Using pH variations to improve the discrimination of wines by 3D front face fluorescence spectroscopy associated to Independent Components Analysis

Author’s Accepted Manuscript Using pH variations to improve the discrimination of wines by 3D front face fluorescence spectroscopy associated to independent components analysis Rita Saad, Delphine Jouan-Rimbaud Bouveresse, Nathalie Locquet, Douglas N. Rutledge www.elsevier.com/locate/talanta

PII: DOI: Reference:

S0039-9140(16)30150-3 http://dx.doi.org/10.1016/j.talanta.2016.03.023 TAL16404

To appear in: Talanta Received date: 13 November 2015 Revised date: 3 March 2016 Accepted date: 5 March 2016 Cite this article as: Rita Saad, Delphine Jouan-Rimbaud Bouveresse, Nathalie Locquet and Douglas N. Rutledge, Using pH variations to improve the discrimination of wines by 3D front face fluorescence spectroscopy associated to independent components analysis, Talanta, http://dx.doi.org/10.1016/j.talanta.2016.03.023 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Using pH variations to improve the discrimination of wines by 3D front face fluorescence spectroscopy associated to Independent Components Analysis Rita Saada,b, Delphine Jouan-Rimbaud Bouveressea,b, Nathalie Locqueta,b, Douglas N. Rutledge a

a,b*

AgroParisTech, UMR1145 Ingénierie Procédés Aliments, F-75005 Paris (France)

b

INRA, UMR1145 Ingénierie Procédés Aliments, F-75005, Paris (France)

*

Corresponding author. Address:16 rue Claude Bernard, 75005 Paris, France. [email protected]

ABSTRACT Wine composition in polyphenols is related to the variety of grape that it contains. These polyphenols play an essential role in its quality as well as a possible protective effect on human health. Their conjugated aromatic structure renders them fluorescent, which means that 3D front-face fluorescence spectroscopy could be a useful tool to differentiate among the grape varieties that characterize each wine. However, fluorescence spectra acquired simply at the natural pH of wine are not always sufficient to discriminate the wines. The structural changes in the polyphenols resulting from modifications in the pH induce significant changes in their fluorescence spectra, making it possible to more clearly separate different wines. 9 wines belonging to three different grape varieties (Shiraz, Cabernet Sauvignon and Pinot Noir) and from 9 different producers, were analyzed over a range of pHs. Independent Components Analysis (ICA) was used to extract characteristic signals from the matrix of unfolded 3D front-face fluorescence spectra and showed that the introduction of pH as an additional parameter in the study of wine fluorescence improved the discrimination of wines.

Keywords: Wine; front face fluorescence spectroscopy; polyphenols; pH; Independent Components Analysis

INTRODUCTION: Red wine is one of the most widely consumed alcoholic beverages in the world. It is essentially a solution of 10% to 15% ethanol in water. Wine pH is usually between 3.2 and 4. Wine composition varies widely: it contains sugars, acids and volatile compounds, phenolics and inorganic compounds. The composition is highly dependent on factors such as grape

