Determination of phenolic compounds and authentication of PDO Lambrusco wines by HPLC-DAD and chemometric techniques

Analytica Chimica Acta 761 (2013) 34–45

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Determination of phenolic compounds and authentication of PDO Lambrusco wines by HPLC-DAD and chemometric techniques夽 Elisa Salvatore a,b , Marina Cocchi a,∗ , Andrea Marchetti a , Federico Marini b , Anna de Juan c a b c

Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 183, 41125 Modena, Italy Department of Chemistry, University of Rome “Sapienza”, P.le Aldo Moro 5, 00185 Rome, Italy Department of Analytical Chemistry, Universitat de Barcelona, Av. Diagonal 645, 08028 Barcelona, Spain

h i g h l i g h t s

g r a p h i c a l

a b s t r a c t

 HPLC-DAD and multivariate curve resolution allows quantification of phenols in wines.  This fast/cheap methodology does not require traditional complete chromatographic separation.  MCR scores also provide a wine fingerprint to authenticate the different wine varieties.  Multiset and constraints permit to solve scenarios of strong coelution and spectral overlap.

a r t i c l e

i n f o

Article history: Received 31 July 2012 Received in revised form 3 November 2012 Accepted 12 November 2012 Available online 23 November 2012 Keywords: Phenolic compounds Lambrusco wine Multivariate curve resolution-alternating least squares (MCR-ALS) Principal component analysis (PCA)

a b s t r a c t This work proposes a fast and simple method for detection and quantification of phenolic compounds in PDO Lambrusco wines using HPLC-DAD and chemometric techniques. Samples belonging to three different varieties of Lambrusco (Grasparossa, Salamino and Sorbara) were analyzed to provide a methodology appropriate for routine analysis. Given the high complexity of the sample and the coelution among chromatographic peaks, the use of chemometric techniques to extract the information of the individual eluting compounds was needed. Multivariate curve resolution-alternating least squares (MCR-ALS) allowed the resolution of the chromatographic peaks obtained and the use of this information for the quantification of the phenolic analytes in the presence of interferences. Use of multiset analysis and local rank/selectivity information was proven to be crucial for the correct resolution and quantification of compounds. The quantitative data provided by MCR-ALS about the phenolic targets and additional compounds present in the samples analyzed provided wine composition profiles, which were afterwards used to distinguish among wine varieties. Principal component analysis applied to the wine profiles allowed characterizing the wines according to their varieties. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The authentication of foodstuff origin is gaining increasing importance in the alimentary field, where ‘Protected Designation

夽 Paper presented at the XIII Conference on Chemometrics in Analytical Chemistry (CAC 2012), Budapest, Hungary, 25–29 June 2012. ∗ Corresponding author. Tel.: +39 0592055029; fax: +39 059373543. E-mail address: [email protected] (M. Cocchi). 0003-2670/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.aca.2012.11.015

of Origin (PDO)’ certified products must be checked by the control authorities. Recently, chemometrics has helped in the development of methods able to single out chemical profiles related to food origin, so that some widespread and economic analytical techniques have been revaluated thanks to the fine information obtained by multivariate analysis of the collected data. Italian food products having a designation of origin label represent about one fourth of all European foodstuff with denomination of origin, especially considering PDO products. In particular, this article concerns the characterization of Lambrusco wine, a traditional oenological

