Characterization of Glycosylated Aroma Compounds in TannatGrapes and Feasibility of the Near Infrared SpectroscopyApplication for Their Prediction

Eduardo Boido & Laura Fariña & Francisco Carrau &

Eduardo Dellacassa & Daniel Cozzolino

Received: 28 December 2011 /Accepted: 12 April 2012 /Published online: 27 April 2012# Springer Science+Business Media, LLC 2012

Abstract The objective measure of grape quality is animportant requirement to enhance wine quality. Particularly,the quantitation of glycosylated aroma compounds could be auseful index for winemakers. In the present work, we evalu-ated the bound compounds profile on Tannat grapes, and thepotential of near infrared (NIR) spectroscopy coupled to che-mometric techniques to determine the concentration of thedifferent glycosylated aroma compounds present in this grapevariety. The results showed phenols and norisoprenoids as thequantitative most important group of compounds, represent-ing almost 80 % of the total glycosidic components present inTannat grapes. The glycosidic form of ethyl-β-(4-hydroxy-3-methoxy-phenyl)-propionate was identified for the first timein grapes. The results suggested NIR technique as a screeningtool for a fast analysis of the grape aroma potential. Theapplication of such methodology would be ideal for fast grapescreening purposes, contributing to the winemaking decisions,classification of the fruit, and perhaps maturity monitoring.

Keywords Tannat grape . Glycosylated aroma compounds .

Near infrared spectroscopy

Introduction

Wine aroma depends on numerous factors, with particularimportance being assigned to the variety of grape, vinification

process, maturation, and aging (Rapp and Mandery 1986;Williams 1993; Swiegers et al. 2005). Moreover, it is wellestablished that the secondary metabolites of grapes are respon-sible for the principal aroma compounds in musts and soprovide the basis of varietal character (Williams 1993; Wirthet al. 2001). In the case of wines obtained from nonaromaticgrapes, their aromas are partly dependent on the compositionand levels of glycosidic precursors produced in grapes. Inmature grapes these nonvolatile and non-odorant compoundsare numerous, and their levels are higher than those of volatiles(Williams et al. 1992). Some of these compounds may behydrolyzed during winemaking, giving rise to odorant com-pounds that can specifically mark the aroma of wines (Wirth etal. 2001; Boido et al. 2002). Four groups of glycosides wereidentified in grapes: β-D-glucopyranosides, and more complexdisaccharides as 6-O-(α-L-arabinofuranosyl)-β-D-glucopyrano-sides, 6-O-(α-L-rhamnopyranosyl)-β-D-glucopyranosides, and6-O-(β-D-apiofuranosyl)-β-D-glucopyranosides (Williams etal. 1982, 1992; Winterhalter and Skouroumounis 1997; Carrauet al. 2008). Complementary, several compounds have alsobeen identified in the aglycones: monoterpenols and monoter-pendiols, alcohols, acids, shikimic compounds, and C13-norisoprenoids (Rapp and Mandery 1986; Williams 1993;Strauss et al. 1986; Versini et al. 1987).

For Vitis vinifera cv. Tannat, a nonaromatic grapes variety,glycosidic precursors are an important source of aroma in thewine after aging (Boido et al. 2003). The Tannat variety,originated in the south of France, was introduced to Uruguayin 1870 from the Basque region, being today Uruguay, theonly country in the Americas where this grape is found.Consequently, the Uruguayan winemaking industry has estab-lished the Tannat wine production as strategy for its marketingdevelopment (Carrau 1997; Carrau et al. 2011).

The ability to objectively measure fruit quality is animportant requirement to enhance wine quality (Ristic et

E. Boido (*) : L. Fariña : F. Carrau : E. DellacassaSección Enología, Facultad de Química, UdelaR,Montevideo, Uruguaye-mail: eboido@fq.edu.uy

D. CozzolinoThe Australian Wine Research Institute,Urrbrae,Adelaide, Australia

Food Anal. Methods (2013) 6:100–111DOI 10.1007/s12161-012-9423-5

al. 2010). For glycosylated aroma compounds, a correlationbetween the classic parameters of fruit evaluation (sugar, pH,acidity) and their concentration (Boido et al. 2006) has not beenfound. However, due to their potential role in the aroma char-acteristics of wine, the quantitation of glycosylated aroma com-pounds could be a useful index for winemakers to determine, forinstance, the optimal grape maturity in order to use the mostsuitable winemaking processes for their maximal valorization.

Methods used for glycoside analysis generally involvecomplex procedures combining extraction and detection bygas chromatography (Wirth et al. 2001). These techniquesare relatively complex, requiring sample preparation, whichconsumes important time of analysis.