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variety, maturity at harvest, soil type, climate conditions, winemaking processes ... so that each type of wine is different and has its own characteristics. Red wine is very rich in polyphenols, such as flavanols (catechin, epicatechin, etc.), flavonols (quercetin, rutin, myricetin, etc.), anthocyanins (of which malvidin- 3-O-glucoside is the most abundant), oligomeric and polymeric proanthocyanidins, phenolic acids (gallic acid, caffeic acid, p-coumaric acid, etc.), stilbenes (trans-resveratrol) and many other polyphenols. Polyphenols play an essential role in wine sensory quality (flavor, color, astringency) and are thought to have a possible protective effect on health (antioxidant activity) [1- 3]. Anthocyanins are primarily responsible for the color of red wines, as they contain the flavylium skeleton where the aglycone moiety is the chromophore. The color of wine depends on the nature of the substituants (OH, CH3) on the central ring of the aglycone. The color of wine is also pH dependent and varies also with copigmentation, metal ion chelation and interactions with ethanol [4, 5]. Anthocyanins are particularly sensitive to pH changes: at pHs below 2, anthocyanins are mainly red in the form of flavylium cation (A +). As the pH increases, the concentration of A+ decreases and that of the colorless carbinol base (AOH) increases. From pH 4.5 upwards, concentrations of the blue-violet chalcone and quinone forms increase. However, at pH 3.2, most anthocyanins are colorless [6]. Anthocyanins tend to bond to other phenolics (including other anthocyanins), which increases color strength (copigmentation) and stability; flavan-3-ols are responsible for browning in wine and their reaction with anthocyanins stabilizes the color of wine [7]. The copigments may be flavonoids, alkaloids, amino acids, organic acids or metal ions. In addition to the effect of pH on the color of anthocyanins [8], the effect of pH on the antioxidant behavior of different polyphenols has also been studied [9, 10] and it has been shown that the pH-dependent behavior is related to hydroxyl deprotonation and that the position and the total number of hydroxyl and methoxyl groups influence the magnitude and mechanism of the antioxidant activity. Several studies have shown the effect of pH on the color and the antioxidant activity of wine, but none with the objective of using these phenomena to facilitate the authentication of wine types. Different simple and non-destructive spectroscopic techniques have been developed to facilitate the authentication of food products. Fluorescence spectroscopy is one of these techniques: it is fast, sensitive and can provide much information on the composition and characteristics of food products [11, 12]. 2

The aim of the present investigation was to assess the potential of three-dimensional front face fluorescence spectroscopy and chemometric data analysis methods to discriminate wine samples and to identify wines according to their varieties. To do this, Independent Components Analysis (ICA) [13- 17] was applied to the fluorescence spectra. Wine polyphenols contain conjugated aromatic structures that render them fluorescent when exposed to an adequate light source. Although the fluorescence analysis of wine at its natural pH does not give a clear discrimination among different wines, it may become possible, by using pH variations, to induce alterations in the structure of polyphenols which could then results in different changes in their fluorescence spectra [12]. The goal of the current study is to see whether it is possible, using pH modification effects as a supplementary controlled source of variation in the 3D-fluorescence spectra, to facilitate the authentication of wine types.

Materials and methods. 1. Sample preparation

Experiments were done to answer three different questions: First, is the reaction of wines towards pH modification punctual or does it evolve over time? Second, in which cases are changes in the structure of studied molecules reversible or not? And finally, can the introduction of the change in pH of the samples lead to improved discrimination among different wine types?

1.1. Study of the influence of pH on the variation of the fluorescence over time

A single wine was used for this preliminary study, namely the Shiraz 2009 (Monoprix brand), and the contents of seven bottles was homogenized and distributed in flasks kept under argon at low temperature (5°C). The wine was analyzed at different pH values ranging from 1 to 10. The pH between 3 and 4 is the natural pH of the wine and at this pH the wine fluorescence spectra are stable. To change the pH, a few µl of a solution of NaOH (5M) or HCl (37%) were added to 100 ml of wine. 3D spectra were then recorded at each pH, every 30 minutes from 0 to 2 hours on the first day, and after 24 hours, 48 hours, and one week. 3

1.2. Study of the modifications of fluorescence intensity of polyphenols as a function of changes in structure due to pH changes.

For this second preliminary study a single type of wine from a single grape variety was used; the Grenache noire 2009 (Monoprix brand). The wine was analyzed at different pHs ranging from pH 3.6 to 10, then back down to pH 1 and then back up to the pH of the wine. The experiments were also done in reverse, going from 3.6 down to 1, then back up to 10 and then back down again to 3.6. To change the pH, a few µl of a solution of NaOH (5M) or HCL (37%) were added to 100 ml of wine.

1.3. Study of the influence of pH on the discrimination of different grape varieties by 3D fluorescence spectroscopy.

Wines from 3 different grape varieties and 3 different producers each were used in this study. All wines originate from the south of France. Grapes varieties are - Shiraz: Monoprix 2009 (S1), Gérard Bertrand 2010 (S2) and Ardèche 2010 (S3). - Cabernet-Sauvignon: Monoprix 2010 (C1), Jean Baptiste Bejot 2010 (C2) and Gérard Bertrand 2010 (C3). - Pinot noir: Monoprix 2010 (P1), Jean Baptiste Bejot 2010 (P2) and Experts Club 2010 (P3). Each of these 9 wines was adjusted to 3 different pHs (4, 7 and 8) by adding a few µl of NaOH (5M) to 30 ml of wine, and the 3D fluorescence spectra for each wine at each pH were recorded every thirty minutes over two hours, the acquisition of each spectrum taking approximately 25 minutes.

