher in red wines. The pres-ence of insoluble solids and suspended particles in the must arebelieved to increase the formation of higher alcohols (Klingshirn,Liu, & Gallander, 1987). Reduced concentrations of higher alcoholsin white wine have therefore been associated with limitedamounts of insoluble solids in the must (Karagiannis & Lanaridis,2002). Volatile phenols, which are believed to be produced duringwood maturation (Pollnitz, Pardon, Sykes, & Sefton, 2004), seem tobe present at slightly higher concentrations in red wines. In partic-ular, volatile phenol levels were higher in Pinotage wines, com-pared to the other red cultivars studied. This observation couldbe related to higher incidence of wood maturation for the Pinotagewines analysed here, as outlined previously. The volatile phenolsguaiacol, o-cresol, 4-ethylguaiacol, p-cresol and eugenol showedpositive correlation amongst each other.

It therefore seems that most of the correlation between vari-ables in Fig. 1 is related to the differences between the red andwhite wines, as evidenced by the principle grouping of winesamples.

n 1 (41%)

Ethyl butyrateHexyl acetateEthyl hexanoateEthyl octanoate

Octanoic acid

Hexanoic acid

SB

CH

PI

SH

CS

M

ultidimensional target of wines from different cultivars (Sauvignon Blanc (SB),a-Bags are used to characterise the probability cloud and to measure the degree ofare each presented by independent axis.

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1106 B.T. Weldegergis et al. / Food Chemistry 128 (2011) 1100–1109

3.5. Linear discriminant analysis (LDA)

In the current work, linear discriminant analysis (LDA) was usedto determine which volatile components could be used to differen-tiate young South African wines according to cultivar, consideringthat only slight differences in volatile composition were observedas a function of geographical origin. Prediction studies were per-formed between white and red wines, between the two white cul-tivars, and amongst the red cultivars by random division of thedata into a training set (70% of samples) and a test set (30% of sam-ples). A best subsets analysis approach was adopted where LDAmodels were fitted (on the training data) for all subsets of predic-tors or variables. Wilk’s lambda was used to select the 10 bestmodels. The reasoning behind selecting 10 models is that thosevariables which tend to be included repeatedly in the models withhigh accuracy may be determined. In this manner LDA was used toobtain information about which variables may be deemed as goodpredictors of cultivar, rather than for the purpose of obtaining asingle prediction model. The best models were then evaluatedusing the test set to see how well the models predicted test data.Hence, the results obtained in this section were used to reviewthe linear discriminant model as a predictive tool, and to expandon the interpretation of the predictive analysis in order to identify

Table 4Summary of linear discriminant analysis (LDA) results obtained using volatile data.

Modela Variables includedb White wine prediction (%)

Training set (n = 79)

White vs. red wine prediction1 4, 9, 11, 13 & 22 98.72 4, 8, 9, 11 & 22 98.73 9, 11, 13, 20 & 22 98.74 7, 9, 11, 13 & 22 98.75 9, 11, 13, 15 & 22 98.76 2, 9, 11, 13 & 22 1007 9, 11, 13, 22 & 37 98.78 4, 11, 13, 22 & 37 1009 8, 9, 11, 13 & 22 98.7

10 4, 11, 13, 22 & 32 100

SB wines prediction (%)

Training set (n = 46)

White wine cultivar prediction1 2, 16, 28, 34 & 37 1002 14, 16, 28, 34 & 37 97.83 1, 14, 16, 28 & 37 1004 2, 3, 16, 28 & 37 97.85 2, 14, 16, 28 & 37 1006 2, 14, 16, 28 & 34 1007 2, 16, 22, 28 & 37 97.88 16, 22, 28, 34 & 37 1009 2, 16, 18, 28 & 37 97.8

10 1, 16, 22, 28 & 37 100

Predicted as Pinotage (%)

Training set (n = 31)

Pinotage prediction1 5, 7, 13, 14 & 22 87.12 5, 7, 13, 16 & 22 87.13 5, 7, 13, 22 & 28 87.14 5, 7, 13, 22 & 35 87.15 5, 7, 10, 22 & 28 83.96 5, 7, 10, 13 & 14 80.77 5, 7, 10, 13 & 16 83.98 5, 7, 13, 22 & 30 87.19 5, 7, 13, 15 & 22 87.1

10 5, 7, 22, 26 & 30 83.9

n: number of samples involved in the test.a Numbers correspond to each of the 10 models used during LDA analysis.b Variables included in each model, numbers correspond to Tables 1–3.

the classification of each group (i.e. cultivar). A summary of the re-sults obtained by LDA is presented in Table 4.