The only method fast enough to assess these glycoconju-gates (glycosyl–glucose method or G–G) was developed byWilliams et al. (1995) and further modified by several authors(Arévalo Villena et al. 2006; Iland et al. 1996; Whiton andZoecklein 2002; Zoecklein et al. 2000). This technique isbased on the determination of the glucose released from thoseprecursors after acidic hydrolysis allowing only the total de-termination of glycoconjugates. Since all the glycosides do nothave the same contribution to wine aroma, it would be valuableto determine them more specifically. In addition, even when itwas modified for use in winery (Arévalo Villena et al. 2006),this assay still might be considered as a too complex and time-consuming analytical method for practical use when manysamples need to be analyzed in a short period of time.

During recent years, developments in both chemometricsand instrumentation have resulted in rapid methods relatingmultivariate spectroscopic and chemical data for predictingthe concentration of specific chemical constituents andhelped to reduce the demand for traditional analysis usingchemical reagents. Near infrared spectroscopy (NIR) is sucha rapid and nondestructive technique and generally requiresminimal sample processing prior to analysis (Osborne et al.1993). The NIR region (750 and 2,500 nm) contains infor-mation concerning the relative proportions of C–H, N–H,and O–H bonds which are the primary structural compo-nents of organic molecules (Osborne et al. 1993). While thespectra may appear diffuse and featureless, they consist ofmany overlapped bands (Osborne et al. 1993) from whichcompositional information can be derived. Quantitative NIRmeasurements are usually based on the correlation betweensample composition, as determined by defined referencemethods, and the absorption of light at different wave-lengths in the near infrared region measured by either re-flectance or transmission spectroscopy. Chemometrictechniques more used are principal component analysis(PCA) and partial least squares (PLS) regression. Severalworks have been reported on the use of these techniques inenology for the determination of diverse compounds, likeanthocyanins (Dambergs et al. 2006; Janik et al. 2007),phenolic compounds (Cozzolino et al. 2004, 2008a; Ferrer-

Gallego et al. 2010), elements in wine (Cozzolino et al.2008b), correlation with sensory parameters in grape andwines (Cozzolino et al. 2008c; Le Moigne et al. 2008),aromatic compounds in wine (Garde-Cerdán et al. 2010;Lorenzo et al. 2009; Smyth et al. 2008), and multiparameteranalyses in grape and wines (Urbano-Cuadrado et al. 2004;González-Caballero et al. 2010; Wu et al. 2010).

In a previous work, the determination of the aromaticpotential in grapes by means of Fourier transform infraredspectrometry on an extract of grapes is described, and theconcentration of the different glycosylated compound groupsis determined (Schneider et al. 2004). In a more recent work,the values of G–G in the juice of the fruit correlate with thedata collected by NIR spectrometry (Cynkar et al. 2007). Thedata collected in these works demonstrate the potential of theinfrared spectrometry to classify the fruit according to theiraromatic potential. Nevertheless, the first method is complexbecause it includes an extraction stage, whereas the secondcorrelates with values of G–G which, as it said before, deter-mine the total of glycosylated compounds and not of thedifferent groups separately.

In the present work, the identification and quantificationof the bound compounds responsible for the varietal aromahave been performed on Tannat grape samples obtained onthe harvest of years 2007–2009. In addition, we examinedthe potential of NIR spectroscopy and chemometric techni-ques to determine the concentration of the different glyco-sylated aroma compounds present in Tannat grapes.

Materials and Methods

Grapes Samples

Fresh V. vinifera L. cv. Tannat grapes were sampled duringthe 2007, 2008, and 2009 harvest from vineyards of different

