GOMES NETO, H.

HALLWASS, F.

PEREIRA, G. E.

SILVA, J. B. P. da

English

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USE OF CHEMOMETRICS TO CHARACTERIZE TROPICAL WINES FROMDIFFERENT VINTAGES AND GRAPE CULTIV ARS ACCORDING TO THE IH MRSPECTROSCOPY DATAHumberto GOMES NET01; Fernando HALLWASS2; Joao Bosco Paraiso da SILVA3;Giuliano Elias PEREIRA4,*IMaster snldent, Department of Chemistry, Federal University of Pernambuco, Av. Luiz Freire sin, 50.740-540, Recife-PE, Brazil.E-mail: hgsnguimica@gmail.com;2Department of Chemistry, Federal University of Pernambuco, Av. Luiz Freire sin, 50.740-540, Recife-PE, Brazil.E-mail: hallwass@ufpe.br;2Department of Chemistry, Federal University of Pernambuco, Av. Luiz Freire sin, 50.740-540, Recife-PE, Brazil.E-mail: paraiso@ufpe.br;4,'EnologyResearcher, Brazilian Agricultural Research Corporation - Embrapa Grape & Wine/Tropical Semiarid, P.O. Box 23, 56.302-970,Petrolina-PE, Brazil. E-mail: gpereira@cpatsa.embrapa.br.SummaryTropical wines have been produced in ortheast of Brazilsince 1980's, between the 8° and 9° S latitude, in a region calledSub-middle Siio Francisco river Valley. This area presents anintra-annual climate variability and wines can be elaborated indifferent months of the year, according to the winery, withdifferent analytical characteristics due to the climatic conditions.NMR spectroscopy is an interesting tool that allows to determineina single analysis many analytical compounds of the wines. PCAand PLS multivariate statistical analyses applied on NMR dataallow to discriminate samples and to identify the responsiblecompounds for the clustering. The aim of this work was to usechemometrics, PCA and PLS, applied on IH NMR spectroscopydata, to characterize tropical wines from different vintages andgrape cultivars, in Northeast of Brazil. Wines were elaborated byusing traditional winemaking process with control of thefermentations temperature and use of antioxidants. Beforestatistical analyses, IH NMR spectra were segmented, normalized,converted to Excel software format and further processed for PCAand PLS analyses. Statistical analyses applied on NMR spectradata were not satisfactory to discriminate between differentvintages of white and red wines together, but they were able toseparate each one according to different vintages and cultivars.Metabolic compounds were found to explain wine clusters, andfingerprints are discussed.Traditional winegrowing areas are located in temperateclimate conditions, where it is possible to have one harvestper year (Reynier, 2007). Recently, tropical wines havebeen produced in some countries, as India, Thailand,Venezuela and Brazil. In these conditions, it is possible tohave two or three harvests per year, according to the geneticand productive cycle of different cultivars. In Brazil, themain characteristics allowing vines a continuous vegetativedevelopment, and then grapevine cropping throughout theyear, are an annual average temperature of 26.4 °C (21.0 °Cminimum and 31.7 °C maximum), high luminosity (about3000 hours of luminosity year'l) and water available forirrigation; wines can present different characteristics due tothe intra-annual climatic variability (Tonietto and Teixeira,2004; Teixeira, 200 I; Teixeira and Azevedo, 1996).Proton nuclear magnetic resonance spectroscopy('H NMR) is a powerfi.d and very useful tool that allows todetermine metabolic profiling and/or metabolitefingerprinting of fruits, olive oil, beer, grapes and wines(Pereira et of., 2007; Krishnan et of., 2005; Pereira et of.,2005; Brescia et 01., 2002; Fan, 1996). The use ofchemometric methods on NMR data, as principalcomponent analysis (PCA) and partial least squares (PLS),allows to separate samples identifying analyticalcompounds responsible for the clustering (Pereira et of.,2007; Kemsley, 1998). The aim of this paper was to usechemometrics to separate wine samples and to identifyanalytical markers of tropical wines elaborated in differentvintages and grape cultivars in ortheast of Brazil, bymeans of data obtained by 1D IH MR spectroscopy.Wines were elaborated in stainless steel tanks of 200 Lfrom seven white and five red grape cultivars harvested