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product of the Modena district, deriving from the homonymous grape varieties This wine is characterized by the PDO certification and can be obtained using three different types of grapes: Grasparossa, Salamino and Sorbara, which give name to three wine varieties. The production protocols must be designed with the aim of ensuring that the final product complies with defined organoleptic and geographical specifications; hence, the need to develop analytical procedures able to correlate the tasting properties and terroir requirements to measurable variables. To this aim, in general, two approaches may be applied: (i) finding a specific marker in the product, i.e., a chemical constituent or a morphological component, or few compositional characteristics, e.g., the ratio among the concentrations of some constituents; or (ii) acquiring a “fingerprint” of the product through instrumental analysis and then building category models by using chemometric tools, i.e. using blind analysis. The main strength of the second approach consists of taking into account both the individual contribution and the interactions of the different compounds in the sample; in other words, the complexity of the food matrix is implicitly used to define the nature of the product. For fingerprint analysis, chemometrics has been shown to play a critical role, since it allows extracting the maximum information from the collected data and build prediction models to characterize, quantify and classify unknown samples [1]. Phenolic compounds have been identified as relevant markers for the chemotaxonomic differentiation of wine [2–5]. Known principally for their antioxidant and anticarcinogenic properties, they are present in wine due to the natural content in grapes as secondary metabolites of plants and also as a consequence of the wine aging process in wood. Besides, they have been found to play a main role in pattern recognition studies and are recognized as good indicators for estimation of wine quality. The recent increasing interest for this class of compounds has given life to many works regarding their analytical determination. Gonc¸alves et al. recently proposed a validated method for the identification and quantification of phenolic constituents in red wine by a MEPSC8 /UHPLC-DA procedure [6]. The use of other analytical techniques, such as two-dimensional liquid chromatography [7], HPLC-ESI-MS with negative ion detection [8–10] and direct injection in mass spectrometry [11] with relative studies of discrimination [12], has been reported. Proteomic and metabolomic studies regarding the characterization and discrimination of wine through the analysis of proteic and petidic fractions identified by liquid chromatography coupled with mass spectrometry or using the MALDI ion source in combination with mass spectrometry [13,14] can also be encountered. However, when a mass analyzer is not available, complete chromatographic separation has been traditionally required for the identification and quantification of the analytes of interest. As a consequence, in using HPLC-DAD instrumentation, long chromatographic runs and perfect resolution among chromatographic peaks is needed [15]. In this paper, the main analytical purpose is proposing an alternative method for the quantification of some phenolic compounds in wine by using liquid chromatography coupled with diode array detection and multivariate curve resolution (MCR) techniques [16–21]. MCR is used to resolve ‘a posteriori’ peak coelution problems that may come from the complexity of the sample, e.g., in food products, or from the eventual shortening of total analysis time in routine analysis [16]. In fact, MCR allows recovering the pure spectra and the related elution profiles of the analytes of interest and other coeluting compounds from the sole information in the raw chromatographic runs. The elution profiles of the analytes are afterwards used for quantification purposes, whereas the elution profiles of other unknown compounds present in the wine are added to the analyte information for food authentication purposes. Therefore, this work shows that the combination of HPLC-DAD analysis and MCR does not only provide quantitative

