Food Chemistry 138 (2013) 1837–1846

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Food Chemistry

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Analytical Methods

NMR metabolite profiling of Greek grape marc spirits

Charalambos Fotakis a,b, Dionysis Christodouleas b, Katerina Kokkotou a, Maria Zervou a,⇑,Panagiotis Zoumpoulakis a, Panagiotis Moulos a, Maria Liouni b, Antony Calokerinos b

a Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Vas. Constantinou 48, Athens 11635, Greeceb Department of Chemistry, National and Kapodistrian University of Athens, Athens 15771, Greece

a r t i c l e i n f o

Article history:Received 24 November 2011Received in revised form 12 November 2012Accepted 17 November 2012Available online 5 December 2012

Keywords:MetabolomicsGrape marc spiritPCAOPLS-DAHR-NMR

0308-8146/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.foodchem.2012.11.128

⇑ Corresponding author. Tel.: +30 210 7273870; faxE-mail address: mzervou@eie.gr (M. Zervou).

a b s t r a c t

This 1H NMR based study profiles metabolites in Greek grape marc distillates, tsipouro and tsikoudia.Eightysix samples of indigenous and international varieties, stemming from major vine growing

regions of Greece were investigated. The monitoring protocol addressed the global metabolic profile ofuntreated samples and accomplished the unambiguous assignment of 35 metabolites.

NMR spectra were acquired by applying the robust, sensitive and rapid WET1D NMR pulse sequence,which succeeded to unveil the presence of minor compounds in a high ethanol matrix.

PCA classified the samples according to their provenance, incorporating also information related to thevariety, vintage year and production process within each formed regional assembly. Metabolites such asfusel alcohols, polyols, ethyl esters, mono- and di-saccharides were associated with the classification ofsamples. OPLS-DA ascribed to samples of common regional entity characteristic genotypic metabolitesand probed to the potential influence of the vintage effect. Finally, metabolite profiling underlined theinfluence of the fermentation and distillation procedures.

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1. Introduction

Greece is a country with strong tradition in the field of vineyardexploitation. Besides wine making, the production of Greek grapemarc spirits also bears an important chapter in the Hellenic vinecultivation history. The denomination ‘‘tsikoudia’’ is used for thegrape marc spirit stemming from the island of Crete and the appel-lation ‘‘tsipouro’’ for the grape marc spirit produced in the conti-nental Greece. Their production begins with the selection of thegrape variety, then ensues the thorough fermentation of the grapemarc and lastly occurs a slow and gradual distillation of the fer-mented grape marc. Some viticulturists also tend to add aromaticseeds or plants (such as anise, fennel, saffron, walnut tree leaves)(Soufleros & Rodovitis, 2004). Recently, in certain viticulture re-gions these spirit drinks have been recognized as products of pro-tected geographical designations (Annex III, Regulation [EC] No.110/2008). Specifically, tsipouro of Thessaly, tsipouro of Macedonia,tsikoudia of Crete, all originate from their homonymous geograph-ical areas and tsipouro of Tyrnavos which is a major viticulture areain the periphery of Thessaly.

Tsipouro and tsikoudia have been mainly studied with GC anal-ysis on the basis of their major volatile compounds (Apostolopou-lou, Flouros, Demertzis, & Akrida-Demertzi, 2005). Similar grapemarc distillates stemming from other countries, such as Zivania

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(Cyprus), Grappa (Italy) and Orujo (Spain) have been also studiedon the basis of their major volatile compounds with the implemen-tation of 1H NMR, GC and GC–MS methods (Cortés, Rodríguez, Sal-gado, & Domínguez, 2011; Flamini, 2005; Petrakis et al., 2005). Asedulous literature search has not traced any research resultaddressing the metabolic profile of grape marc distillates.

Metabolomic studies apply high throughput analytical tech-niques, such as mass spectrometry (MS) and HR-NMR, coupledwith multivariate statistical modelling platforms (Dunn & Ellis,2005; Zhang, Nagana Gowda, Ye, & Raftery, 2010).

NMR based metabolic studies have been applied to investigatethe origin of wines and predict certain sensory aspects or to ex-plore the influence of vintage and ageing effects (Ali, Maltese,Toepfer, Choi, & Verpoorte, 2011; Consonni, Cagliani, Guantieri, &Simonato, 2011). Besides ‘‘wine-omics’’, other NMR based meta-bolomic studies have been extensively applied to yeast and grape.For instance, such a research investigated the impact of differentyeast strains in relation to metabolic changes in musts during alco-holic fermentation and in wines during aging (Son, Hwang, Kim,et al., 2009). Lastly, NMR metabolic profiling of grapes identifiedgenuine differences mirrored in the distribution of metabolitesamong different cultivars at different developmental stages (Ali,Maltese, Fortes, et al., 2011).