Table 1 Tannat grapes sampling during 2007, 2008, and 2009vintages

Region Harvest Total

2007 2008 2009

Las Brujas – 1 8 9

Canelon Chico – 3 3

Melilla 16 8 5 29

Pando – 2 – 2

Progreso 18 – – 18

Rivera 8 – 7 15

Santa Lucia – 1 – 1

Las Violetas 8 9 3 20

Total 50 24 23 97

Food Anal. Methods (2013) 6:100–111 101

Table 2 Identification of the glycosylated aromatic compounds

LRI Identity assignmenta Compound

Alcohols

1093 A 2-Methyl-1-propanol

1155 A 1-Butanol

1224 A 3-Methyl-1-butanol

1260 A 1-Pentanol

C6 alcohols

1368 A 1-Hexanol

1382 A (Z)-3-Hexen-1-ol

1410 A (Z)-2-Hexen-1-ol

Terpenes

1449 A trans-Linalool oxide (furanoid)

1474 A cis-Linalool oxide (furanoid)

1558 A Linalool

1718 A α-Terpineol

1739 B (Adams 2007; McLafferty and Stauffer 1991) trans-Linalool oxide (pyranoid)

1763 B (Adams 2007; McLafferty and Stauffer 1991) cis-Linalool oxide (pyranoid)

1810 A Nerol

1860 A Geraniol

1969 A Ho-diol I (trans-3,7-dimethyl-1,5-octadiene-3,7-diol)

2285 B (Adams 2007; McLafferty and Stauffer 1991) trans-8-Hydroxylinalool (trans-2,6-dimethyl-2,7-octadiene-1,6-diol)

2310 B (Adams 2007; McLafferty and Stauffer 1991) cis-8-Hydroxylinalool (cis-2,6-dimethyl-2,7-octadiene-1,6-diol)

2331 B (Adams 2007; McLafferty and Stauffer 1991) Geranic acid

2528 B (Versini et al. 1991) p-Menten-7,8-diol

Phenols

1753 A Methyl salicylate (methyl 2-hydroxybenzoate)

1855 A Guaiacol

1882 A Benzyl alcohol

1916 A β-Phenylethanol

2065 A o-Cresol

2090 B (Adams 2007; McLafferty and Stauffer 1991) p-Cresol

2180 A 4-Vinylguaiacol

2240 A 2,6-Dimethoxyphenol

2779 A Zingerone

2794 B (NIST Chemistry WebBook 2011) Ethyl-β-(4-hydroxy-3-methoxy-phenyl)-propionate

Norisoprenoids

2418 B (Uegaki et al. 1979) 3,4-Dihydro-3-oxoactinidol I

2458 B (Uegaki et al. 1979) 3,4-Dihydro-3-oxoactinidol II

2479 B (Uegaki et al. 1979) 3,4-Dihydro-3-oxoactinidol III

2537 B (Winterhalter 1990) 3-Hydroxy-β-damascenone

2651 B (Marais et al. 1992) 3-Oxo-α-ionol

2658 B (Marais et al. 1992) 4-Oxo-β-ionol

2694 B (Winterhalter 1990) 4-Oxo-7,8-dihydroxy-β-ionol

2726 B (Winterhalter 1990) 3-Oxo-7,8-dihydroxy-α-ionol

3167 B (Strauss et al. 1986) Vomifoliol

LRI lineal retention indexa A, identities confirmed by comparing mass spectra and retention times with those of authentic standards supplied by Aldrich (Milwaukee, WI) andFluka (Buchs, Switzerland); Ho-diol I was supplied by G. Versini (Istituto Agrario di San Michele all’Adige); B, identities tentatively assigned bycomparing mass spectra with those obtained from the literature

102 Food Anal. Methods (2013) 6:100–111

production regions of the Uruguay vineyard (Table 1). Thesamples were frozen and stored at −20 °C until their extractionfor aroma analysis and NIR spectroscopy measurements.

Spectroscopic Measurements

Fifty grams of seedless grapes was homogenized with aWaring lab mixer (Model DMC20; Waring Commercial,USA) in high speed (20,000 rpm) for 60 s and scanned in

reflectance mode using a 2-mm path length cuvette. Sam-ples were kept at 20 °C before scanning. The homogenizerobtained in the last step was centrifuged at 3,000 rpm for10 min followed by a filtration through glass wood. Theobtained solution was temperature equilibrated at 20 °C andscanned in transreflectance mode in a 1-mm path lengthcuvette. The spectrum was collected using a FOSS NIRSys-tem 5000 (FOSS NIRSystems, Silver Spring, MD, USA;1,100–2,500 nm). Spectral data collection was made using

Table 3 Mean value, minimum,maximum, standard deviation,and variation coefficient for thedifferent quantified compounds

0—concentration below limit ofquantification for thecorresponding compound

SD standard deviation (n097),CV coefficient of variation

Compound (μg/L) Min Max Mean SD CV(%)

1-Hexanol 16.31 290.77 85.83 42.87 50

(Z)-3-Hexen-1-ol 4.19 85.92 24.63 12.36 50

(Z)-2-Hexen-1-ol 1.84 124.94 12.99 14.38 111

Σ C6 alcohols 35.89 385.34 123.45 56.75 46

trans-Linalool oxide (furanoid) 0.63 37.14 18.17 8.22 45

cis-Linalool oxide (furanoid) 7.65 548.00 33.08 53.87 163

Linalool 0 59.74 6.59 10.04 152

α-Terpineol 0 36.17 13.59 7.69 57

trans-Linalool oxide (pyranoid) 7.38 102.49 32.97 16.95 51

cis-Linalool oxide (pyranoid) 20.22 191.25 65.23 29.16 45

Nerol 0 50.81 20.78 11.36 55

Geraniol 0 117.96 23.66 18.03 76

Ho-diol I (trans-3,7-dimethyl-1,5-octadiene-3,7-diol)

