Fluorescence Fingerprinting of Bottled White Wines Can Reveal

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Fluorescence Fingerprinting of Bottled White Wines Can Reveal Memories Related to Sulfur Dioxide Treatments of the Must Christian Coelho,*,† Alissa Aron,† Chloé Roullier-Gall,†,‡,§ Michael Gonsior,∥ Philippe Schmitt-Kopplin,‡,§ and Régis D. Gougeon† †

UMR PAM Université de Bourgogne/AgroSupDijon, Institut Universitaire de la Vigne et du Vin, Jules Guyot, Dijon, France Research Unit Analytical BioGeoChemistry, Department of Environmental Sciences, Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany § Chair of Analytical Food Chemistry, Technische Universität München, Alte Akademie 10, 85354 Freising-Weihenstephan, Germany ∥ University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory, Solomons, Maryland 20688, United States ‡

S Supporting Information *

ABSTRACT: For the first time, Excitation Emission Matrix (EEM) fluorescence spectroscopy was combined with parallel factor statistical analysis (PARAFAC) and applied to a set of 320 dry white wines of the Chardonnay grape variety. A four component PARAFAC model (C1, C2, C3 and C4) best explained the variability of fluorescence signatures of white wines. Subtle changes were observed in EEMs of white wines from two different vintages (2006 and 2007), where different concentrations of sulfur dioxide (0, 4, and 8 g·hL−1) were added to the grape must at pressing. PARAFAC results clearly indicated that sulfur dioxide added to the must subsequently influenced white wine chemistry into three distinct sulfur dioxide dose-dependent aging mechanisms. For both vintages, C1 and C2 were the dominant components affected by sulfur dioxide and likely reacting with phenolic compounds associated with some presumably proteinaceous material. Distinct component combinations revealed either SO2 dependent or vintage-dependent signatures, thus, showing the extent of the complex versatile significance underlying such fluorescence spectra, even after several years of bottle aging. goal because it can reveal the presence of specific fluorophore groups as was shown in the case of grape maturity.7 Specific excitation/emission wavelength pairs are indicative of fluorescent molecules or classes of organic compounds, which include phenolic compounds, anthocyanins, stilbenes, aromatic amino acids, and vitamins and thiol compounds, as classically detected in conjunction with liquid chromatography (LC), by direct injection, or after derivatization.1b,7b,8 Wine proteins and enzymes such as thaumatin-like proteins, Chitinase, and invertase can also be detected due to their intrinsic fluorescence, which can be used to monitor thermal unfolding kinetics during haze formation.1c,9 Modern fluorometers have evolved and an excitation emission matrix (EEM) of fluorescence can now be obtained by acquiring successive excitation and emission spectra at multiple emission or excitation wavelengths, thus showing the bulk emitting compounds in the wine over the scanned wavelength range. EEMs can be viewed as genuine fingerprints

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ine is a complex matrix containing aromatic and nonvolatile organic metabolites surrounded by a subtle macromolecular pool of compounds.1 Chemical analyses of such matrices classically rely on combining separation techniques to spectroscopic detectors, to isolate and quantify specific families of compounds. Such approaches can target polyphenols,1a stilbenes,1b vitamins and amino acids,2 thiols,3 anthocyanins,4 or proteins.1c An alternative approach to assess wine molecular fingerprints consists of wine analysis by direct injection to high-resolution mass spectrometers.5 These nontargeted studies usually employ metabolomics or proteomics for the evaluation of the wine molecular diversity5 or macromolecular6 diversity, respectively. From an oenological point of view, these expensive techniques are often restricted to use in research laboratories, in contrast to more straightforward analytical tools required for rapid quality assessments during the different steps of the winemaking processes and throughout the shelf life of the bottle. In this regard, a low-cost, rapid, noninvasive, and very sensitive spectroscopic technique that instantaneously investigates the chemical diversity of wine would be beneficial. Fluorescence is a good candidate toward accomplishing this © XXXX American Chemical Society

Received: January 29, 2015 Accepted: July 19, 2015

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DOI: 10.1021/acs.analchem.5b00388 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

Figure 1. EEMs of white wines from the 2007 vintage, with must having been treated with 0 (A), 4 (B), and 8 g·hL−1 (C) of sulfur dioxide.