In this study an untargeted 1H NMR ‘‘omics’’ approach was ap-plied in the high ethanol matrix of the traditional grape marc spir-its in order to monitor their metabolic signature. For this purpose,samples stemming from the major viticulture areas of Greece were

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selected. The implemented metabolomic protocol included theapplication of the WET1D pulse sequence to untreated samples,the implementation of the Chenomx metabolite database forassignment purposes and the application of spectral alignmentalgorithms (Correlation Optimized Warping) (Tomasi, Van den Ber-gand, & Andersson, 2004). Moreover, multivariate statistical che-mometric methods, such as Principal Component Analysis (PCA)and Partial Linear Squares Regression (PLS) (Consonni, & Cagliani,2010; Trygg, Holmes, & Lundstedt, 2007) were applied in orderto identify patterns within the pool of data and trace metabolitesthat could potentially ascribe to genotypic diversity, geographicindication, vintage effect and production practices.

2. Experimental

2.1. Samples and chemicals

The sample set was collected from 4 cooperative associationsand 3 wineries and comprised of 86 grape marc spirits. Specificallythe tsipouro samples included: (a) 6 samples (100% Cabernet Sauvi-gnon, 2009 vintage year) from the viticulture area of Thessalonikiin the region of Macedonia, (b) 34 samples from the region ofThessaly, encompassing: 5 samples (100% Muscat Blanc, 2008 vin-tage year) from the viticulture area of Pilio; 8 samples (80% MuscatHamburg, 10% Roditis, 10% Savvatiano, 2007 vintage year) from theviticulture area of Meteora; 21 samples (70% Muscat Hamburg, 15%Roditis, 15% Savvatiano, 2004 and 2005 vintage years) from the viti-culture area of Tyrnavos, (c) 28 samples from the viticulture area ofNemea in the region of Peloponnesos including: 9 samples (100%Agiorgitiko, 2009 vintage year); 6 samples (100% Moschofilero,2009 vintage year); 13 samples (25% Moschofilero, 25% Chardonnay,50% Muscat Hamburg, 2009 vintage year). Finally, 18 tsikoudia spir-its from the region of Crete were included: 7 samples (60% Liatiko,20% Cabernet Sauvignon, 20% Grenache Rouge, 2008 vintage year)from the viticulture area of Iraklio and 11 samples (100% Liatiko,2004 and 2005 vintage years) from the viticulture area ofRethimno.

NMR solvents and standards D2O (99.9%) and DSS (97%) werepurchased from Sigma (St. Louis, MO).

2.2. Methods – Workflow of NMR based metabolic fingerprinting

2.2.1. NMR spectroscopyNMR spectra were acquired on a Varian 600 MHz NMR spec-

trometer at ambient temperature using a 1H{13C/15N} 5 mm PFGAutomatable Triple Resonance probe.

Two hundred milliliters of each sample were diluted with200 lL of 99.9% D2O, mixed with 140 lL oxalate buffer (400 mM,pH 4.0) and 60 lL sodium 2,2-dimethyl-2-silapentane-5-sulfonate(5 mM, DSS, 97%), and vortexed for 60 s. The solution was trans-ferred into 5-mm NMR tubes. D2O and DSS provided a field fre-quency lock and chemical shift reference.

The WET1D pulse sequence of the Varian library was appliedusing shaped selective pulses to suppress the methyl and ethylpeaks of ethanol as well as the residual water. 13C decouplingpulses were applied to eliminate the 13C-satelites of the sup-pressed signals. Decoupling pulses were applied during the WETpulses using the Wurst40 scheme. Two hundred and fiftysix tran-sients were collected with 72 K data points using a spectral widthof 9615.4 Hz with a relaxation delay of 1.5 s and an acquisitiontime of 4.00 s. Exponential apodisation with line-broadening of0.3 Hz was applied to the spectra prior to Fourier Transformation.NMR spectra were phased and baseline corrected using the versionof Chenomx NMR suite 7.0 software, professional edition (Che-nomx Inc., Canada).

A series of 2D experiments, gCOSY, zTOCSY, gHMBCad, gHSQ-Cad were implemented and permitted the assignment of the exist-ing metabolites (Suppl. Table 1, Suppl. Figs. 1–4). Theinterpretation of 2D spectra was performed with the use of Mest-reNOVA software.The identification procedure was also assisted bythe combined use of a reference metabolic 1H NMR database (Che-nomx Suite 7.0), spiking experiments and literature data.

2.2.2. Data reduction and spectral alignmentThe NMR spectral data was reduced into spectral buckets of

0.001 ppm. The following regions were removed: D2O (4.6–4.8 ppm), residual ethanol methyl, methylene peaks (1.15–1.20 ppm and 3.59–3.72 ppm), and the region of buffer (1.9–2.1 ppm). The spectra were then normalized to the standardizedarea of the reference compound and converted to ASCII format.The ASCII format files were imported into MATLAB (R2006a, Math-works, Inc., 2006), and all spectra were aligned using the Correla-tion Optimized Warping (COW) method (Tomasi et al., 2004).

2.2.3. Multivariate data analysisThe matrix of the processed spectral data sets was imported

into the SIMCA-P version 13.0 (Umetrics, Umeå, Sweden) for statis-tical analysis.

First principal component analysis was employed in order tovisualize any relation (trends, outliers) among the observations(samples). A PCA model estimates the systematic variation in adata matrix by a low dimensional model plane. The spectral datawas mean-centered with no scaling (Ctr) and the PCA model wasextracted at a confidence level of 95%. In particular, when Ctrscaling is applied the impact of a variable is ascribed to its inten-sity and therefore regions of low intensity have little or no impacton the model. Following that, four contribution plots were ex-tracted by comparing the average values of each formed groupto the average value of the rest of the samples in an effort tohighlight the variables which contribute to the classification ofthe samples.