Wine samples were placed in a 3 ml quartz cell. During all experiments, the temperature of the samples was maintained between 22 and 23 °C.

2. Equipment and acquisition of 3D fluorescence spectra

When the sample is opaque, its absorbance is high, so that the emitted fluorescence may be reabsorbed by the sample leading to a decrease in measured fluorescence intensity. In this case, front-face fluorescence spectroscopy is used because only the surface of the sample is

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examined (Genot et al., 1992)[18]. This method is simple and fast because it does not require the dilution of the sample. 3D spectra provide an overview on the fluorescence over a range of excitation and emission wavelengths. This facilitates the detection of the fluorescence peaks corresponding to the different fluorophores present in the sample. 3D Fluorescence spectra were acquired with a Xenius spectrofluorimeter (Safas, Monaco) equipped with a xenon lamp source, excitation and emission monochromators and a front-face sample cell holder. Measurements were carried out using quartz cuvettes in front face mode. The spectra acquisition settings were: bandwidths 10 nm, excitation wavelengths 269–490 nm (1.1 and 1.2) and 250-490 (1.3), with a step of 3 nm, and emission wavelengths 286–540 nm (1.1 and 1.2) and 286-520 (1.3) with a step of 3 nm. A photomultiplier (PM) voltage of 540 V (1.1 and 1.2) and 620 V (1.3) was used to avoid detector saturation. Also, to avoid interference from Rayleigh scattering which occurs when the emission wavelength is equal to or doubles the excitation wavelength, the ‘‘Forcing’’ option was used in order to limit the emission range so that data acquisition started 25 nm beyond the excitation wavelength. In the first two experiments, (1.1, 1.2) the intervals used were those found in the literature. As an interesting signal was observed at an excitation of 269 nm, the excitation range was extended to begin at 250 nm (1.3). Similarly for the emission range: as there were no interesting signals after 520 nm, the range was decreased.

All measurements were performed over a short period of time (1 week) to minimize the effect of instrumental fluctuations (e.g., lamp intensity).

3. Independent Components Analysis (ICA). The goal of ICA (Independent Components Analysis) is to extract pure underlying source signals from a set of mixed signals (mixed signals are considered as weighted sums of pure source signals [19]), as well as their proportions, or contributions to each mixture. To do this, ICA performs a linear transformation of the original data to calculate components that are statistically independent. The ICA algorithm JADE (Joint Approximate Diagonalization of Eigenmatrices) [13, 14, 20] was used to analyse the unfolded cube of excitation-emission matrices (EEMs).

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An essential step in the application of ICA is the determination of the optimal number of independent components (IC) to be extracted. This was done using the method called "ICA_by_blocks" [15] which consists in dividing sample-wise the matrix of measured signals into two representative blocks, and then calculating for each block several ICA models, with increasing numbers of ICs. The correlations between pairs of independent components extracted from the two blocks are then calculated, and sorted in decreasing order. Significant ICs should be found in both blocks, and will therefore lead to high correlations, while noiserelated ICs will be different in the two blocks, leading to low correlation values. The largest number of ICs yielding a high correlation is regarded as the optimal number. Since the results may be influenced by the distribution of the measured signals into the two blocks, the calculation is repeated a large number of times with different random distributions of the samples between the blocks.

Software Matlab Version 7.5.0.342 (R2007b) was used to do the chemometric treatments, with inhouse functions [13, 30].