indifferent months between 2005 and 2008. It was used thetraditional winemaking process, with control of thefermentations temperature for red (25°C for alcoholic and18°C for malolactic fermentation) and white wines (18 °Cfor alcoholic fermentation, only) and use of antioxidant toincrease wine quality and stability (Peynaud, 1997). Afterstabilization, wines were bottled and then analyzed by usinga Varian 300 MHz NMR spectrometer to determine onedimension IH NMR spectra of each wine sample analyzedin triplicate. Spectra were acquired according to ourprevious work and each spectrum was obtained in about 15minutes (Pereira et of., 2007). The samples were: i) whitewines: Schomburger from ovember 2005 (ScOS);Schomburger from September 2006 (Sc06); Regner fromMay 2005 (ReOS); Viognier from December 2006 (Vi06);Flora from December 2005 (F10S); Sauvignon blanc fromNovember 2008 (Sb08); and Colombard from December2005 (CoOS); ii) red wines: Alfrocheiro from June 2006(AI06); Tempranillo from February 2007 (Te07);Trincadeira from February 2007 (Tr07); Periquita fromFebruary 2007 (Pe07); and Petit Verdot from July 2007(PY07). Before statistical analyses, IH NMR spectra weresegmented into 100 spectral domains (variables) of 0.04ppm between 0.54 and 5.99 ppm, normalized to the totalspectral intensity, then converted to Excel software formatand further processed with Win-Das software for PCA andPLS analyses.RESUL TS AND DISCUSSIONStatistical analyses obtained of36 IH NMR spectraof red and white wine samples from different vintagestogether were not efficient to discriminate clusters (data notshown). However, multivariate statistical analyses wereable to discriminate between wine samples from differentgrape cultivars. Figure 1 shows PCl x PC2 plot obtainedfrom 21 IH NMR spectra of seven white wines analyzed.Wines of the grape cultivar Schomblirger elaborated in twovintages (November 2005 and September 2006) presentedsimilarities and are located in the positive side of PC1.Even if climatic conditions were different in the two years,wines were similar, and in this case the most importantfactor was genetic. The chemical shifts-characterizing thesesamples were not identified (5.33 and 5.99 ppm), but it canbe suggested some aromatic compounds (Fan, 1996). In thepositive side ofPC2 are located wine samples of Sauvignonblanc (Sb08), characterized by the shifts 3.68 ppm,identified as glycerol, 4.83 ppm (residual water) and 4.06ppm (unknown). In the negative side of the axes PCl andPC2 are located the wine samples of Regner (Re06), andchemical shifts responsible for discrimination were 2.69ppm, identified as pyruvic and succinic acids, 4.56 ppm,identified as tartaric acid, 0.87 ppm (unknown) and 3.51ppm (glycerol). According to previous works, organic acidsand glycerol can be considered as important discriminatingfactors to separate wines, explained by genetic influenceand cultivar adaptation to the edafo-climatic conditions ofthe region (Silva Neto et aI., 2009; Pereira et aI., 2007;Reynier, 2007).;;~NUQ.-15 -10~• Se05/'; o Se06/'; • ReD6DViOGAFIOS0 XX21 0 l>SbOBXCo05oi-~ --5 0 5 • 10-2 • 0C()-4-6-8PC130,9%-10Figure 1. PCA scores on the 100 buckets of the lD 'H NMRspectra from seven tropical white wines with triplicates. ThePCIIPC2 plot explained 49.7% of the total variance, where: Sc05:Schomburger 2005; Sc06: Schomburger 2006; Re06: Regner2006; Vi06: Viognier 2006; F105: Flora 2005; Sb08: Sauvignonblanc 2008; Co05: Colombard 2005.Red wines elaborated from five grape cultivarswere very well discriminated according to the PCA (Figure2) and PLS (data not shown).11••• • • IIJ060,80 TeD?••• • Tr1l70,6 0 PeG?~.•. PV1)7••• 0.4~ •N •u 0,2Q.