again until 60 min, with a 0.3 mL·min−1 flow rate. Injection volume was 5 μL. Excitation Emission Matrix Fluorescence. Wine fluorescence was acquired on an Aqualog (Horiba Jobin Yvon, Inc.) system for excitation wavelengths ranging from 225 to 600 nm (3 nm interval) and emission wavelengths spanning from 230 to 600 nm (3.22 nm intervals). The spectrofluorometer used a conventional right-angle optical setup. Wines were diluted in order to minimize inner filter effects and put in a water cooled 1 cm path-length quartz cuvette and temperature was held constant at 25 °C. A CCD sensor enabled a sensitive, accurate and fast operating analysis of emitting light. EEMs were corrected daily for their Rayleigh and Raman scattering, as well as inner filtering effect by using the functions provided within the FluorEssence software of the Horiba Aqualog system. The fluorescence intensity was normalized to a Starna 1 ppm quinine sulfate reference cell, which also was measured every day. PARAFAC Modeling. PARAFAC was used to model the data set of EEMs. It used an alternating least-squares algorithm to minimize the sum of squared residuals in a trilinear model.10 Each EEM was reduced into a set of trilinear terms and a residual matrix.

of the optically active molecular and macromolecular composition of wines. Exploring in detail the overall extent of EEM data requires the construction of a trilinear decomposition model via a parallel factor analysis (PARAFAC). PARAFAC decomposes the three-dimensional signal into a fixed number of statistical components, with specific contributions, called scores, describing more specifically the variability of all analyzed EEMs.10 Such a strategy has already enabled the differentiation of red wine samples by grape varieties, origin, and appellation.11 It has also been applied to detect brandy adulteration in mixed spirits.12 Recently the methodology was even applied to classify sherry vinegars as a function of their aging in oak barrels.13 In this study, and for the first time, an unprecedented PARAFAC model was built from a large series of 320 chardonnay white wines from Burgundy, and spanning vintages from 1934 to 2012. In order to further assess this PARAFAC model, we specifically analyzed a series of white wines from two vintages (2006 and 2007), resulting from experiments where the dose of added SO2 at pressing had been adjusted to 0, 4, and 8 g.hL−1.

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EXPERIMENTAL SECTION Wine Samples. A total of 320 chardonnay white wines from Burgundy from different appellations and vintages between 1934 and 2012 were carefully sampled in an inert atmosphere, and transferred to amber glass vials for analyses and the building of the PARAFAC model. 43 other chardonnay white wines from the Montagny appellation in Burgundy, from vintages 2006 and 2007, were also sampled and analyzed in order to assess the PARAFAC model. For each of the two vintages, wines originated from identical musts, which were treated with three different concentrations of sulfur dioxide (0, 4, and 8 g·hL−1) immediately after pressing. All wines were bottled after supplementation of a constant SO2 dose of 3.5 g.hL−1. Additional winemaking operations are described in detail in Figure S.I.1. A total of 43 bottles (17 for the 2006 vintage and 26 for the 2007 vintage) were analyzed for each dose of sulfur dioxide. Analysis of the Polyphenolic Composition. An Acquity Waters ultraperformance LC (UPLC) interfaced with a diode array (DAD) fluorescence detector was used to separate and quantify individual polyphenolic compounds in the white wine samples. The column was a BEH C18, 1.7 um, 2.1 mm × 150 mm, protected by a guard column packed with the same material. Column temperature was kept constant at 30 °C, and samples were held at 8 °C. An elution was applied starting isocratic from 100% A (ultrapure water, 0.1% formic acid) from 0 to 6 min and then it increased linearly over 56 min to 100% B (methanol, 0,1% formic acid), where it was held isocratically