Data sets were further subjected to OPLS-DA analysis, an exten-sion of the supervised PLS regression method in order to increasethe quality of the classification model. Specifically, OPLS-DA facili-tates the separation of the systematic variation in X into two parts,one that is linearly related to Y (predictive information) and onethat is unrelated to Y (orthogonal information). The predictiveinformation of Y in X is concentrated in the first predictive compo-nent and is associated with the between groups variation while thevariation in X which is unrelated to Y is put in the second andorthogonal component and is linked to the within groups variation.The cognition of the orthogonal variation improves model visuali-zation and interpretation.

The extracted OPLS-DA models at a confidence level of 95%were Ctr and Pareto scaled (Par). Particularly, it is anticipated thatwith the application of Par scaling any regions of low-mediumintensity will affect the analysis but only if they represent system-atic variation. Loading plots were then extracted to reveal the vari-ables that bear class discriminating power. Also, S-line plots wereextracted in order to detect the metabolites that influence mostthe group membership. The top end of the color scale visualizesthe NMR shifts that influence the separation of the groups.

The quality of the models was described by the goodness-of-fitR2 and the predictive ability Q2 values. The goodness-of-fit, R2

(0 6 R26 1) explains the variation in the PCA model, thus consti-

tuting a quantitative measure of how well the data of the trainingset was mathematically reproduced. The overall predictive abilityof the model is assessed by the cumulative Q2 representing thefraction of the variation of Y that can be predicted by the model,which was extracted according to the internal cross validation

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default method of SIMCA-P software (Eriksson, Johansson, Ketta-neh-Wold, & Wold, 2006).

3. Results and discussion

3.1. 1H NMR metabolic profile

Tsikoudia and Tsipouro encompass minor compounds that exhi-bit weak intensity resonances in a high ethanol matrix. Also, astrong overlapping in the aliphatic and sugar regions impedesthe resonances identification. The use of the robust WET1D pulsesequence allows the triple suppression of the ethanol methyl,methylene and water residual strong signals and the applicationof 13C decoupling scheme. These features limited the presence ofartefacts and the baseline distortions and optimized the signal tonoise ratio. The removal of the 13C-satellites unveiled the presenceof metabolites such as 2,3-butanediol, ethyl acetate, lactic acid, fu-sel alcohols (more specifically amyl alcohols such as 3-methylbutanol, 2-methyl butanol, isobutanol) and mono-/di-saccharidesthat were otherwise obscured.

Resonance peak identification was accomplished by the use ofliterature data (Ali, Maltese, Toepfer, et al., 2011; Consonni et al.,2011) while a series of 2D experiments (gCOSY, zTOCSY, gHMBCad,gHSQCad), further assisted the assignment of the overlapping res-onances. This problem was especially prominent in the sugar re-gion, where most of the mono- and di-saccharides’ peakscoincided. In this case, the utilisation of the Chenomx NMR suite7.0 platform in conjunction with the 2D experiments facilitatedthe identification of the carbohydrates. Metabolites in the aromaticregion were also difficult to trace, taking into consideration thelack of literature data and their low concentration. Therefore, het-eronuclear and homonuclear correlation experiments were ren-dered necessary in order to elucidate their potent identification(Suppl. Figs. 1–4).

A representative WET1D spectrum of an untreated grape marcdistillate sample is displayed in Fig. 1 with indications to the iden-tified metabolites. In total, thirty five metabolites were identifiedcontributing to the results interpretation. Alcohols, esters, alde-hydes, organic acids, sugars and aromatic compound resonanceswere ascribed (Suppl. Table 1).

It has to be noted that the 1H and 13C resonances of several com-pounds were assigned for the first time in Greek grape marc distil-lates, namely acetaldehyde hydrate, succinic acid, pyruvic acid,2,3-butanediol, glycerol, mono- and di-saccharides, lactic acidand tartaric acid. Tyrosol was only traced in samples of Liatiko.

Fig. 1. The WET1D spectrum of an untreated Cabernet Sauvignon tsipouro from Macedoniabutanediol, 3: 2,3-butanediol, 4: ethyl acetate, 5: ethyl lactate, 6: acetaldehyde hydrate,11: buffer/acetic acid, 12: acetates, 13: acetaldehyde, 14: succinic Aaid, 15: methyl acetglucose, 21:2-phenyl ethanol, 22: furanic compounds, 23: formic acid). The ethanol and

Evidently, the 1H NMR metabolite profile of Greek grape marcspirits constitutes a very interesting mosaic of metabolites. In fact,literature data highlight several factors that affect their presence inalcoholic beverages and a synoptical description is attempted inthe followings.