4.14 87.98 19.27 14.96 78

p-Menten-7,8-diol 0 250.36 32.20 40.20 125

Σ terpenes 98.59 719.89 265.54 121.12 46

Methyl salicylate (methyl 2-hydroxybenzoate) 0 63.69 3.97 10.46 264

Guaiacol 0 130.23 5.76 13.57 236

Benzyl alcohol 138.23 531.07 275.16 92.62 34

β-Phenylethanol 88.52 469.00 236.77 86.57 37

o-Cresol 0 26.24 3.19 3.68 115

p-Cresol 0 16.40 3.49 3.50 100

4-Vinylguaiacol 7.88 216.78 56.17 38.56 69

2,6-Dimethoxyphenol 0 32.81 4.15 6.21 149

Zingerone 7.10 402.38 78.08 70.19 90

Ethyl-β-(4-hydroxy-3-methoxy-phenyl)-propionate

11.90 453.59 77.91 78.73 101

Σ phenols 303.25 1,604.26 688.49 261.76 38

3,4-Dihydro-3-oxoactinidol I 1.38 52.96 13.17 10.27 78

3,4-Dihydro-3-oxoactinidol II 3.67 82.48 20.42 15.63 77

3,4-Dihydro-3-oxoactinidol III 3.83 196.35 30.06 27.29 91

3-Hydroxy-β-damascenone 6.45 275.40 93.52 66.58 71

3-Oxo-α-ionol + 4-oxo-β-ionol 52.32 1,707.65 294.26 282.27 96

4-Oxo-7,8-dihydroxy-β-ionol 16.56 626.76 129.85 131.01 101

3-Oxo-7,8-dihydroxy-α-ionol 21.38 772.91 166.56 154.92 93

Vomifoliol 0 637.64 128.41 155.97 121

Σ norisoprenoids 153.93 3,891.23 876.24 780.74 89

Σ phenols + norisoprenoids 469.41 5,217.16 1,564.74 978.01 63

Σ glycosidic precursors 669.51 5,937.05 2,009.89 1,052.50 52

Food Anal. Methods (2013) 6:100–111 103

NSAS software package (version 2.50; NIRSystems, SilverSpring, MD, USA) and stored as the logarithm of the in-verse of reflectance (Log 1/R) as the average of 32 scans.

Extraction of Grape Glycosides

Fifty grams of grapes without seeds was homogenized witha Waring lab mixer (Model DMC20, Warring Commercial)in high speed (20,000 rpm) for 60 s in presence of 50 mL ofwater. The solution obtained was shaken during 15 min in abath with cool water and then was centrifuged at 3,000 rpmfor 10 min follow by a filtration through glass wood. Isola-tion of the glycosidic compounds was achieved usingAmberlite® XAD®-2 polymeric adsorbent (50–80 meshsize), in a column containing 7 g of resin and sequentiallyconditioned with methanol (150 mL), ethyl ether (40 mL),and distilled water (100 mL). A sample of 100 mL of thesolution obtained as described above was applied (4–5 mL min−1), and washing the residual with 100 mL ofdistilled water. The free aroma components were eluted with50 mL of pentane to dichloromethane (2:1, v/v), and the

bound forms were eluted with 80 mL of ethyl acetate tomethanol (9:1, v/v). This last solution was evaporated todryness at the rotavapor, then dissolved in 6 mL of citratebuffer at pH 5, added with Cytolase PCL5 (Gist-brocades,Lille Cedex, France), and reacted at 40 °C for 16 h. After theaddition of the internal standard (1-heptanol at 230 ppm in a50 % hydroalcoholic solution), the aglycons were extractedthree times (4, 2, 2 mL) with pentane to dichloromethane(2:1, v/v), and the organic phase was dried with sodiumsulfate and concentrated to 0.5 μL on a small Vigreuxcolumn and further reduced to 100 μL prior to gas chroma-tography–mass spectrometry (GC-MS) analysis.

GC-MS Analysis of Aglycone Extracts

GC-MS analyses were conducted using Shimadzu QP 5050mass spectrometry, using a BP 20 (SGE, Ringwood, Australia)bonded fused silica capillary column (25 m×0.25 mm i.d.),coated with poly(ethylene glycol) (0.25 μm phase thickness),column temperature, 40 °C (8 min) to 180 °C at 3 °C/min, thento 220 °C at 5 °C/min, 220 °C (20 min) and then to 240 °C at20 °C/min; injector temperature, 250 °C; injection mode, split;split ratio, 1:40; volume injected, 2.0 μL; carrier gas, helium,92.6 kPa (55.9 cm/s); interface temperature, 250 °C; energy,70 eV; and acquisition mass range, 40–400 amu. The identifi-cation of the compounds was confirmed by injection of stand-ards or comparison of their retention index and mass spectrumreported in the literature (Adams 2007; McLafferty andStauffer 1991). The amounts of the identified compounds werecalculated as 1-heptanol equivalents, as previously described(Versini et al. 1994).

Chemometrics and Statistical Analysis

Statistical differences between harvest year and productionregion for glycosylated aroma compounds were assessed byanalysis of variance using the software Statistica 7.1 (Stat-Soft, Tulsa, OK, 1984–2005).