Results and discussion 1) Study of the influence of pH on the variation of the fluorescence spectra over time

The 3D spectra are initially arranged in a cube with dimensions 72×74×84 (Number of samples × Number excitation wavelengths × Number of emission wavelengths). To apply Independent Components Analysis, the cube must be unfolded into a rectangular matrix of dimensions 72 × (74 × 84) = 72×6216. Figure S-1 shows the boxplot of values of the correlations calculated between the different ICs of the 2 blocks with 100 different random distributions of the samples into the 2 blocks. For this set of experiment, a model with 7 ICs was found to be optimal. After calculating the ICs, the 7 source signals were folded back to give matrices similar to the initial excitation-emission signals. 6

ICA applied to the fluorescence spectra facilitates the extraction of 7 pure signals of 7 different fluorophores. As an example, the source signal obtained for IC6 is presented in Figure 1. This corresponds to a single fluorophore at λex / λem = 480nm / 520nm and an examination of the original spectra show that it has maximal fluorescence intensities at pH 8 and 9. At the same time the effect of pH change on individual polyphenols was studied. Solutions of gallic acid, resveratrol, catechine, epicatechine and quercetin, in tartaric acid based synthetic wine were diluted in buffers at 3 pHs (4, 7 and 8). Figure S-2 shows the example of quercetin. It can be seen that quercetin is not fluorescent at pH 4; however, when left for 24 hours at pH 8, deprotonation of quercetin gives rise to a fluorescent compound. The observed spectral characteristics (excitation/emission wavelengths : 480/520 ) of the quercetin solution and the intensity variations with pH seem to be compatible with the results in Figure 2 where IC6 has a higher intensity at pH 8 than pH 7 or pH 4. By observing the evolution of the fluorescence intensity of the fluorophore extracted in IC6 as a function of pH (Figure 2), it can be seen that it is stable at acid pHs, and progressively increases in intensity at basic pHs (pH 8 and 9). These changes in intensity of the fluorophore in question may be due to the structural changes in the molecule over time, leading to the formation of new more fluorescent molecular structures at basic pHs. In fact the quercetin molecule has in its structure two aromatic rings with two hydroxyl groups and an unsaturated heterocycle with a hydroxyl group and a carbonyl group. This carbonyl group is directly linked to the heterocyclic ring which enhances the mesomeric attractive effect of the–M carbonyl. Initially this molecule presents very low fluorescence intensity in aqueous solution [21, 22]. Furthermore it has been demonstrated [23-25] that hydroxyflavones have acidic protons depending on the position of the hydroxyl group. Following the order of increasing pKa [23, 26] for each of those groups, it has been demonstrated [21] that from quercetin at pH 10 and following a reaction of deprotonation, trianionic quercetin and tetra-anionic quercetin can be formed (Figure S-3). Deprotonation induces a delocalisation of the conjugated bonds which are responsible for the fluorescence of the molecule; this delocalization results in a molecule with a planar structure and thus with a higher fluorescence intensity.

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Figure 2 shows that time is also an important parameter for the study of the acido-basic reactions because the changes in fluorescence as a result of changes in molecular structure do not occur instantaneously. Figure 2 shows that the intensities for time delays beyond 120 minutes do not evolve uniformly as a function of time. For this reason, the following analyses will be limited to spectra acquired every 30 minutes up to two hours.

2) Study of the modifications of fluorescence intensity of wines as a function of pH. 21 spectra, corresponding to 21 different steps of pH modification, were acquired and a model with 6 ICs was found to be optimal by ICA_by_Blocks. Each independent signal and its corresponding proportions are presented in Figure 3. The proportions given by ICA have been corrected for the dilutions due to addition of the NaOH and HCl solutions. The numbers beside each point correspond to the order in which the spectra were acquired. The symbol ● shows the evolution of proportions of the fluorophore when pH goes from 3.6 to 10 and then down to 1 and then back up to 3.6; while ■ shows the evolution of proportions of the fluorophore when pH goes from 3.6 down to 1, then up to 10 and back down to 3.6. This series of experiments shows that the pH-dependent structural changes of some fluorophores are reversible when bringing the sample back to its original pH, whereas other fluorophores remain partially modified and do not regain their original structure. This can be seen by the difference in the fluorescence intensity of the fluorophores at a given pH after having been submitted to pH changes. As can be seen in Figure 3 (symbol ■), when going from pH 3.6 down to 1 and then back up to pH 3.6 all fluorophores recover their initial intensity. However, certain fluorophores have decreased intensity after going through pH 10. IC1 shows several fluorescence peaks (λex / λem = 290nm / 360nm, λex / λem = 310nm / 430nm and λex / λem = 380nm / 470nm) that behave the same way in relation to the modification of pH. The fluorescence intensity of these fluorophores increases at alkaline pH with a maximum intensity at pH 8, whereas it decreases sharply at acid pHs. It can also be seen that the pH-dependent changes in their intensity are reversible when returning the sample back to its original pH; so it can be concluded that the modifications in structure of these molecules are reversible. 8