The 1H NMR profiling enabled the monitoring of the mono- anddi-saccharide content related to residual fermentable sugars. Sug-ars are primary metabolites directly associated with the grape ber-ry being involved in energy metabolism, osmoprotection,biosynthesis of important secondary metabolites such as pheno-lics, terpenes and organic acids and regulating the production ofanthocyanins. Differences in accumulation of glucose and fructosehave been observed between different Vitis species including Cab-ernet Sauvignon species and have been attributed to differences inthe activity of specific sugar metabolism enzymes (Pan, Cao, &Duan, 2009). Glucose accumulation in grapes is known to bestrongly affected by environmental factors as mirrored in studiesreferring to grape vine of different geographical origin (Son et al.,2008) and studies referring to the influence of stress conditionsand particularly drought (Cramer et al., 2007).

Another important class of compounds present in grape marcspirits is acetic esters produced during fermentation through acet-yl CoA chemical esterification of alcohols and acids in yeast. Ethylacetate is often the most abundant, with an important contributionon the organoleptic characteristics of distilled alcoholic beverages.It has to be noted however that increased concentrations of ethyland methyl acetate are related with aerobic conditions during fer-mentation and probable acetic bacterial spoilage or incorrect sep-aration of the distillation fractions. The ensiling conditions maypromote a significant proliferation of anaerobes, resulting in an in-crease of LAB that favours formation of ethyl lactate with ensilingtime of grape pomace (Cortés et al., 2011).

The major group of volatile compounds in distillates is fuselalcohols. These are formed during alcoholic fermentation throughthe anabolic route from carbohydrates or through the catabolicroute from precursor amino acids and have a notable influenceon the sensory properties. Production by yeast is highly influencedby the content in precursor amino acids of the must and thereforeby the grape variety. In Greek grape marc spirits a subgroup of fu-sel alcohols was dominant, amyl alcohols and specifcally 2-methyl-1-butanol and 3-methyl-1-butanol (Apostolopoulou et al., 2005;González, Agrasar, Pastrana Castro, Fernández, & Pérez Guerra,2011).

2-Phenyl ethanol is the dominant aromatic alcohol in grapemarc spirits that contributes rose nuances to the distillate. Bacte-ria, fungi and yeasts may synthesize 2-phenyl ethanol using L-

is presented indicating representative metabolites (0: DSS, 1: fusel Alcohols, 2: 1,2-7: 3-hydroxy-2-butanone, 8: lactic acid, 9: 3-methyl butanol, 10: 2-methyl butanol,ate, 16: methanol, 17: D-sucrose, 18: mono-/di-saccharides, 19: b-fructose, 20: b-D-

the residual water peaks are suppressed by the WET1D pulse sequence.

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phenylalanine as a substrate, which allows this metabolite to bedeemed as a potent genotypic marker for grape marc spirits (Cortéset al., 2011) and wines (Son et al., 2008).

The identified polyols in Greek distillates include glycerol, 2,3-butanediol and 1,2-butanediol. In particular, glycerol is a majorby-product of alcoholic fermentation. Its concentration is affectedby growth as well as environmental parameters, including thegrape variety and ripeness, the microbial flora on grape berries,the sulfite concentration, pH, fermentation temperature, nitrogensource and yeast strain (Son, Hwang, Kim, et al., 2009). 2,3-Butane-diol is produced from pyruvate or acetoin and higher levels of thismetabolite indicate reduction of acetoin which has a negative im-pact on the beverage taste with low taste and odour threshold (Ali,Maltese, Toepfer, et al., 2011).

Furfural is the major representative of the furans group that wasidentified in Greek grape marc spirits. It is formed during distilla-tion due to dehydration of residual sugars (pentoses) caused byheating in acid conditions and/or Maillard reaction (Apostolopou-lou et al., 2005). Its content is affected by the cultivar and excessivepressing of the corresponding marc (Cortés et al., 2011).

Differentiation of grape derived products has been also basedon the organic acid profiles. The levels of lactic, pyruvic, tartaricand succinic acids, depend upon various factors, such as the grapecultivar and/or fermentation process (Son et al., 2008). In general,some acids are formed in grapes before or during ripening and oth-ers are formed during wine fermentation. Tartaric acid is one of themost prevalent acids in unripe grapes. It is the primary non fer-mentable soluble acid in grapes which accumulates in grape ber-ries during ripening and is formed by the ascorbic acid (vitaminC) catabolism. Recent studies have established the relation of tar-taric acid’s content to the grapevine species by identifying thekey enzyme in its biosynthesis. Tartaric acid is also found in mustand since the combustion is more effective when the grapes aremaintained at high temperatures, its concentration in must insouthern vineyards is lower compared to northern ones. Further-more, strong reduction of this acid was connected to shaded berryclusters (DeBolt, Cook, & Ford, 2006; Gomez-Benitez, 1993; Kun-kee, 1991).

Following the organic acids from the fermentation, pyruvic acidhas a crucial function in cell metabolism. Although its concentra-tions of in wine are low, pyruvic acid produces two stereoisomersof lactic acid, L and D. The first is mainly produced from bacteriawhile the second mainly originates from yeasts. L(�)lactic acid isalso used in order to avoid negative aspects of acidification, sinceit is listed as a food additive (E270) and meets the requirementsof both the Food chemical Codex and the European Pharmacopoeia.

Another acid that is produced during fermentation due to theaction of yeast is succinic (1,4-butanedioic acid). Succinic acidmay be formed by yeasts from sugar or via GABA metabolismand its content has been associated with the vintage factor andthe grape cultivar (Son, Hwang, Kim, et al., 2009; Ali, Maltese,Toepfer, et al., 2011).