6% 13% 44% 34%0

200

400

600

800

1000

1200

C6 alcohols terpenes norisoprenoids phenols

gly

cosy

late

d c

om

po

un

ds

(µµg

/kg

)

Fig. 1 Mean values for the main groups of components present in theTannat grape samples analyzed during the 3 years. Bars correspond tothe standard deviation for each sample. In each bar the percentagerespect to the total glycosylated compounds is indicated

Table 4 Mean values and standard error for the concentration of glycosylated compound (in micrograms per kilogram) and sugar concentration (ingrams per liter) in the different vintage studied

Harvest N C6 alcohols Terpenes Norisoprenoids Phenols Σ glycosylated Sugar

2007 50 143±9 b 245±16 466±52 a 619±37 a 1,522±92 a 238±3

2008 24 116±7 a 280±20 1,273±144 b 708±45 ab 2,434±188 b 231±5

2009 23 88±6 a 295±32 1,354±213 b 819±54 b 2,627±286 b 227±3

Significance level –* ns –* –** –* ns

Different letters indicate concentration differences in LSD test (p<0.05)

N number of samples analyzed in each year, ns not significant

*p<0.001; **p<0.01, significant level

104 Food Anal. Methods (2013) 6:100–111

Spectra were exported from the NSAS format for chemo-metric analysis in The Unscrambler software (version 9.5;CAMO ASA, Norway). Two spectral pretreatment algo-rithms were used: the second derivative of the spectral datato correct baseline effects and enhance the separation ofoverlapped peaks, using the Savitzky–Golay transformationand smoothing (Hruschka 1992); or multiplicative scattercorrection (MSC), a transformation method used to com-pensate for additive and/or multiplicative effects in spectraldata (Wu et al. 2010).

PCA was used to examine any relevant and interpretablestructure in the data and to detect outliers (Naes et al. 2002).The spectra containing useful information were determinedas being the region between 1,200 and 2,000 nm, andtherefore, the calibration was performed using this region.Calibration models were developed using partial leastsquares regression (PLS1) with full cross-validation in orderto establish, for the first time, the potentiality of NIR tech-nique for quantification of glycosylated aroma compoundcompare with the traditional technique. The optimum num-ber of terms in the PLS calibration models was selected onthe basis of minimizing the standard error of cross-

validation (SECV). Statistical parameters calculated fromthe calibrations include the coefficient of determination incalibration (R2), the SECV and residual predictive deviation(RPD), defined as the ratio between the standard deviation(SD) of the population and the SECV for the NIR predic-tions. In order to study a possible colinearity between aromacompounds, a matrix correlation was performed using thedata analysis pack from Excel 2007 (Microsoft Inc., Red-mond, WA).

Results and Discussion

Identification of Glycosylated Compounds

The GC-MS analysis of different Tannat grapes samplesshowed that 39 glycosylated compounds, released by en-zyme hydrolysis, were identified: 7 alcohols, 13 terpenes, 10phenols, and 9 norisoprenoids (Table 2). The largest numberof compounds identified was terpenes, phenols, and noriso-prenoids, like those reported for other varieties (Wirth et al.2001; Bureau et al. 2000; Loscos et al. 2009).

Fig. 2 NIR spectrums obtained from homogenized samples (a), juices (d) and those obtained after spectra pretreatment algorithms, secondderivative (c homogenized samples and f juice) and multiplicative scatter correction (b homogenized samples and e juice)

Food Anal. Methods (2013) 6:100–111 105

The compound with retention index 2794 (Table 2)showed a mass fragmentation pattern similar to those ofzingerone. The mass spectrum obtained for this com-pound was m/z (rel. int.): 39(19), 41(16), 51(11), 55(18), 57(27), 65(9), 77(12), 91(15), 107(8), 122(8),137(100), 150(42), 153(9), 179(2), 224(M+, 18), wherethe mass ion (M+) presented 30 amu over zingerone(M+ 194), being tentatively identify as ethyl-β-(4-hydroxy-3-methoxy-phenyl)-propionate (NIST Chemistry WebBook2011). This compound has been recently reported in Chardon-nay wines (Liberatore et al. 2010), but to our knowledge, thisis the first time that it is reported as a glycosidic precursor ingrapes.

Quantification of Glycosylated Compounds

The mean, range, SD, and coefficient of variation (CV) for theconcentration of glycosylated aroma compound analyzed areshown in Table 3. Those compounds identified only in somesamples or present in very low concentrations were not in-cluded. The coefficient of variation was high for the quantifiedcompounds, a behavior which can be explained by the varia-tions produced in the different vintages and sampling regions.Interestingly the higher coefficients of variation found for theglycosylated compounds occurred in samples collected atharvest time, and with low dispersion in the sugar levelsvalues (mean 234 g/L, CV 8 %).