35

information about the phenolic compounds, but also general fingerprinting information on the wine samples analyzed. This fingerprinting information has been proven to be of fundamental importance for the potential differentiation of the three Lambrusco wine varieties [22]. From a methodological point of view, the analysis of complex wine samples is used to stress aspects insufficiently exploited from MCR techniques, such as the use of local rank/selectivity constraints to cope with the resolution of highly overlapped compounds in both elution and spectral directions and the use of properly designed multiset structures (containing known standards and samples) for a better definition of the target and unknown compounds in the samples and for the improvement of the quality linked to the quantitative information. 2. Experimental 2.1. Samples This study focuses on the three PDO Lambrusco wines, namely “Lambrusco Sorbara”, “Lambrusco Salamino of Santa Croce” and “Lambrusco Grasparossa of Catelvetro”. The production protocol allows for the possibility of having grape blendings within each PDO, as long as some requirements are fulfilled. Thus, the “Lambrusco Sorbara” needs to consist of, at least, 60% Sorbara grapes, while the remaining 40% can be made of either Salamino grapes only, or from a mixture of Salamino grapes with other minor Lambrusco grapes (the latter must not exceed the 15% of the total composition). The “Lambrusco Salamino of Santa Croce” must consist of, at least, 85% Salamino grapes, while the remaining 15% can be from other minor Lambrusco grapes (Ancellotta and Fontana) and the “Lambrusco Grasparossa of Castelvetro” consists of, at least, 85% of Grasparossa grapes and 15% of other minor Lambrusco grapes (Malbo and Gentile). Therefore, with the only exception of Sorbara wine, for which the law provides a percentage of Sorbara grapes that can be as low as 60%, the other PDOs are composed of, at least, 85% of the respective pure grape varieties. One hundred and ten bottles of Lambrusco wine coming from different producers were sampled and analyzed in this study. The wine bottles were provided directly by the winegrowers and belong to three different varieties: 38 of Grasparossa, 38 of Salamino and 34 of Sorbara. A code was assigned to each sample, indicating the class and a progressive number XXXX AAA (i.e. 0005 GRA) and they were randomized prior to analysis to avoid any experimental drifts. All the bottles were conserved at 25 ◦ C away from light and the analysis was carried out the same day of wine opening. From each open bottle, a second subsample was collected and stored at 2 ◦ C for further controls and analysis reproducibility assessment. Each wine was previously tasted by an expert panel and scored according to their sensory parameters. 2.2. Sample treatment The wine samples were purified using solid phase extraction, as suggested by several authors [23]. For this purpose, Supelco DSC-18 cartridges with 6 mL tubes were selected and the extraction procedure was carried out using a manifold system connected to a vacuum pump. This kind of extraction is the most used in phenolic compound purification. The cartridges for the extraction were conditioned rinsing them with 5 mL of methanol and 5 mL of water; after that, 1 mL of the wine sample was loaded. The retained fraction in the cartridge was first washed with 5 mL of water and then extracted with 6 mL of diethyl ether. The collected ether fraction was evaporated to dryness under nitrogen flow and then dissolved in a water–methanol (80–20%) solution. Although ether is supposed

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to elute most of the phenolic compounds, the cartridges after elution still presented a dark purple color, which means that some compounds were retained. The obtained extract was then filtered and injected in a system for chromatography.

described by a bilinear model, based on the multiwavelength extension of Beer’s absorption law:

2.3. High performance liquid chromatography

where C (I × NC) is the matrix of elution profiles of the analyzed compounds and ST (NC × J) is the matrix of their pure spectra. NC is the number of components. The same bilinear model is used to describe multiset structures obtained combining several chromatographic runs. These structures are organized appending the Dk data matrices (the index k indicates a chromatographic run for a specific sample) one on top of each other (Fig. 1b). The resulting Daug (column-wise augmented) multiset can be decomposed into the Caug matrix, which contains the Ck submatrices of the resolved elution profiles for the single chromatographic runs, ST the matrix of pure spectra common to all chromatograms analyzed and Eaug , the difference between the raw data and the reconstructed data by the Caug ST model, i.e., the experimental error not explained by the bilinear model. The multiset structure for our data set is shown in Fig. 2.