Certain observed compounds are in part implicated to thetechnological phase of the production of distillates. For instance,methanol is a colourless volatile compound deriving from enzy-matic degradation of pectins. The methanol content in grape marcdistillates depends on the processing of the grapes (crushing,pressing), storage time, initial composition, pH, temperature offermentation and distillation process. It is considered as a roughindicator of the degree of pressing in distillates (Lukic et al.,2011).

The incorrect storage of the fermented grape marc before distil-lation leads to bacterial spoilage. The high concentrations of thestrongly odour compounds 1-propanol and 1-butanol are consid-ered indications of such mistreatment (Apostolopoulou et al.,2005).

The major carbonyl compound in grape marc distillates is acet-aldehyde (González et al., 2011), a direct alcoholic fermentationbyproduct.

3.2. Classification through multivariate data analysis

3.2.1. Overview on geographic indicationPrincipal component analysis (PCA) was implemented in order

to obtain an overview of the samples’ classification pattern. Contri-bution plots were generated to determine which metabolites bearthe class discriminatory power.

A PCA model with two components was computed on the totalset of samples and explained sufficiently the 84.7% of the datasetvariance, as displayed in Fig. 2. The model demonstrates verygood fitness and high predictability as indicated by the statisticalvalues R2X(cum) = 0.85 and Q2(cum) = 0.82. In particular, theexcellent prediction of the model with a Q2(cum) P 0.80 andR2X(cum) � Q2(cum) < 0.2 enhances the robustness and predictiveresponse of this model thus amplifying the reliability of the classi-fication (Eriksson et al., 2006).

In fact, the 86 grape marc spirits were divided clearly into fourgroups in the PCA scatter plot according to their geographic origin.Arbitrary ellipsoids have been drawn to encircle the grape marcspirits of common origin. Moreover, distinct subgroups wereformed within each cluster, thus highlighting the contribution ofother factors in the differentiation of the samples, with the geno-type posing as the dominant.

3.3. Macedonian grape marc spirits’ profiling

The grouping of Cabernet Sauvignon samples from the region ofMacedonia was framed by the PCA model. The contribution plot ofthese samples (Fig. 3) indicated an extremely high content ofmono- and di-saccharides, (fructose, glucose and sucrose respec-tively) as well as a high content in ethyl esters and acetaldehydehydrate and low concentration in fusel and especially amyl alco-hols. Interestingly, glucose has already been identified as a metab-olite contributing to the production area discrimination ofCabernet Sauvignon wines (Son et al., 2008).

3.4. Cretan grape marc spirits’ profiling

These samples formed a tight cluster on the first quadrant thatslightly extends to the second quadrant. The contribution plot(Suppl. Fig. 5) ascribed to these samples the highest concentrationin esters (methyl acetate, ethyl acetate, ethyl lactate). The rich es-ter profile can be related to the implemented production process.Cretan producers often use wild yeasts in the production of tsikou-dia that could result in increased content of ethyl lactate (Cortéset al., 2011). Furthermore, these samples exhibited low concentra-tion in mono- and di-saccharides, polyols and fusel alcohols. Inter-estingly, two strong subgroups were formed, differentiating themonovarietal samples of Liatiko from the blended samples (Liatiko,Cabernet Sauvignon, Grenache Rouge). This information constitutesthe first attempt to assess the metabolic profile of the indigenousvariety Liatiko.

3.5. Peloponnesian grape marc spirits’ profiling

These samples spread along the first and fourth quadrant of thePCA model. Their contribution plot (Suppl. Fig. 6) showed that theyexhibited the highest concentration in fusel alcohols and polyols,while they displayed low concentration in mono-/di-saccharidesand high concentration in acetaldehyde, lactic acid, methyl acetateand ethyl acetate. The increased concentration of acetaldehydecould be a result of fermentation temperature and the amount of

Fig. 2. PCA model with two components extracted from the tsipouro/tsikoudia samples stemming from four major viticulture regions in Greece. PC1 explains 74.1% of thevariation and PC2 explains 10.6% of the variation. The varietal classification is depicted using different symbols: Macedonia (black star = Cabernet Sauvignon), Crete (red opencircle = Liatiko, 2004/2005 Vintage; red filled circle = Liatiko & Cabernet Sauvignon & Grenache Rouge, 2008 Vintage), Peloponnesos (green open square = Moschofilero, 2009Vintage; green filled square = Agiorgitiko, 2009 Vintage; green filled diamond = Moschofilero & Chardonnay & Muscat, 2009 Vintage), Thessaly (blue open triangle = 90% MuscatHamburg & 5% Roditis & 5% Savvatiano, Meteora, 2007 Vintage; blue filled triangle = 80% Muscat Hamburg & 10% Roditis & 10% Savvatiano, Tyrnavos, 2004 Vintage; blueinverted filled triangle = 80% Muscat Hamburg & 10% Roditis & 10% Savvatiano, Tyrnavos, 2005 Vintage; blue open inverted triangle = Muscat Blanc, Pilio 2008). (Forinterpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

C. Fotakis et al. / Food Chemistry 138 (2013) 1837–1846 1841

SO2 in marc (Lukic et al., 2011). The increased content of higheralcohols could be associated with the influence of terroir in elevat-ing the nitrogen sources in the grape and thus affecting the conc-etration of the high alcohols’ precursors, the amino acids.Additionally, lactic acid is a metabolite affected by wild yeastand lactic acid bacteria and hence is associated with the terroir ef-fect (Mazzeia, Francesca, Moschetti, & Piccolo, 2010). The three dis-tinct subgroups contain samples from the same vineyard thereforetheir variation is probably explained by their genotypic diversity.