Fig. 3 Score plot of the first two principal components of the NIR spectrum for the samples of grape juice (a) and homogenized (b)

106 Food Anal. Methods (2013) 6:100–111

The quantitatively more important aglycones in the sam-ples of Tannat grape were the linalool oxides between theterpene compounds, the phenols benzyl alcohol, β-phenylethanol, zingerone and ethyl-β-(4-hydroxy-3-methoxy-phenyl)-propionate, and the megastigmane formsfor norisoprenoids (Table 3). As it is showed in Fig. 1,phenols and norisoprenoids were the quantitative most im-portant group of compounds, representing almost 80 % ofthe total glycosidic components present in Tannat grapes.

Moreover, the different biosynthetic origin (Versini et al.2008) allows to explain the low correlation (R200.464)

found for the content of those two groups of compoundsin the different samples analyzed, but also indicating theymust be quantified separately in order to better character-ize the grapes for vinification. Besides, the correlationbetween sugar and phenolic or norisoprenoid levels wasvery low (R200.011 and 0.043, respectively), demonstrat-ing the utility of the aromatic precursors analysis as ma-turity index in addition to the classical tests used inenology.

The variance analysis showed no significant differencesbetween the levels of the glycosylated aroma compounds

Fig. 4 Eigenvectors for the twoprincipal components of theNIR spectra for the samples ofgrape juice (a) andhomogenizated (b)

Food Anal. Methods (2013) 6:100–111 107

analyzed and the vineyard location (data not shown). By thecontrary, when the comparison was performed with vintageyear, the differences were significant for C6 alcohols, phe-nols, and norisoprenoids compounds (Table 4).

Near Infrared Spectroscopy

The NIR absorbance spectra of homogenized and grapejuice samples are shown in Fig. 2a, d, respectively. Similar-ly, Fig. 2b, e presents spectra after multiplicative scattercorrection, and Fig. 2c, f after second derivative treatment.These pretreated spectra were used for the calibration of theglycosylated aroma compounds in grape homogenized andgrape juice.

Figure 3 shows the score plots from the PCA analysis ofNIR spectra for homogenized and grape juice samples. ThePCA for the samples of grape juice showed a better expla-nation of the variance with the first two PC (84 %) than thatobtained when performing the analysis with the homoge-nized grapes (76 %). No separation in the score plot wasobserved related to region (data not shown), however sometrend was observed in the NIR spectra of samples collectedin different vintages (Fig. 3). The vintage influence cannotbe explained by ripeness differences in the samples collect-ed (Table 4). The climatic differences between the yearsprobably are reflected in the high loadings of the eigenvec-tors for the two PCs around 1,430 and 1,930 nm, relatedwith O–H overtones of water than can reflect differences inwater content between vintages (Fig. 4).

Calibration for the Glycosylated Compounds Using NIRSpectroscopy

Statistical parameters, calculated from the calibrations ofNIR data using either second derivative or MSC as mathe-matical pretreatment, are shown in Table 5. The coefficientsof determination values were low for the calibration modelsusing homogenized grape samples. Besides no differenceswere observed in the values obtained from the mathematicalpretreatment used. However, the SECV values were lower(up to 21 and 22 % for norisoprenoids and total glycosylatedcompounds, respectively) and RPD values higher whenMSC was applied. When grape juice samples were used,the coefficients of determination were lower for some rele-vant compounds (phenols, norisoprenoids, and total glyco-sylated compounds) when MSC was applied to thespectrum. The SECV values were lower (up to 36 % lowerin the case of norisoprenoid compounds), and RPD valueswere higher. In this work, the RPD values obtained rangedbetween 1.5 and 3.0 for the different groups of glycosylatedcompounds and total glycosylated compounds either in ho-mogenized or grape juice samples. In order to further inter-pret the NIR, calibration models developed in PLS loadingwere analyzed.

For glycosylated phenols the highest loadings were ob-served at 1,390–1,400 nm associated with O–H overtones ofalcohols (Cozzolino et al. 2007; Workman and Weyer2008), at 1,505–1,515 nm associated with either N–Hstretch first overtone or O–H stretch first overtone bands

Table 5 Statistical descriptors of NIR model calibration for the principal compounds groups analyzed in homogenized and grape juice (inmicrograms per kilogram of grape)

Glycosidic compound Spectralpretratment

Grape homogenized Grape juice

N PLSfactors

R2 SECV RPD N PLSfactors

R2 SECV RPD

C6 alcohols Second derivative 84 2 0.5471 38.8 1.5 82 3 0.5925 38.5 1.5

MSC 88 4 0.5347 39.3 1.7 86 4 0.5436 39 1.5

Terpenes Second derivative 88 5 0.6774 107.8 1.1 97 4 0.4407 124 1.0

MSC 89 4 0.4407 104 1.5 91 6 0.4736 96.1 1.5

Phenols Second derivative 87 2 0.4659 257.1 1.0 78 4 0.8155 258 1.0

MSC 85 4 0.5034 240 1.7 91 4 0.4049 217 1.6

Norisoprenoids Second derivative 86 2 0.6533 620.4 1.3 82 7 0.8111 814 1.0

MSC 76 4 0.6906 494 1.7 80 8 0.6953 522 1.8

Phenols +norisoprenoids

Second derivative 97 2 0.5716 858.4 1.1 83 7 0.8158 1,006 1.0

MSC 76 4 0.6514 674 1.8 74 7 0.5673 746 1.7

Σ glycosidic precursors Second derivative 87 3 0.6316 948.1 1.1 83 7 0.8159 1,101 1.0