Wine extracts were analyzed by reversed phase liquid chromatography by a Beckman System Gold (USA) for HPLC, with a Model 126 pump built in binary high pressure gradient, coupled with a Model 168 diode array detector of the same producer. The column used was a reversed-phase Atlantis dC18 (250 mm × 4.6 mm, 5 ␮m packing) Waters-Milford-MA. The injector consisted of a Model 7015 Reodyne valve equipped with a 100 ␮L loop. The mobile phase was formed by two solvents: solvent A was water (0.1% TFA) and solvent B was 80% acetonitrile and 30% water (0.1% TFA). An elution linear gradient was used following the scheme: 0.00 min: 0% B; 1.00 min: 20% B; 19.00 min: 40% B; 29.00 min: 100% B; 39.00 min: 0% B. The flow rate was set at 0.6 mL min−1 . The system was thermostatted at 40 ◦ C. The wavelength range in the diode array detector was from 220 to 430 nm with a resolution of  = 2 nm. 2.4. Reagents and standards For the preparation of the mobile phase and the sample treatment, acetonitrile and methanol for HPLC-Gold-Ultragradient by Carlo Erba and water obtained from a Milli-Q purification system (Millipore) were used. Diethyl ether (a.r.) by Riedel-de Haën was used for the solid phase extraction. Gallic acid RPE provided by Carlo Erba Analyticals, (+)-catechin, syringic acid, caffeic acid, vanillin, p-coumaric acid, myrecetin, quercetin provided by Sigma–Aldrich were used to prepare the standards of phenolic compounds. The concentration levels of standard solutions prepared for each analyte were the following (in mg L−1 ): Gallic acid: 0.5-1-5-10; (+)-catechin: 0.3-1-4-10; syringic acid: 0.5-1-2-4; caffeic acid: 0.51-2-4; p-coumaric acid: 0.5-1-2-5; vanillin: 0.5-2-5-10; Mirecetin: 1-2-5-10; quercetin: 0.5-2-5-10. 2.5. Software 32-Karat (version 3.0) was the software used for control and data acquisition in the Beckman HPLC system. Matlab© (Mathworks, version R2010a) was used for preprocessing, calculation and graphical representation of the data. An in-house made Matlab routine was written for the conversion of the ASCII files imported from 32Karat software into a Matlab readable file and for the selection of chromatographic windows (see Section 4.1). Moreover, two Matlab toolboxes were also used for the data analysis: the PLS-toolbox (Eigenvector Research Inc., version 6.5.2) and the MCR GUI (multivariate curve resolution graphical user interface) developed by the chemometrics group of Universitat de Barcelona and IDAEA-CSIC, which is available at the web site http://www.mcrals.info/ [24]. 3. Data analysis 3.1. Data structure Each HPLC-DAD chromatographic run of a single sample provides a data matrix, indicated as D (I × J), where the I rows represent the UV spectra recorded at the different elution times and the J columns the chromatographic elution profiles recorded at the different wavelengths (Fig. 1a). This kind of chemical data can be

D = CST + E

(1)

3.2. Data pretreatment All chromatograms were treated to suppress the baseline contribution due to variations of mobile phase absorbance during the gradient elution. The Elimination of Background Spectrum method (EBS) by Eilers et al. [25,26] was used to correct the background effect. The advantage of this method, based on an asymmetric leastsquares algorithm, is that no predefined shape is assigned to the baseline and, therefore, irregular baseline shapes can be corrected.

3.3. Multivariate resolution of chromatographic multisets The Multivariate Curve Resolution – Alternating Least Squares algorithm calculates Caug and ST from the sole information in the experimental data, Daug (see Eqs. (2) and (3)). The first step is to determine the number of eluted compounds present in a particular cluster of peaks, i.e., the “chemical rank” associated with the data matrix. This determination is performed with a principal component analysis on the Daug matrix. Then, an initial estimate of the ST matrix is obtained with techniques based on the detection of purest variables (SIMPLISMA) [27]. These initial spectral estimates are iteratively optimized with a constrained alternating least squares regression procedure. In each iteration, a new estimate of Caug and ST are obtained solving the two equations: Caug = Daug (ST )

+

(2)

ST = (Caug )+ Daug

(3)

In these formulas (ST )+ and (Caug )+ are the pseudoinverse matrices of the ST and Caug matrices, respectively. The iterative optimization is performed until the results agree with the convergence criterion, which often means that the difference in lack of fit between two consecutive iterations is below a predefined threshold (0.01% change in standard deviation). The lack of fit and the explained variance (EV) express the fitting quality of the resolution results and they are calculated as follows:

 EV% =

dij2 −





dij2

eij2

(4)

E. Salvatore et al. / Analytica Chimica Acta 761 (2013) 34–45

37

Fig. 1. (a) 2D-landscape obtained from an HPLC-DAD run, related data matrix and scheme of the MCR bilinear model for the same matrix; (b) scheme of the MCR bilinear model for a multiset structure.

where dij is an element of the data matrix Dk and eij is the related residual, obtained from the difference among the experimental data and the reproduced by the MCR model, dij∗ .

 lack of fit% =

(dij − dij∗ )



dij2

2

× 100

(5)