IC2 shows a single fluorophore at (λex / λem = 360nm / 410nm). The signal of this constituent is most intense near pH 5. The passage through pH 10 results in a reduction in its fluorescence intensity when returning to lower pH values; for example, a decrease in proportion from about 9 to 7 at pH 5. After passing through pH 1, the reaction is reversible and the fluorescence intensity at each pH comes back to the same value as before that passage. IC3 shows a fluorophore at (λex / λem = 320nm / 400nm) with maximal fluorescence intensity near the natural pH of wine (pH 4) and with the same behavior as the fluorophore in IC2. The structure of this molecule is in large part irreversibly modified by a passage through pH 10 (for example, its proportion at pH 5 drops from about 6 to about 2.5), whereas changes due to passage through an acid pH are reversible. IC4 show a single fluorophore at (λex / λem = 430nm / 450nm) with maximal fluorescence intensity between pH 7 and 8 and very low intensities at acid pHs. Going from 3.6 to 10 and then back down (●)somewhat increases the fluorescence, especially at the maximum around pH 8. Going from 3.6 down to 1 and then up to 10 and back down (■), increases the signal much more, again with a maximum near pH 8. As well, the maximum intensity for IC4 shifts to slightly lower pHs after passage through pH 10. This fluorophore has very weak fluorescence signals at the initial pH of the wine. After undergoing structural changes at alkaline pHs, it does not recover its initial form and, although its maximum is at a pH only slightly lower, its intensity is higher. IC5 shows a fluorophore at (λex / λem = 285nm / 312nm). This fluorophore is weakly fluorescent initially (near pH 4) and is slightly more intense at more acid pHs. As with the other ICs, the modifications in structure at acid pHs are reversible. When passing through pH 10, the signal intensity decreases, especially after an initial passage through pH 1. IC6 shows a single fluorophore at λex / λem = 480nm / 520nm with very low intensities at acid pHs and maximal fluorescence intensities at pH 8 before passage through pH 10, but at pH 7 after passage through pH 10. This fluorophore has very weak fluorescence signal at the initial pH of the wine. After undergoing structural changes at alkaline pHs, this fluorophore does not regain its initial form and, although its maximum remains close to the same pH, its intensity is higher. Table 1: Comparison between literature values of different polyphenols [11, 27, and 29] and experimental values obtained for the ICs.

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IC 1 2 3 4 5 6

Experimental values (nm) 290/360 360/410 320/400 430/450 285/312 480/520

Polyphenols Anthocyanins Coumaric acid Trans-resveratrol

Literature values (nm) 280/355 348/410 318/390

Catechine Quercetin

279/317 427/480

Based on the results of different publications [11, 27, 29], it is possible to propose identifications for some of the fluorophores extracted by ICA (Table1). One of the fluorophores in IC1 would correspond to anthocyanins, IC2 would correspond to p-coumaric acid, IC3 to trans-resveratrol; IC5 to catechine, and IC6 to quercetine. Since the fluorescence of molecules differs from one chemical environment to another, and therefore depends strongly on conditions such as the pH, temperature, etc., this identification needs to be confirmed in subsequent work by LC-MS. This section has shown that the intensity of the fluorescence signals of wine polyphenols varies with the pH and that some fluorophores with low intensity at the normal pH of wine give much more intense signals at high pH values. For that reason, spectra acquired at different pH values may contain extra information that could be helpful in the discrimination of wines based on their polyphenol composition.

3) Study of the influence of pH on the discrimination of different grape varieties by 3D front face fluorescence spectroscopy.