MSC 77 4 0.6080 746 1.6 76 8 0.5767 823 1.8

Statistical descriptors—the concentration range used in the calibration model for the different groups of compounds is shown in Table 3

N number of samples, R2 coefficient of determination in calibration, SECV standard error of cross-validation (in micrograms per kilogram ofgrape), RPD residual predictive deviation

108 Food Anal. Methods (2013) 6:100–111

(Cozzolino et al. 2008a; Smyth et al. 2008; Workman andWeyer 2008), at 1,680–1,690 nm the C–H stretch firstovertone associated with aromatic compounds (Smyth etal. 2008; Workman and Weyer 2008), and around 1,850–1,870 nm associated with O–H hydrogen bonding between

water and exposed polyvinyl alcohol (Workman and Weyer2008). The last absorbance bands were similar to thosereported for lignin, 1,688 and 1,870 nm (Soukupová et al.2002), and explained by their relative similar chemical com-position; both contain aromatic rings as well as hydroxyl

Table 6 Full cross-validation statistics for glycosylated compounds measured in homogenized and grape juice by near infrared spectroscopy (inmicrograms per kilogram of grape)

Glycosidic compounds Grape homogenized Grape juice

N PLSfactors

R2 SECV RPD N PLSfactors

R2 SECV RPD

C6 alcohols

1-Hexanol 73 5 0.5230 30.6 1.4 85 4 0.5156 27.4 1.6

(Z)-3-Hexen-1-ol 88 1 0.2151 9.8 1.3 86 6 0.4683 9.6 1.3

(Z)-2-Hexen-1-ol 89 1 0.2402 5.5 2.6 86 1 0.2668 5.0 2.9

Terpenes

trans-Linalool oxide (furanoid) 97 5 0.4653 7.9 1.0 85 11 0.7089 7.3 1.1

cis-Linalool oxide (furanoid) 93 11 0.6695 9.9 1.1 92 6 0.5342 9.4 1.0

trans-Linalool oxide (pyranoid) 97 4 0.4476 16.4 1.0 – – – – –

cis-Linalool oxide (pyranoid) 97 5 0.4622 28 1.0 91 7 0.5105 25 1.2

α-Terpineol 91 2 0.2990 7.68 1.0 88 6 0.4973 7.3 1.1

Nerol 97 7 0.6017 10.2 1.1 81 5 0.5147 10.9 1.0

Geraniol 84 2 0.4147 13.5 1.3 83 9 0.6061 14.6 1.2

Ho-diol I 97 4 0.5450 13.7 1.1 88 3 0.3819 12.8 1.2

Linalool 73 4 0.5655 6.8 1.5 80 4 0.5269 7.4 1.4

p-Menten-7,8-diol 83 1 0.3430 26.2 1.5 87 6 0.4357 24.4 1.6

Phenols

Benzyl alcohol 82 3 0.4127 87.6 1.1 80 4 0.4858 88.1 1.1

β-Phenylethanol 90 1 0.2918 87.9 1.0 86 1 0.2057 85.6 1.0

4-Vinylguaiacol 88 1 0.1991 31.1 1.2 – – – – –

o-Cresol – – – – – 97 6 0.5487 3.4 1.1

p-Cresol – – – – – 88 6 0.3680 3.3 1.0

Guaiacol 91 1 0.2787 4.6 1.0 87 2 0.4126 4.5 1.0

2,6-Dimethoxyphenol 88 3 0.4097 5.3 1.2 86 3 0.3946 5.5 1.1

Zingerone 75 4 0.6605 45.2 1.3 76 6 0.4853 54 1.3

Ethyl-β-(4-hydroxy-3-methoxy-phenyl)-propionate

– – – – – 90 3 0.3573 69.6 1.1

Methyl salicylate 70 1 0.2153 3.2 1.0 79 7 0.6103 2.9 1.0

Norisoprenoids

3-Hydroxy-β-damascenone 79 4 0.6590 56.4 1.2 76 7 0.6549 58.1 1.1

3-Oxo-α-ionol + 4-oxo-β-ionol 78 4 0.6609 184 1.5 89 9 0.5981 125.6 2.2

4-Oxo-7,8-dihydroxy-β-ionol 80 4 0.6545 96.2 1.6 89 9 0.6644 102.7 1.5

3-Oxo-7,8-dihydroxy-α-ionol 78 2 0.4818 121.9 1.3 81 8 0.6239 188 1.1

Vomifoliol 76 4 0.7175 117 1.5 81 9 0.6938 144.7 1.1

3,4-Dihydro-3-oxoactinidol I 77 5 0.6432 8.2 1.5 73 6 0.6033 8 1.6

3,4-Dihydro-3-oxoactinidol II 78 5 0.6187 11.8 1.5 88 6 0.4928 13.1 1.2

3,4-Dihydro-3-oxoactinidol III 78 4 0.5606 21.0 1.3 86 9 0.5932 18.9 1.5

Only data for compounds with RPD01 or more are presented

N number of samples, R2 coefficient of determination in calibration, SECV standard error of cross-validation (in micrograms per kilogram ofgrape), RPD residual predictive deviation