3.6. Thessalian grape marc spirits’ profiling

A well defined cluster spreading along the second and thirdquadrants is formed by these samples. The contribution plot (Sup-pl. Fig. 7) indicated a high concentration in the odoriferous com-pounds 2-phenyl ethanol and furfural. Furthermore, thesesamples exhibited the lowest concentration in esters (methyl ace-tate, ethyl acetate, and ethyl lactate), mono- and di-saccharidesand fusel alcohols. They are also characterized by low concentra-tion in acetaldehyde and polyols.

Evidently, the implemented WET1D ‘‘omics’’ approach accom-plished to classify the 86 samples and probed to metabolic markersthat shape the ‘‘trends’’ in the extracted model. A distinct profile for

each viticulture region was extracted according to the metaboliteconsistency. Interestingly with the exception of the Peloponnesiansamples, each group in this classification corresponds to spirit drinksthat bear recognized geographical indication. In line with this, theseresults could provide evidence for the Peloponnesian samples topromote a claim as eventual candidatures of geographic indication.

It has to be noted, that metabolic profiling studies on wine indi-cate sugar moieties as the dominant markers of the provenance,especially when very different geographical locations are ad-dressed (Consonni & Cagliani, 2010). On these grounds, the assess-ment of mono- and di-saccharides content in grape marc spiritsthrough NMR profiling further enabled this PCA model to elaborateon their relation to geographic origin. In total, the influence of theprovenance is the prevalent factor for the classification of the grapemarc spirits. Lastly, the global metabolic profile of samples was as-sessed and succeeded in revealing the concomitant impact of thegenotype as well as the production procedure and the vintage year,as is apparent by the formation of clear subgroups in the score plot.

3.6.1. Elucidation on the subgroup formationThe Orthogonal Partial Least Squares Discriminant Analysis

(OPLS-DA) was used to elucidate further the observed subgroup for-mation within the samples that stem from the same region. Regres-

Fig. 3. PCA Contribution plot of the Cabernet Sauvignon tsipouro samples from Macedonia (green line). Overlaid is the corresponding NMR spectrum (blue line). (Forinterpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

1842 C. Fotakis et al. / Food Chemistry 138 (2013) 1837–1846

sion models have been validated using cross validation-analysis ofvariance (CV-ANOVA), with a P-value <0.05. The OPLS-DA loadingsand S-line plots were used to convey information regarding themetabolites that constitute chemotaxonomy criteria.

3.7. Diversity of Peloponnesian grape marc spirits

The generated OPLS-DA model (Fig. 4) accomplished the clearseparation among the blended samples (Moschofilero & Chardonnay& Muscat) and the Agiorgitiko and Moschofilero samples along thefirst predictive component, with R2Y(cum) = 0.98 and a strong pre-dictive ability Q2(cum) = 0.85. A loading plot (Suppl. Fig. 8) was ex-tracted in order to elucidate the discriminant metabolites of eachgroup. The blended samples (Moschofilero & Chardonnay & Muscat)were related to acetaldehyde hydrate, lactic acid, 3-hydroxy-2-butanone, 2,3-butanediol and glycerol. In an effort to explore morethoroughly the metabolites which contribute to the differentiationbetween the marcs derived from the indigenous varieties Agiorgi-tiko (red) and Moschofilero (white) an OPLS-DA model was extractedfor the respective samples. The model further separated the twovarieties along the first component and the corresponding s-lineplot highlighted the metabolites that mainly affect the separationof the two groups as displayed in (Suppl. Fig. 9). Interestingly, 3-methyl butanol, pyruvic acid, polyols and 2-phenyl ethanol arecharacteristic for Agiorgitiko, while ethyl acetate, ethyl lactate,methanol and succinic acid characterize Moschofilero. The highermethanol content in white grapes has also been reported by Cortésand Fernández (2011). NMR studies have ascribed to 2-phenyl eth-anol the role of genotypic biomarker. The discriminating organicacids levels are mainly affected by fermentation behaviors of wineyeast or lactic acid bacteria (Son, Hwang, Hyuk-Jin, et al., 2009).

3.8. Diversity of Cretan grape marc spirits

The extracted OPLS-DA model between Liatiko monovarietalsamples from one vineyard and their blended counterparts from

another vineyard exhibited an R2Y(cum) = 0.99 and a satisfactorypredictive ability, indicated by the value of Q2(cum) = 0.76. As ob-served in the OPLS-DA map (Fig. 5), the blended samples (Liatiko &Cabernet Sauvignon & Grenache Rouge) are clearly differentiatedfrom the Liatiko samples along the first predictive component. Infact, the s-line plot (Suppl. Fig. 10) detected the metabolites withthe highest influence on the separation of the two groups. Forthe Liatiko these are 2-phenyl ethanol, methanol, methyl acetate,succinic, fusel alcohols, while for the Liatiko blend are sucrose, lac-tic acid and ethyl lactate.