Several constraints can be applied to confer chemical meaning to the profiles obtained by MCR-ALS in the analysis of a single HPLC-DAD run [28]. Non-negativity constraint is selected for both the elution and spectra profiles of the resolved compounds (results must be positive) and unimodality is applied to the elution profiles (presence of only one maximum per profile). The local rank constraint forces some components to be absent in a

specific elution region and is highly relevant to resolve compounds with extremely similar spectral profiles. In a multiset structure, all previous constraints can be applied; taking into account that unimodality is applied separately to each one of the Ck submatrices in Caug . The local rank constraint plays also a relevant role, since it only needs to be applied in chromatographic runs, in which the elution information related to partially overlapping compounds is clear and the benefit of this information affects the whole multiset structure. Another constraint specific for multiset structures is the so-called correspondence among species, which allows setting the information of presence/absence of the different compounds in the chromatograms analyzed together. This is particularly helpful when runs of standards and samples are analyzed together, since the composition of the standards (i.e., which compounds are present/absent) can be actively used.

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Fig. 2. (a) Scheme of the MCR bilinear model representing the multiset structure built using all the chromatographic experiments of the Lambrusco wine samples.

3.4. Quantitative information Multivariate curve resolution provides elution profiles and spectra for each analyzed compound, but can also offer quantitative information. This information can be derived from the area of the resolved chromatographic peaks, obtained from the elution profiles of each Ck submatrix in Caug (Fig. 3). When runs of standards at

different concentration levels are analyzed with the wine sample runs, the peak areas obtained from the MCR-ALS results for the standards can be correlated to the real concentrations, like in univariate calibration. Since the information of each compound is separated in a particular elution profile, a calibration line for each of them is built, which allows the estimation of concentration in the analyzed samples.

Fig. 3. Representation of the quantitative information (peaks area matrix) obtained from the C matrix in the MCR resolution and absolute quantification performed with standard solutions.

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3.5. Study of wine varieties The peak areas obtained from the resolution results, belonging to both phenolic targets and additional resolved compounds represent the relative quantitative information of all the eluted species and are the fingerprint information of the samples (see Fig. 1b) [29]. These data are collected in a matrix in which the columns design compounds (phenolic or non-identified) and rows design the different wine analyzed. This matrix of peak areas is studied with principal component analysis, which provides a score map (related to wine samples) and a loading map (related to wine compounds). The interpretation of both plots allows us to explore the possibility to distinguish among wine varieties and to identify the compounds responsible for their characterization. 4. Results and discussion 4.1. Resolution of chromatographic datasets To speed up the resolution of the multiset data, the chromatographic runs have been divided in six elution windows, from which six related multiset structures formed by samples and standards are built. To define the six elution windows, the location of the compounds in the standard runs has been taken as a first reference. An example of the position of the six windows selected for standards and for the mean (over wavelengths) chromatogram of one of the sample is shown in Fig. 4; in supplementary material 2 is reported for the same sample a chromatogram recorded at 280 nm. Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.aca.2012.11.015. To set the six elution windows in each run, a routine has been written that finds the peak maximum of each target compound in the sample run by spectral comparison with the spectrum of the standard compound and builds the related window taking 50 time channels in both sides of the maximum found (which is equivalent to 2–3 min of elution time). This way to build the elution windows, i.e., looking at the spectral similarity of the sample spectra with the standard spectrum, is more reliable than just using the retention time of the target compound, since shifts in elution can occur among standard and sample runs. The retention time of the standard has been taken as the reference to build elution windows only when the spectral information was not conclusive, i.e., when the sought compound in the sample was very minor or

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Table 1 Resolution parameters for the six multisets resolved. Multiset

Number of component

% Explained variance(a)

Lack of fit % exp(b)

Gallic acid (+)-Catechin Syringic, Caffeic acids p-Coumaric, Vanillin Myrecetin Quercetin