An initial prediction study between red and white wines wasbased on the 10 best models. Only variables that showed the high-est selection rate (variables with correct prediction values P90%and selected in most of the models) were used. A total of twelvecompounds with high percentage prediction were observed (datanot shown). Only four of these variables (hexyl acetate, ethyl octa-noate, ethyl-D-lactate, and diethyl succinate) were selected as pre-dictors, since these variables were picked in most of the models. Asmentioned above, the former two compounds were present inhigher concentrations in white wines, while the levels of the lattertwo compounds were lower in white wines. These four esters cor-rectly predicted 100% of the wine samples in all the models, withthe exception of the second model where 99% of the red wineswere predicted correctly (Table 4).

The high selection rate of these esters is an indication that thesecompounds can be used as predictors for differentiation betweenwhite and red wines. The higher levels of ethyl lactate in red winescould be due to longer skin contact (Cabaroglu et al., 1997). Inaddition, the higher levels of ethyl lactate can presumably be as-cribed to higher incidence of malolactic fermentation (Tredouxet al., 2008). The observation could also arise from the

Red wine prediction (%)

Test set (n = 31) Training set (n = 155) Test set (n = 69)

100 100 100100 99.4 98.6100 99.4 100100 100 100100 100 100100 100 100100 100 100100 100 100100 100 100100 100 100

CH wines prediction (%)

Test set (n = 19) Training set (n = 33) Test set (n = 12)

100 93.9 91.7100 93.9 100100 93.9 100100 93.9 91.7100 90.9 91.7100 97.0 100100 97.0 83.3100 93.9 100100 93.9 83.3100 93.9 75.0

Predicted as non-Pinotage (%)

Test set (n = 10) Training set (n = 124) Test set (n = 59)

90.0 96.0 98.380.0 96.0 10080.0 95.2 96.690.0 95.2 96.690.0 96.8 94.990.0 93.6 94.990.0 94.4 93.280.0 96.0 96.690.0 96.0 96.690.0 95.2 94.9

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transformation of lactic and succinic acids to form ethyl lactate anddiethyl succinate during fermentation and maturation, as red winecontains on average higher amounts of these acids (de Villierset al., 2003).

While the successful classification of red and white wines wasobtained using this approach, this is of limited practical relevance.Therefore, a detailed classification of the Sauvignon Blanc (SB) andChardonnay (CH) using the same methodology was performed (Ta-ble 4). A total of eight compounds showed more than 90% correctprediction for these two white cultivars, although only four (aceticacid, propanoic acid, o-cresol, and vanillin) were selected as poten-tial predictors of white wine cultivar as they were repeatedly usedin most of the models and showed significant contribution towardsthe classification with R2 > 0.5. To evaluate the classification of SBand CH, a PCA bi-plot was constructed using these four variables.It is clear from Fig. 2A that the two white cultivars are relativelywell separated. The higher levels of propanoic acid and o-cresolin SB wines seem to play a prominent role in this separation. Sim-ilarly, the concentrations of vanillin and acetic acid were higher inCH wines. The higher levels of vanillin in Chardonnay could be re-lated to the common practice of producing wooded wines of thiscultivar in South Africa (Tredoux et al., 2008).

Using a similar approach, linear discriminant analysis (LDA)was performed for the four red wine cultivars Pinotage (PI), Shiraz(SH), Cabernet Sauvignon (CS), and Merlot (M). Twelve compoundsappeared to be predictors for these cultivars, five of which were se-lected repeatedly in different models (data not shown). However,

Dimension 1 (63%)

Dim

ensi

on 2

(25%

)

Vanillin Acetic acid

Propionic acid o-CresolSB CH

(A)

Dimension 1 (61%)

Isoamyl acetate

Dim

ensi

on 2

(25%

)

Ethyl octanoate

Isoamyl alcohol

(B)

PI SHCS M

Fig. 2. PCA bi-plots constructed using the predicting variables identified by lineardiscriminant analysis for: (A) the two white cultivars Sauvignon Blanc (SB) andChardonnay (CH); (B) Pinotage (PI) wines. Other red cultivars included in (B) areShiraz (SH), Cabernet Sauvignon (CS), and Merlot (M).

with the exception of Pinotage, the correct prediction percentagesfor the other red cultivars were very low (SH = 46.7–71.4%,CS = 35.3–76.5% and M = 38.5–61.9%). As a result further analysisfocused on differentiation of Pinotage wines from the other threered wine cultivars.