In particular, the fermentation derived aroma compounds 3-methyl butanol, 2-methyl-1-butanol and 2-phenyl ethanol charac-terize the monovarietal grape marc distillates from the ancientvariety Liatiko potentially probing to an increased content of theprecursor amino acids L-phenylalanine, leukine and isoleukine inthe raw material. The Macedonian Cabernet Sauvignon samples dis-cussed previously revealed increased levels of sucrose, the sametrend was observed in the blended samples, and thus this metabo-lite could be linked to the Cabernet Sauvignon cultivar. Succinic andlactic acids levels depend mainly on the fermentation conditionsand partly on the grape cultivar. Lastly, the variation in methanoland methyl acetate corresponds to differences in the productionprocesses followed by the two vineyards. The production of thesecompounds is also dependant of the yeast strain and distillationprocesses.

This discrimination shows that NMR profiling may tracechanges imparted on the final product by the blending procedure.

3.9. Impact of Vintage on the Cretan liatiko samples

A within-group variation for the monovarietal samples wastraced, related to the vintage factor. Samples of the 2005 vintagewere separated against those of 2004 along the first orthogonalcomponent (Fig. 5). The extracted loading plot (Suppl. Fig. 10) re-veals that the former samples (2005 vintage) located in the fourthquadrant, corresponding to positive values for the first predictive

Fig. 4. OPLS-DA scores plot with tsipouro samples from Pelloponessos. The Moschofilero, the Agiorgitiko and the blended samples (Moschofilero & Chardonnay & Muscat) aredifferentiated.

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component and negative values for the first orthogonal compo-nent, are characterized by pyruvic acid and mono-saccharides.The samples of the 2004 vintage year are located in the first quad-rant of the loading plot thus corresponding to positive values forthe first predictive component and negative values for the firstorthogonal component, and are characterized by methanol, methylacetate, 2,3-butanediol, 3-methyl butanol, 2-methyl butanol, iso-butanol, succinic, and 2-phenyl ethanol. Several factors may belinked to this variation, for instance, vine water deficit stress hasbeen observed to affect negatively the berry sugar content andon the other hand, it has been observed to correlate positively withthe nitrogen content (Peyrot des Gachons et al., 2005). The latterobviously associates with the amino acids precursors of the higheralcohols. The selected yeast strains may influence the productionof succinate, of higher alcohols and of pyruvic acid. The fermenta-tion conditions (temperature, pH values) influence the levels ofsuccinate and pyruvate. The content of succinate as well as thatof higher alcohols may also be influenced by the aeration condi-tions. (Konig, Unden, & Frohlich, 2009; Lambrechts & Pretorius,2000).

3.10. Diversity of Thessalian grape marc spirits

The extracted OPLS-DA model exhibited a high value of R2-

Y(cum) = 0.99, as well as of Q2(cum) = 0.97 providing an excellentpredictive ability. This model succeeded to differentiate the mono-varietal Muscat blanc distillates from Pilio, the blended samples(90% Muscat Hamburg & 5% Roditis & 5% Savvatiano) from Meteoraand the blended samples (80% Muscat Hamburg & 10% Roditis & 10%Savvatiano) from Tyrnavos (Fig. 6). Specifically, along the first pre-dictive component a separation is observed between the samples

from the viticulture area of Tyrnavos and the rest of the samplesstemming from Meteora and Pilio. It has to be noted that each ofthe formed clusters contains samples that bear geographical indi-cation. Samples of Tyrnavos are located in the second and thirdquadrants and the extracted loading plot (Suppl. Fig. 11) ascribeslactic acid, acetaldehyde, pyruvic acid, tartaric acid, ethyl acetate,formic acid, furfural and 2-phenyl ethanol to them. On the otherhand, the two clusters that spread along the first and fourth quad-rants are influenced by succinic acid, mono- di-saccharides, acetal-dehyde hydrate, methanol and methyl acetate. The participation ofmono- and di-saccharides in this discrimination mirrors the geo-graphic and environmental impact. Besides, the two blendedgroups originating from Tyrnavos (Blend 1) and Meteora (Blend2) contain common varieties and the subtle difference in theblending ratio postulates that other factors could contribute totheir discrimination. In this context, tartaric acid and 2-phenyl eth-anol which bear genotypic discriminant power characterize Tyrna-vos samples indicating that environmental conditions, as vinewater and nitrogen status could be another factor influencing tar-taric and phenylalanine, the precursor of 2-phenyl ethanol (Peyrotdes Gachons et al., 2005). Moreover, the increased levels of pyru-vate, lactate, ethyl acetate, as well as furfural and acetaldehyde ob-served for the Tyrnavos samples prompt to fermentation anddistillation process respectively. (Apostolopoulou et al., 2005;Cortés et al., 2011).