3 4 4 7 5 6

99.2 98.9 98.8 99.3 99.5 99.7

9.1 10.2 11.0 8.3 7.0 5.1

found to be absent after the resolution analysis. Each elution window in a sample run contains a peaks cluster with, at least, one analyte and some co-eluted compounds. The six multiset structures formed by standard and sample runs are named after the phenolic compounds eluted in them, as follows: (1) gallic acid, (2) catechin, (3) caffeic and syringic acids, (4) coumaric and vanillin, (5) myrecetin and (6) quercetin. Each multiset is separately analyzed by MCR-ALS and the results obtained are the resolved elution profiles and related pure spectra. The quality parameters and number of compounds resolved in each multiset are reported in Table 1. For all MCR models, the explained variance is close to 99% or higher, which indicates the validity of the results. In all cases, the resolved elution profiles and related pure spectra were consistent with the information in the raw data. Increasing the number of compounds in the different multisets did not lead to better fit or more interpretable results. Below, the resolution of two of the multisets analyzed is described in more detail since the optimization involved the use of some constraints and allows discussion of common problems encountered in the analysis of this kind of chromatographic data. These multisets are (4) p-coumaric acid-vanillin and (6) quercetin. 4.2. Presence or absence of compounds in samples: p-coumaric acid and vanillin multiset analysis p-Coumaric acid and vanillin are examples of compounds that were very difficult to identify from the sole inspection of the spectral information in the raw data linked to sample chromatographic runs. This can be due to a high coelution of these two phenols with other compounds or to the fact that these compounds are very minor or absent in the samples analyzed. Knowing which the real situation would be difficult if these chromatograms were

Fig. 4. The six elution windows are shown: (a) in the mean chromatogram of the standard mixtures and (b) in the mean chromatogram of sample 0020 SOR; 1, gallic acid, 2, (+)-catechin, 3, syringic caffeic acid, 4, vanillin p-coumaric acid, 5, myrecetin 6, quercetin.

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Fig. 5. (a) Matrix of the solved elution profiles for the p-coumaric acid and vanillin multiset; C profiles and the zoom of sample 0015 SAL, where the peaks of p-coumaric (continuous black-green line) and the vanillin (dashed black-blue line) are highlighted; (b) matrix of the pure spectra ST for the p-coumaric and vanillin multiset, where the spectra of p-coumaric acid (continuous-green line) and the vanillin (dashed-blue line) are highlighted. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

analyzed without additional information. The use of chromatographic standard runs of both analytes in the multiset and the application of the constraint of correspondence among species in these standard runs with very well known composition helps to define the identity of these species and to model unequivocally the presence or absence of these compounds in the sample runs. A total of seven compounds were modeled in the multiset. The ST matrix with the seven spectra is reported in Fig. 5a, including those of the two analytes. The C matrix of the whole multiset is shown in Fig. 5b, zooming on the sample 0015SAL X, where it is clear that the p-coumaric acid peak is present and perfectly resolved from the coeluting compounds, whereas vanillin is detected in a very minor proportion and may likely be absent in the samples analyzed. Similar conclusions about this pair of compounds are obtained in the rest of sample runs analyzed. Once quantitative information is obtained (see Section 3.4), it will be seen whether vanillin is a

minor compound or whether it can be considered absent (if its concentration is below the LOD). 4.3. Strongly coeluting compounds with similar chemical structure (spectra): quercetin multiset analysis In this multiset, the use of local rank information helps in solving the quercetin peak from a strongly coeluted interference, despite the large spectra similarity between them and the minor presence of the second compound. The local rank constraint is applied only in some specific zones of the chromatogram, where the quercetin peak is clearly separated from the neighbor interference. In this elution region, the interference is set to be absent. It is important to say that, in a multiset structure, this local rank constraint has only been applied to some sample runs where this differentiation is clear. This strategy makes possible a perfect resolution of the