-0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8-0,200-0,40,• -0,6 0-0,8 PC127,5%Figure 2. PCA scores on the 100 buckets of the ID IH NMRspectra from five tropical red wines with triplicates. The PCl/PC2plot explained 52.6% of the total variance, where: A106:Alfrocheiro, 2006; TeD7: Tempranillo 2007; Tr07: Trincadeira2007; Pe07: Periquita 2007; PV07: Petit Verdot 2007.PC1/PC2 plot explained 52.6% of total variability,In the positive side of PC 1 and PC2 are located Trincadeirawines (Tr07) from 2007 vintage. Chemical shiftscharacterizing these wines were 4.83 ppm (residual water),4.01 (unknown), 3.68 ppm (glycerol), 4.45 ppm (tartaricacid) and 1.15 ppm (butylenglycol). In the negative side ofPCl and in the positive side of PC2 are Petit Yerdot 2007(PY07) wines, that were characterized by chemical shifts1.86 and 1.92 ppm (proline). Alfrocheiro wines, located inthe negative side of the axes PCl and PC2, presented, ascharacteristics, the chemical shifts 4.34 (lactic acid) and1.86 ppm (unknown). In the positive side of PC 1 and in thenegative side ofPC2 wines from Tempranillo and Periquita,both elaborated in 2007, presented similarities, andchemical shifts were identified as 4.01 (unknown), 3.68ppm (glycerol) and 1.86 ppm (unknown). For red winesgrape cultivar and year of the elaboration had a greatimportance to discriminate different wines (Silva Neto elal., 2009; Pereira et al., 2007).CONCLUSIONSChemometrics applied on lD H NMR dataallowed to discriminate brazilian tropical wines. The toolwas not able to discriminate bewteen red and white wineswhen analyzed together, presenting some similarities, butwhen applied to red or white wines separated, it allowed toseparate wines according to different climates (month ofwinemaking) and grape cultivar. The main compoundsidentified explaining clusterings were glycerol, tartaric,lactic, pyruvic and succinic acids, butylenglycol, prolineand some unknown chemical shifts.ACKNOWLEDGEMENTSMany thanks to the wineries, to Embrapa, to UFPE, to theFundayiio de Amparo it Cieneia e it Teenologia do Estado de Pernambuco(FACEPE) and to the Conselho Naeional de Desenvolvimento Cientifieo eTeenol6gieo (CNPq) for their financial support,LITERATUREBRESCIAM.A., CALDAROLA V., DE GIGLIO A., BENEDETfI D.,FANIZZI F.P., SACCO A., 2002. Characterization of thegeographical origin of Italian red wines based on traditional andnuclear magnetic resonance spectrometric determinations. Anal. Chim.Acta, 458, 177-186.FA T.W M., 1996. Metabolite profiling by one- and two-dimensional NMRanalysis of complex mixtures. Prog. Nucl. Magn. Res. Spectrosc., 28,161-219.KEMSLEYE. K., 1998. Discriminant Analysis of Spectroscopic Data, JohnWiley and Sons.KRISHNAN P., KRUGER N.J., RATCLIFFE R.G., 2005. Metabolitefingerprinting and profiling in plants using NMR. 1. Exp. Bot., 56,255-265.PEREIRA G.E., GAUDlLLERE J.-P., VAN LEEUWEN c., HILBERTG., LAVIALLE 0., MAUCOURT M., DEBORDE c., MOING A.,ROLIN, 2005. 'H NMR and chemometrics to characterize maturegrape berries in four wine-growing areas in Bordeaux, France. 1.Agric. Food Chem., 53, 6382-6389.PEREIRA G.E., GAUDlLLERE J.-P., VAN LEEUWEN c., HILBERTG., MAUCOURT M., DEBORDE c., MOING A., ROLIN, 2007.IH NMR metabolic profile of wines from three cultivars, three soiltypes and two contrasting vintages. 1. Int. Sci. Vigne Vin, 41,2, 103-109.PEYNAUDE., 1997. Connaissance et travail du vin. Ed. Dunod, Paris, 341 p.REYNIERA., 2007. Manuel de viticulture, 10" Edition, Lavoisier, 532 p.SILVANETO H.G., DA SILVA J.B.P., PEREIRA G.E., HALLWASS F.,2009. Determination of metabolite profiles in tropical wines by IHNMR spectroscopy and chemometrics. Magnetic Resonance inChemistry, 47, SI27-S129.TEIXEIRA A.H.C., AZEVEDO P.V., 1996. Zoneamento agroclimatico paraa vidcira europeia (Vitis vinifera L.) no Estado de Pernambuco, Brasil.Revista Brasileira de Agrometeorologia, 4,1, 139-145.TEIXEIRA A.H.C., 2001. lnformayoes agrometeorol6gicas do P610Petrolina-PE/Juazeiro-BA. Petrolina: Embrapa Scmi-Arido, 46 p.(Embrapa Semi-Arido. Documentos, 168).TONIETTO J., TEIXEIRA, A.H.C., 2004. Zonage climatique des periodesviticolcs de production dans l'annee en zone tropicale: application dela methodologie du Systeme CCM Geoviticole. In: Joint InternationalConference on Viticultural Zoning, Cape Town, South Africa [S.l.:s.n.], p.193-20 I.

Use of chemometrics to characterize tropical wines from different vintages and grape cultivars according to the 1H NMR spectroscopy data.

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Use of chemometrics to characterize tropical wines from different vintages and grape cultivars according to the 1H NMR spectroscopy data.

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