3.11. Impact of Vintage on the Tyrnavos blended samples

Additionally, the model framed a within-group variation for thesamples in Tyrnavos attributed to the vintage factor (Suppl.Fig. 11). The samples of the 2004 vintage in the second quadrant

Fig. 5. OPLS-DA scores plot with tsikoudia samples from Crete. The blended Liatiko samples (Liatiko & Cabernet Sauvignon & Grenache Rouge) from the monovarietal Liatikosamples are discriminated. Two successive vintages for monovarietal Liatiko samples are indicated.

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are characterized by 3-methylbutanol, 2-methylbutanol, 1-propa-nol, isobutanol and methyl acetate, while the samples of the2005 vintage in the third quadrant are characterized by acetalde-hyde, furfural, 2-phenyl ethanol, pyruvic and tartaric acids. Thesemetabolites mirror a multi factorial impact responsible for this var-iation. As already stated, the content of higher alcohols and tartaricacid can be influenced by environmental conditions. Moreover,studies that elaborated on Grappa and Orujo probed to a positivecorrelation between 3-methylbutanol, 2-methylbutanol and 2-methylpropanol with low pressed grape marc, distilled in the pres-ence of wine lees (Cortés et al., 2011). Samples of the 2004 vintageyear are dominated by increased levels of fusel alcohols that areindicative of the same formative process. 1-propanol is a bacterialspoilage index (Apostolopoulou et al., 2005) and together withmethyl acetate they relate to less controlled storage conditions.The increased levels of acetaldehyde and furfural could be attrib-uted to less effective separation of the distillation fractions (Apos-tolopoulou et al., 2005).

In summary, the extracted OPLS-DA models elicited specificinformation on the determination of metabolic markers that relateto the genotypic diversity of the distillates originating from com-mon regional entities of Greece. An effort has been made to delin-eate the influence of the vinification and distillation procedures.Also, the terroir influence has been monitored in the differentiationof products bearing geographical indication as in the case of thesamples from Thessaly. Notably, the generated models have alsounderlined the potential influence of the vintage factor. To the bestof our knowledge, no other research has employed 1H NMR meta-bolic profiling to frame differences in the metabolic constituents ofdistillates between two vintage years. It is necessary to includemore samples from different production years in order to explore

in depth the potential influence of the vintage effect to the distil-lates. Most of the proposed metabolites have already been ren-dered important markers for the differentiation of wine, yeastand must, but this novel metabolomic approach in high ethanolmatrixes confirms and elucidates their influence in grape marcspirits.

4. Conclusions

The untargeted NMR based metabolomic protocol implementedin untreated Greek grape marc distillates, accomplished their clas-sification and revealed potent metabolic markers. The WET1Dpulse sequence was implemented in the ‘‘omics’’ approach, in or-der to profile primary and secondary metabolites, which can beused to establish authentication criteria associated with geograph-ical origin and/or varietal discrimination. This study further illus-trates the versatility of NMR to elicit information from grapederived products despite their production procedure.

The unveiling of this high ethanol matrix accomplished theassignment of 35 metabolites. In fact, concerning Greek grape marcdistillates it is the first time that acetaldehyde hydrate, succinicacid, pyruvic acid, tyrosol, 2,3-butanediol, glycerol, mono-/di-sac-charides, lactic acid, ethylene glygol, a-hydroxy isobutyrate, tar-taric acid, 1,2-butanediol moieties were traced by HR-NMR. Adatabase has been created based on the acquired spectroscopicdata that could be used as a guide and tool for eventual candida-tures for denomination of origin and confirm/support both claimsfor Greek distillates and distillate-markers.

The extracted statistical models (PCA & O-PLS/DA) in the con-text of their excellent goodness of fit [R2(cum) > 0.8] and predictive

Fig. 6. OPLS-DA scores plot with tsipouro samples from different subregions of Thessaly. The differentiation of Blend 2 samples (90% Muscat Hamburg & 5% Roditis & 5%Savvatiano) from Meteora, the Muscat Blanc samples from Pilio and the Blend 1 samples (80% Muscat Hamburg & 10% Roditis & 10% Savvatiano) from Tyrnavos is presented.Two successive vintages for the Blend 1 samples are discriminated.

C. Fotakis et al. / Food Chemistry 138 (2013) 1837–1846 1845

ability, [Q2(cum) > 0.8] may constitute a reliable and tentantiveauthentication tool that determines the geographic origin of Greekgrape marc distillates and discriminates markers that ascribe togenotypic diversity, production procedure and the vintage year.

Concluding, this study may pave the way for NMR based meta-bolomic analyses both in grape marc spirits and distillates of highethanol content, such as whiskey and vodka.

Acknowledgements

We would like to express our sincere gratitude to ProfessorKakoulidou Tsantili Anna for her valuable contribution in the che-mometric methods. D. Christodouleas gratefully acknowledgesfinancial support from ‘‘Alexander S.Onassis’’ Foundation. Wewould like to thank the winery Diamantakis, Domaine Karavitakisand the Cooperative association of Syvritou for their kind donationof premium quality Greek grape marc distillates. The researchleading to these results has received funding from the EuropeanUnion’s Seventh Framework Programme (FP7-REGPOT-2009-1)under grant agreement No. 245866.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.foodchem.2012.11.128.

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