A well-defined grouping of Pinotage wines observed in Fig. 1was an indication of the unique volatile composition in youngwines of this cultivar. In the 10 models developed for the LDA ofPinotage, 12 variables were observed to have high prediction value(Table 4). Four of these (isoamyl acetate, isoamyl alcohol, ethyloctanoate, and diethyl succinate) demonstrated a high selectionrate per model. However, only the former three showed a signifi-cant contribution to wards the classification of Pinotage samples(R2 > 0.5), and were therefore considered as variables with high po-tential in predicting of this cultivar. A multidimensional extrapola-tion of these three variables was constructed as a PCA bi-plot(Fig. 2B) in order to identify the significance of these compoundsin differentiating Pinotage wines. It is evident that Pinotage winesare separated from the rest of the studied red cultivars using theidentified variables as predictors. Levels of isoamyl acetate andethyl octanoate in particular are higher in Pinotage, while isoamylalcohol levels are lower for wines of this cultivar, which is in agree-ment with the previous report (Tredoux et al., 2008; Weldegergis,Tredoux, & Crouch, 2007).

The importance of esters in the differentiation of Pinotagewines is in accordance with the fruity character of this cultivar:according to van Wyk et al. (1979), young Pinotage wines are char-acterised by higher amounts of esters. In the same report, it wasalso indicated that isoamyl acetate may be an important varietalcompound for Pinotage wine. Young Pinotage wines are character-ised by a fruity bouquet, which is absent from either must or grape(van Wyk et al., 1979). This suggests that this particular aromacharacteristic is produced during fermentation.

A comprehensive study of the factors affecting the characteris-tic young Pinotage flavour (Joubert, 1980), which, to the best of ourknowledge, has not been published elsewhere, implicates the high-er levels of free amino content in Pinotage musts as the most influ-ential parameter in this regard. This work included sensoryevaluation of the characteristic young Pinotage flavour with quan-titative analysis of the major volatiles by GC, and supported theimportance of isoamyl acetate as a principle contributor to this fla-vour. Factors such as soil type, geographical origin, rootstock andmetal content of the must were shown not to contribute to thecharacteristic young Pinotage aroma, or indeed affect the levelsof isoamyl acetate significantly in the studied wines. In contrast,factor such as harvest date, must pH (lower pH is more favourable),skin contact and fermentation temperature (increases in both arefavourable) and yeast strain were found to be influential in the for-mation of the Pinotage aroma (although the effect of yeast strainwas found to be of secondary importance to the must composi-tion). The general conclusion of this study was that higher levelsof free amino content in the must, which was partially due to high-er levels of amino acids, led to higher yeast activity and the fasteronset of anaerobic fermentation conditions. This in turn leads tothe production of higher levels of saturated fatty acids in yeast cellmembranes. Taken together, these factors were concluded to in-crease the likelihood of ester formation, presumably via catalysisof alcohol acetyltransferase (Cordente et al., 2007). Particularly inthe case of isoamyl acetate, the result is the presence of this com-pound at levels above its odour threshold in red wines (Joubert,1980).

Our quantitative results are in agreement with these finding,particularly the higher levels of isoamyl acetate and ethyl octano-ate, and lower levels of isoamyl alcohol, which are shown to beimportant distinguishing characteristics for young Pinotage winesfrom other red varieties (Fig. 2B). Moreover, the results of Joubert

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1108 B.T. Weldegergis et al. / Food Chemistry 128 (2011) 1100–1109