Food Anal. Methods (2013) 6:100–111 109

groups which have very distinctive features in the NIR spectra(Cozzolino et al. 2008a; Soukupová et al. 2002). The regionaround 1,900–1,910 nm can be associated with C0O secondovertone in carboxylic acid or O–H (water) (Workman andWeyer 2008). For glycosylated norisoprenoids and total gly-colsylated compounds, similar PLS loadings were observed,possibly due to the importance of this group in total grapeglycosylated aromatic compounds. The highest loadings wereobserved at 1,360–1,380 nm associated with CH3 combina-tion tones (Smyth et al. 2008; Workman and Weyer 2008).The calibration for grape juice shows negative PLS loadings at1,390 nm associated with O–H overtones of alcohols(Cozzolino et al. 2007; Workman and Weyer 2008), at 1,470–1,490 nm also related to O–H stretch overtone in alcohols(Workman and Weyer 2008), and at 1,510–1,520 nm associ-ated with either N–H stretch first overtone or O–H stretch firstovertone bands (Cozzolino et al. 2008a; Smyth et al. 2008;Workman andWeyer 2008). Higher PLS loadings at 1,690 nmwere observed only for grape juice calibration, associated withthe C–H stretch first overtone in aromatic compounds (Smythet al. 2008; Workman and Weyer 2008), at 1,810–1,830 nmassociated with O–H stretching and C–O stretching combina-tion, while values at 1,875–1,885 nm were associated to O–Hhydrogen bonding between water and exposed polyvinyl al-cohol (Workman and Weyer 2008).

Table 6 shows the PLS calibration using MSC spectralpretreatment, which presented the best results for the cali-bration of grouped compounds, for individual glycosylatedcompounds studied in grape juice and homogenized. Highcolinearity is possible observed due to the similarity inbiochemical pathways from which these chemical com-pounds are formed. For studied this comportment, matrixcorrelation between the individual and group of compoundanalyzed was performed (data not shown). Only correlationvalues over 0.7 were observed for 1-hexanol and the sum ofC6 alcohols; trans and cis-linalool oxides (pyranoids) be-tween them and with sum of terpenes; and zingerone, ethyl-β-(4-hydroxy-3-methoxy-phenyl)-propionate, benzyl alco-hol, and β-phenylethanol with sum of phenol compounds,respectively. Conversely, all norisoprenoids between themand with their sum showed high correlation, and thereforeaffecting the discussion presented below for this group ofcompounds.

RPD values greater than 1.5 were found for five and fournorisoprenoids compounds, in homogenized and grape juicerespectively; therefore, it could be evaluated using NIR as ascreening technique tool. By contrast, none of the glycosy-lated phenols studied, the second quantitatively more im-portant group of compounds, showed RPD values greaterthan 1.5, possibly due to the presence of compounds withsimilar absorbance curves which could interfere, as antho-cyanins and tannins (Cozzolino et al. 2008a). A group ofcompounds can be found in high concentrations in Tannat

grapes (Boido et al. 2011). The results obtained suggestedthat NIR techniques might offer the possibility of being usedas a fast screening tool to analyze total and individualgroups of glycosidic aroma compounds in Tannat grapes.The PLS loadings used for the calibration model showedthat different wavelength regions can contribute to the cal-ibration model developed for the different group ofglycosides.

Conclusion

In this study the profile of glycosylated aroma compoundsliberated by enzymatic hydrolysis from Tannat grapes wasdeterminate. Glycosylated phenols as benzyl alcohol, β-phenylethanol, zingerone and ethyl-β-(4-hydroxy-3-methoxy-phenyl)-propionate, and the megastigmane noriso-prenoid forms were quantitatively the most important com-pounds. The spectral and chemometric techniques herestudied showed the potential of NIR spectroscopy to mea-sure glycosylated aroma compounds in Tannat grapes, sug-gesting that NIR technique can be used as a screening toolfor a fast analyze of the grape aroma potential.

However, the entire procedure will need to use an exter-nal set of data in order to provide a completely validatemodel. The application of such method would be ideal forfast grape screening purposes, contributing to the winemak-ing decisions, classification of the fruit, and perhaps matu-rity monitoring.

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