A first attempt was made to discriminate the fluorescence spectra of the wines from three grape varieties without changing the pH. ICA_by_Blocks applied to the matrix of unfolded spectra of the 45 wine samples (3 grape varieties ´ 3 brands each ´ 5 different times) gave an optimal number of 7 ICs. The best separation of wine types was to be had by plotting IC7 against IC5 (Figure S-4) where it can be seen that the differentiation between the 9 different bottles is very clear, whereas the differentiation between the three varieties is not complete. Figure S-4 presents the proportions on IC5 versus IC7 for the samples at pH4. Applying ICA to the 3D-fluorescence spectra of the different varieties of wine near their natural pH (4) did not discriminate them completely. 10

The spectra were grouped into 3 matrices (3 wines × 3 brands × 5 time delays) according to their pH (4, 7 and 8), and an ICA model was calculated on each unfolded (45 × 79 × 81) matrix. It was thus possible to extract the excitation / emission signals for multiple fluorophores from each matrix. The examination of the ICs from different matrices shows that ICs 5 at pH7 and pH8, corresponding to the same fluorophore (λex / λem = 280nm / 360nm), partially separate the varieties of grapes (figures 4, 5). By plotting IC5 at pH8 against IC5 at pH7 (figure 6), the discrimination between the three types of grapes is complete. According to Airado-Rodriguez et al (2009) [11], this fluorophore corresponds to anthocyanins. According to Março et al. (2011) [31], the anthocyanin form present at pH 6.5-8 is the quinoidal anhydrobase. Scores plots in figures 4, 5 and 6 show that all samples are in the positive part of the figures. This implies that all the grape varieties contain anthocyanins in quinoidal anhydrobase form at this pH, but that there are differences in the concentration of the anthocyanins among the grape varieties.

Conclusion The introduction of pH as an additional controlled source of variation in the fluorescence spectra of wine polyphenols improved the extraction of fluorophore signals using Independent Components Analysis, resulting in a better discrimination of the wines. The use of pH variations in the study of the fluorescence of wine polyphenols gives information on the reversibility or irreversibility of the reactions involving the fluophores. It has been shown that the wines studied in the present case can be discriminated by the spectra of their fluophores at pH 7and pH 8. The objective of this work was not to identify all the polyphenols in question and the nature of their structural changes as a function of pH; an additional analysis by LC-MS will help to identify major structures that allowed such discrimination. This study was based on the research of the differences that can be observed in the spectra of wines that could be attributed to certain polyphenols. These wines each contained a single grape variety and were all of the same geographical area. A follow-up study would be interesting to examine whether this procedure can be used to characterize wines composed of a mixture or blend of several grape varieties or mono-varietal wine from different geographical areas.

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Fig 1: Source Signal for IC6. Fig.2: Proportions of IC6 as a fonction of pH and time; the values adjacent to each point correspond to time in minutes between pH adjustment and spectrum acquisition Fig.3: Source signals and proportions of the 6 ICs: Influence of pH modification on the intensity of fluorescence of different fluorophores in wine. ■: pH 3.6->1->3.6->10->3.6; ●: pH 3.6->10->3.6->1>3.6. Fig.4: Source signals and proportions on IC5 calculated from spectra at pH 7: Shiraz (x), Pinot Noir (○) and Cabernet Sauvignon (Δ). Fig.5: Source signals and proportions on IC5 calculated from spectra at pH8: Shiraz (x), Pinot Noir (○) and Cabernet Sauvignon (Δ). Fig.6: Proportions of IC5 at pH8 against IC5 at pH7: Shiraz (x), Pinot Noir (○) and Cabernet Sauvignon (Δ).

Highlights · 3D front face fluorescence spectroscopy was used to acquire spectral fingerprints of wine polyphenols. · pH modification was introduced as a supplementary parameter to increase the information content of Excitation-Emission fluorescence spectra through its effect on the molecular structure of the polyphenols. · Independent Components Analysis (ICA) was used to extract qualitative information from the resulting multi-way (Samples / pH / Excitation / Emission) fluorescence fingerprints. · Source signals extracted from the fluorescence spectra at pH7 and pH8 allowed the discrimination of wines belonging to three grape varieties.

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*Graphical Abstract (for review)