(1980) are supported by evidence that certain yeast strains havethe ability to promote the formation of isoamyl acetate (Yilmazte-kin, Erten, & Cabaroglu, 2008). Higher alcohols are formed by yeast,either directly from sugars or from grape amino acids, which in-volves the formation of keto-acidic and carbonyls as intermediatesby removal of ammonia and carbon dioxide, respectively, prior tothe formation of alcohols via reduction. Through this pathway leu-cine produces isoamyl alcohol which is then converted to isoamylacetate. Indeed, higher levels of leucine were measured in Pinotagemusts by Joubert (1980). Moreover, higher levels of ammonia weremeasured in Pinotage musts compared to other South Africangrape varieties (Ough & Kriel, 1985), while Ough & Lee (1981) mea-sured higher levels of isoamyl acetate in wines produced frommust with higher total nitrogen content. It has also been shownthat factors that increase the fermentation rate (yeast biomass,oxygenation, high temperature, and suspended particles) increasethe fermentation of higher alcohols (Lilly, Bauer, Styger, Lamb-rechts, & Pretorius, 2006).

A further potential contributory factor to the higher isoamylacetate levels in Pintage wines might be the characteristically highconcentrations of caffeic acid in these wines (de Villiers et al.,2005): in the presence of N-acetyl-cystein, caffeic acid has beenshown to inhibit the decrease of esters during storage (Roussis,Lambropoulos, & Papadopoulou, 2005), although this is expectedto play a minor role in young wines analysed here.

It is known that the typical Pinotage fermentation bouquet de-creases with ageing, and unless the wine is stored at low temper-atures, this bouquet usually disappears after two or more years(van Wyk et al., 1979). Wines stored in wooden containers tendto lose the typical bouquet faster than equivalent wines stored insteel tanks. The typical Pinotage fermentation bouquet wouldtherefore be retained longer if wines are not aged in small cooper-age, are bottled relatively young and stored under cool conditions.On the other hand, a slightly different style of Pinotage wine with-out the typical fermentation bouquet will be obtained by ageingsuch a wine in small cooperage or even large cooperage over a rel-atively long period of time.

It is clear from the precedent discussion that the impressive dif-ferentiation of Pinotage wines from other red wine varieties evi-dent from Fig. 2B would not be as significant if older wines wereincluded in the study, largely due to the fact that isoamyl acetateand ethyl octanoate would be expected to play less discriminatoryroles for such wines. Nevertheless, the importance of especiallyisoamyl acetate in differentiation of young Pinotage wines is onceagain highlighted by our results.

It is also worth noting that a similar tendency for high isoamylacetate levels in Pinot noir wines has been reported (Girard, Kopp,Reynolds, & Cliff, 1997). Pinotage was cross-bred from Pinot noirand Cinsaut (Hermitage) varieties, which indicates that the charac-teristics of the former variety may be related to those observed inPinotage wines.

4. Concluding remarks

Chemometric techniques were used to study the variation inmajor volatile content in a large number of young South Africanwines. ANOVA results indicated significant differences in volatilecontent between different cultivars, especially between Pinotagewines and the other varieties. Significance differences betweenthe two white wine cultivars were also evident. Factor analysis(FA) showed that fermentation practices and wood ageing wereprimarily responsible for the variation in volatile content of theinvestigated wines. PCA bi-plots proved especially valuable inrelating the different variables responsible for differentiation be-tween wine samples. Finally, LDA was used to identify prominent

variables useful in the prediction of wine cultivar. The integrationof LDA with PCA bi-plot presentations was used to study the con-tribution of a small number of variables to differentiation of SouthAfrican wines of different cultivar. Generally speaking, the resultsof the statistical approaches were complementary. They illustratedthe differences between the red and white wines, between the twowhite cultivars, and highlighted the unique character of Pinotagewines in relation to other red wine cultivars in terms of the volatilecompounds studied. Isoamyl acetate and ethyl octanoate wereseen as influential in this latter differentiation, and the most likelyreasons for this phenomenon have been highlighted.

Acknowledgments

The authors thank the South African Young Wine Show for pro-viding the wine samples; the Institute for Wine BioTechnology(Stellenbosch University) for storing the wines; WineTech andStellenbosch University for financial support of the project, andTesfamariam Hagos for his contribution in data analysis. Theauthors also gratefully acknowledge the contribution of Prof. M.Kidd, Stellenbosch University (Statistics and Actuarial Science,Centre for Statistical Consultation) for the statistical analysis andediting suggestions.

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