N

xijk =

∑ aif bjf ckf

+ εijk

i = 1, ..., I ; j = 1, ..., J ;

f =1

k = 1, ..., K

where xijk is the fluorescence intensity of the ith wine sample at jth emission wavelength and kth excitation wavelength. The scores, aif, are directly proportional to the concentration of the f th fluorophore in the ith wine sample. The loadings, bif and ckf, represent, respectively, emission and excitation spectra for the nth fluorophore. The variable, N, represents the number of fluorophores. The residual εijk corresponds to the unexplained variation of the PARAFAC model. Our model was built with the recently developed toolbox drEEM 0.1.0 run on Matlab.14 Data was treated with a nonnegativity constrain and normalized after outlier tests. Models of 3, 4, 5, and 6 components were explored, but the best fit of the data was achieved by a four component model. The PARAFAC model was confirmed by modeling independent halves of the data set in a split half validation. Spectral loadings of six random splits are shown in Figure S.1.2. They were consistent with previous PARAFAC component spectra for wine samples11b,15 with no overlapping data. The script enables to incorporate the intensity of fluorescence at the maximum (“Fmax”) of each PARAFAC components. “Fmax” is obtained by multiplying the maximum excitation loading and maximum emission loading for each B


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Analytical Chemistry component by its score and presents the same intensity scale as the original EEMs. Statistical Analyses. Principal component analyses (PCA) was used to explore the relationships between wine phenolic compound contents (Table S.I.1) and SO2 concentrations at must pressing, Fmax PARAFAC components (Table S.I.2) and finally SO2 concentrations at must pressing, and the combination of wine phenolic compound contents and Fmax PARAFAC components and SO2 concentrations at must pressing. For the latter case, Fmax and concentration scales were considered to be similar. Therefore, their combination for the PCA analysis was done without normalization. Partial leastsquares discriminative analysis (PLS-DA) was used to validate the link of PARAFAC components and ratio combinations to sulfur dioxide concentrations applied on the musts at pressing. Two factors of the model were obtained through a crossvalidation. The PLS-DA model was validated by its R2X = 79.7% and R2Y = 89.7%, with a predictive ability Q2 of 87.4% (Figure S.I.6). PCA and PLS-DA were performed with the Origin Lab software.

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RESULTS AND DISCUSSION Wine Fluorescence. All 320 EEMs showed a broad intense fluorescence area spanning from 290 to 450 nm for the 250 to Figure 3. Plot of fluorescence intensities maximum of PARAFAC components C2 vs C1 and C4 vs C1 for white wines with three different doses of sulfur dioxide: 0 (squares), 4 (circles), and 8 g·hL−1 (triangles) for vintages 2006 (filled black symbols) and 2007 (red outlined symbols).

phenolic compounds in must that was less protected against oxidative browning.16 This affirmation was confirmed by the polyphenolic composition of corresponding wines (see Table S.I.1). Compounds such as caftaric acid, gentisic acid, chlorogenic acid, caffeic acid, tyrosol, gallic acid and protocatechuic acid were indeed found in higher amounts in the protected white wines (4 an 8 g·hL−1 of sulfur dioxide). Among them, gallic acid and protocatechuic acid, emitting at the 280/340 nm pair, are possible candidates to explain the decrease of fluorescence intensity in the nonprotected wine, since these compounds are very easily oxidized due to their vicinal hydroxyl groups.17 For other phenolic fluorophores, like gentisic acid or caffeic acid, other areas of the EEMs should be explored.11b Describing EEMs with single pairs of excitation/emission wavelengths would be minimalist. The broad fluorescent region in EEMs of white wines is much more complex due to several overlapping spectral bands of distinct fluorophores related to their concentration and their chemical environment. Further adding to this complexity, charge transfer processes could also take place as suggested previously for natural dissolved organic matter.18 PARAFAC Model Results. The statistical PARAFAC model enabled the statistical separation of fluorescent areas, or components, based on the variation of each excitation and emission wavelength for all EEM data. The PARAFAC model was tested using different numbers of components, and we showed that four components were appropriate to interpret the variability of the 320 EEMs. The four major components, C1, C2, C3, and C4, are described in Figure 2. The four-component PARAFAC model explained 99.6% of the variability in the 320 EEMs. Even if a core consistency of

Figure 2. EEMs of the four components that comprise the PARAFAC model applied to 320 white wines of the Chardonnay grape variety.