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Fig. 6. (a) Matrix of the solved elution profiles for the quercetin multiset: C profiles and the zoom of sample 0006 GRA, where the peaks of quercetin (continuous black-green line) and the interferents (dashed black-cyano line) are highlighted; (b) matrix of the pure spectra ST for the quercetin multiset, where the spectra of quercetin (continuous black-green line) and the interferents (dashed black-cyano line) are highlighted. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

two species, which otherwise would be seen as a unique component because of the high elution and spectral overlap in many runs. The ST matrix in Fig. 6a shows the compounds’ spectra, where bold line and dashed line belong, respectively, to quercetin and the interference. In Fig. 6b the zoom on the sample 0006GRA X in the C matrix shows the elution region in the chromatogram where the absence of the interference has been set as a local rank constraint. 4.4. Quantification of phenolic acids As described in Section 3.4 an external calibration strategy is performed for the quantification of the eight analytes: gallic, caffeic, syringic, p-coumaric and vanillin, (+)-catechin, mirecetin and quercetin. For this purpose, standard solutions and mixtures with different concentration of the phenolic compounds were prepared

(see Section 2.2). Each of the six multisets formed by standards and samples, as described in Section 3.1, was analyzed to obtain quantitative information. The general structure of all mutisets was presented in Fig. 2. Table 2 reports the quality parameters of the individual calibration models of the eight phenols analyzed. The values of limit of detection and of quantification obtained by this technique are comparable with values obtained by other authors by using HPLC-UV in operational conditions ensuring good peak separation [14,30]. The advantage of the methodology presented in this work is that comparable results are obtained in a shorter analysis time, even in the presence of coeluting interferences. The calibration models were used to determine the concentrations of the phenolic compounds in 110 wine samples. These values are reported in the supplementary material. As a summary of the quantitative information about the content of phenolic compounds

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in the wine samples analyzed, the minimum, maximum and mean value of concentrations of each variety are listed in Table 2. As general conclusions, vanillic acid is found to be in concentrations lower than LOD in all samples and, hence, can be assumed to be absent in these Lambrusco varieties. Quercetin is the most abundant phenolic compound in all varieties, gallic and p-coumaric acids seem to dominate in some varieties and syringic and caffeic acid seem to be minor in the three kinds of wines studied. Among varieties, Sorbara, in general, contains lower concentrations of almost all the compounds compared with the other two varieties that have more similar concentrations. This difference can also be easily seen by visual inspection since the Grasparossa and Salamino varieties present a dark purple color. Other trends can be seen, such as the high amount of (+)-catechin in Salamino or the high amount of syringic acid in Grasparossa. However, the possibility to differentiate wine varieties from the information related to the abundance of these compounds will be much better seen when the whole fingerprint information is studied by multivariate analysis, as shown in the next sections. Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.aca.2012.11.015.

8.06 − 3.00 3.72 7.52 − 3.13 3.95 1.30 − 0.32(
f

Limit of detection (LOD) 3Sb /b. Limit of quantitation (LOQ) 10Sb /b. Concentration values for each analyte in each wine variety, first line: maximum − minimum and second line: average. e

c

Standard deviation of slope of the calibration line. Standard deviation of offset of calibration line. √ Root mean square error in calibration [ (cpred − ctrue )2 /n − 1] a

b

d

0.996

0.26

0.38

4.5. Analysis of the fingerprint information

0.43 76 11541x + 2489 Quercetin

199

0.14 1.98 0.09 1.12 0.996 0.992 0.12 0.13 248 81 7235x + 1251 2855x + 2158 Vanillin Mirecetin

37 348

0.13 0.04 0.993 0.32 291 13289x − 1023 p-Coumaric

120

0.57 0.34 0.997 0.29 141 12905x − 3125 Caffeic Ac.

418

0.32 0.13 0.994 0.09 157 8705x − 451 Syringic Ac.

231

356 10385x − 765 (+)-Catechin

78

0.05

0.997

0.04

0.26

8.87 − 3.02 4.38 6.91 − 3.02 3.90 2.01 − 0.18(
23

0.06

0.995

0.09

0.11

Salamino max − min mean conc Grasparossaf max − min mean conc LOQe LODd Rb RMSECc Sb b Sm a Calibration line Compound

Table 2 Calibration parameters for analytes, LOD LOQ and concentration values are expressed in mg L−1 the values in gray are below the LOD.