325 nm excitation wavelengths. Three EEMs are shown in Figure 1 and are representative of EEMs of white wines produced from musts that had received 0, 4, and 8 g·hL−1 of sulfur dioxide at pressing in 2007. The maximum excitation wavelength was around 280 nm corresponding to an emission wavelength maximum at about 340 nm and with a shoulder located at 300 nm. In terms of the location of the excitation/emission wavelength pair, very little difference was observed between sulfur dioxide doses, meaning that the global composition of individual fluorescent compounds was approximately the same and independent of the initial sulphite concentration. The application of sulfur dioxide did not seem to form additional fluorescent compounds compared to musts treated with 0 g·hL−1. However, a decrease in the intensity of the 280/340 excitation/emission pair with the decrease of the added sulfur dioxide was noted. Such an observation could be related to the degradation of some C


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Figure 4. Evolution of the fluorescence intensity maxima ratios C1/C2 and C1/C4 over varying sulfur dioxide doses for wines from the vintages 2006 (filled black symbols) and 2007 (red outlined symbols) (A). Partial least squares discriminative analysis (PLS-DA) of white wines with three different doses of sulfur dioxide: 0 (squares), 4 (circles), and 8 g·hL−1 (triangles) for vintages 2006 (filled black symbols) and 2007 (red outlined symbols). Score and loadings plots of the PLS-DA analysis of the PARAFAC component Fmax and of PARAFAC components Fmax ratios depending on the sulfur dioxide concentration (B).

Fluorescence intensity maxima (Fmax) spanned from 3.3 to 5.5 and from 9.5 to 11.1, respectively, for C1 and C2, excepting the value of 5.46 for C2. The highest Fmax values of C1 were found for protected musts (8 g·hl−1), meaning that this component was the most highly expressed and preserved upon sulfur dioxide additions. This validated our previous observation of a higher intensity of the 280/340 excitation/emission pair for white wines with highest values of sulfur dioxide, indicating a better initial preservation of musts polyphenolic compounds from browning oxidation. From Figure 3, it appeared that the PARAFAC model enabled to detect three clusters of white wine samples in function of the sulfur dioxide dose applied at the pressing stage. Clusters were the most evident with the representation of C4 vs C1, with an additional vintage cluster visualized by higher values of C4 intensities, from 1.05 to 1.15, always higher in 2006 compared to 2007. These discriminations were also visualized in the four other representations of C3 vs C1, C3 vs C2, C4 vs C2, and C4 vs C3, as represented in Figure S.I.4. This suggested that the initial sulfur dioxide addition to the must subsequently had an impact on the way white wine compounds evolved upon aging. This was not the case for white wines made from musts that were not protected in 2006 and 2007 (0 g·hL−1). These white wines presented C1 and C2 fluorescence intensity maximum values that were very close from one another, with Fmax values comprised between 10 and 10.5. Such observation could be interpreted as a similar

64.4% was found, the model was split half validated with six independent splits (Figure S.I.2). The dominant component C1 presented a maximum emission ranging from 330 to 370 nm at excitation 280 nm. C2 and C3 were represented by excitation/emission wavelength couples of 280/305 and 280/ 320 nm, respectively which likely presented tyrosine-containing proteinaceous signatures, but further studies would be needed to unravel the significance behind these two components and why they were nicely separated by the model. C4 showed a broad emission from 400 to 500 nm with excitation wavelengths ranging from 250 to 380 nm. This C4 component matched well with the fluorescence of individual molecules such as gentisic acid, flavonols such as quercetin, quercitrin, or kaempferol, vitamins like riboflavin or even more condensed structures involving quinone moieties.11,15,19 However, it must be stated here that PARAFAC components were statistically derived and their chemical meanings are not conclusive (see Figure S.I.3.C). Differentiation between Sulfur Dioxide Additions to the Must and Bottle Storage Evolution of the Wine. The next step of our investigation consisted in analyzing the fluorescence intensity maximum of each statistical component for the 43 white wines from vintages 2006 and 2007. Data are shown in the Supporting Information (Table S.I.2.). Six pairs of components could be plotted in order to investigate the relationships between the four PARAFAC components. Two of them are plotted in Figure 3: C2 vs C1 and C4 vs C1. D



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oxidability of the must compounds between the two vintages (similar intensities for C1) and also by a very slow evolution of the related fluorescent compounds upon bottle storage. In order to analyze in more detail the effects of sulfur dioxide doses on the evolution of white wines, the evolution of six distinct PARAFAC component Fmax ratios were plotted as a function of SO2 added concentrations. Two of them, C1/C2 and C1/C4, presented good correlations with sulfur dioxide additions (R2 = 0.80 and R2 = 0,90, respectively; Figure 4A). The C2/C4 and C1/C3 ratios, not presented here, also exhibited some correlation to sulfur dioxide concentrations (Figure S.I.5). Such ratios indeed reveal better correlations, likely related to the integration of the relative contribution of PARAFAC components associated with other fluorescent components that have different excitation and emission maxima. Interestingly, the two other ratios C2/C3 and C3/C4 ratios were not or only poorly affected by sulfur dioxide addition to the must (R2 < 0.5) and ratio values were higher in 2007 than in 2006, except for C2/C3 ratio that seemed to be independent of sulfur dioxide additions and vintages. In order to statistically validate relationships between Fmax ratios and sulfur dioxide concentrations, a partial least-squares discriminative analysis was done using SO2 concentrations as the variables for the PLS-DA model (Figure 4B). The first two factors explained 79.8% of the variance, with the first axis, clearly correlated to the SO2 concentration added at pressing, in particular, for the 2007 vintage. Furthermore, the C1/C4 Fmax ratio showed the highest correlation with the SO2 addition at pressing, consistently with Figure 4A. Similarly, the C2/C4 Fmax ratio and the C1 Fmax exhibited some SO2 additions dependency, with a more significant correlation for the 2007 vintage (Figure S.I.5). Therefore, these results revealed that the use of different concentrations of sulfur dioxide at pressing changed the initial fluorophore composition, which evolved upon bottle aging, leading to higher amounts of fluorophores described by C4 compared to C1, C2, and C3. This effect might occur during bottle storage and would be independent of the different amounts of sulfur dioxide used for must protection, for any vintage. These results further confirm previous observations of the reminiscent complex chemical signatures, as revealed by ultrahigh resolution mass spectrometry.5

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Article

ASSOCIATED CONTENT

S Supporting Information *

Information about Figure S.I.1 on winemaking operations, Figure S.I.2 on the spectral loadings of the four component PARAFAC model, Table S.I.1 on the quantification of polyphenols, Table S.I.2 on the four PARAFAC components Fmax values for the 43 white wines, Figure S.I.3 on the statistical discrimination of the 43 white wines by means of principal component analysis based on PARAFAC components Fmax, polyphenol concentrations and a combination of both, Figure S.I.4 on Fmax PARAFAC components combinations: C3 vs C1, C3 vs C2, C4 vs C2, and C4 vs C3, and Figure S.I.5 on the PARAFAC components Fmax ratios C1/C3, C2/C3, C2/C4, and C3/C4 in function of the sulfur dioxide dose applied at pressing. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.5b00388.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +33 380396195. Fax: +33 380396265. E-mail: christian. [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are very grateful to the wine producers who provided the samples, Domaine Sauvestre, Domaine des Comtes Lafon, Chateau de La Velle, Maison Drouhin, and Maison Bouchard, and our financial supporters, Conseil Régional de Bourgogne (FABER), Bureau Interprofessionnel des Vins de Bourgogne (BIVB), Comité Interprofessionnel des Vins de Champagne (CIVC), and the Unesco Chair “Culture and Traditions of Wine”. This is contribution N° 5053 of the University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory.

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REFERENCES

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CONCLUSIONS

In this paper, we introduced the PARAFAC model built by means of 320 different white wines from Chardonnay grape variety from the Burgundy area. We found that four components were able to precisely describe the variability of all of the wines. We applied our PARAFAC model to a subset of 43 white wines from two different vintages, produced from musts, which had received three different concentrations of sulfur dioxide at the pressing stage of grapes. PARAFAC component C1 and ratio of components C1/C4 in particular were dependent on initial sulfur dioxide concentrations, with distinct variation for distinct vintages. Therefore, these results showed for the first time that the subtle fluorescent fingerprints of wine chemistry are maintained during bottle storage and can be revealed and decomposed into memories of either oenological practices or vintage related metabolic signatures. E


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Fluorescence Fingerprinting of Bottled White Wines Can Reveal

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