Sorbara max − min mean conc

42

The fingerprint information is obtained from the value of the peak areas obtained for all the compounds resolved by MCR-ALS (the identified phenolic compounds and the unknown compounds resolved in the elution windows analyzed). In order to study the relation among the fingerprint information and the three wine varieties, the matrix of peak areas is subjected to a principal component analysis, as mentioned in Section 3.5. Two pretreatments are applied to the matrix of peak areas prior to PCA: normalization to unit area per rows and auto-scaling per columns. Normalization along the rows is primarily applied to reduce the effect of the total intensity of fingerprint profiles due only to variation in samples concentration, such as those coming from differences of volume in the sample extraction. This is done because the interest of the analysis is on the relative amount of compounds within a sample and not on the differences of global intensity among fingerprint profiles. Autoscaling along the columns is done to give the same potential importance to all compounds analyzed. PCA results from the analysis of the complete fingerprinting information containing all resolved compounds (target phenol compounds and unidentified species) are in Figs. 7 and 8. The scores plot for the first two principal components is shown in Fig. 7a. The first principal component helps to differentiate between the two varieties Sorbara and Grasparossa while the second principal component separates these classes from the third one of Salamino. The loadings plot (Fig. 8) shows that quercetin, syringic and mirecetin point out to the Grasparossa samples and, indeed, these compounds have clearly the lowest concentrations in the Sorbara variety, the samples of which are oriented in the opposite direction. p-Coumaric and caffeic acids are very linked to differentiate the Sorbara variety from the rest and gallic acid seems to be clearly more present in the Salamino variety when compared with the other two. Besides, the correlation among phenol concentrations can also be clearly seen. For instance, quercetin and myrecetin concentration values shows a high direct correlation and a high inverse correlation with catechin, which suggests that maybe not all these compounds need to be determined for authentication purposes. Conversely, p-coumaric acid seems completely uncorrelated to the previous compounds and slightly correlated with caffeic acid. The PCA scores plot performed only using the quantified phenolic compounds as variables (Fig. 7b) is also provided. This plot

E. Salvatore et al. / Analytica Chimica Acta 761 (2013) 34–45

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Fig. 7. (a) Scores plot, PC1 vs. PC2, of PCA of the fingerprinting information data (110 samples × 23 peaks area) with; (b) scores plot, PC1 vs. PC2 of PCA of quantified phenolic compounds data (110 samples × 6 peaks area).

Fig. 8. Loadings plot of PCA model including all 23 peaks area, highlighting the variables useful for the differentiation of the classes.

displays that the sole use of the phenolic compounds allows only the differentiation of the Sorbara variety from the other two varieties. Without the introduction of the information related to the additional compounds obtained from the multivariate resolution analysis, it would not be possible to distinguish the two varieties with most similar concentrations, i.e., Grasparossa and Salamino. This point clarifies the importance of the quantitative data provided by MCR-ALS, which gives the peak areas of all the resolved species in each of the six elution windows in addition to the target compounds. This additional information allows a better separation of the wine varieties. This preliminary explorative data analysis is encouraging and further work will be developed to discriminate the wine varieties by using multivariate tools to build classification models, such as Partial Least Squares-Discirminant Analysis (PLSDA) and others. From a chemical point of view, the loadings plot evidences that some of the unidentified compounds are extremely important for the characterization of the wines, in particular to differentiate between Salamino from Grasparossa and Sorbara varieties. An ongoing study is devoted to identify the unknown resolved compounds by using HPLC coupled to mass spectrometry detection.

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E. Salvatore et al. / Analytica Chimica Acta 761 (2013) 34–45

Table 3 Concentration values of the phenolic compounds for the test samples, all the concentrations are expressed in mg L−1 values of Vanillin concentration are below the limit of detection. Test samples Grasparossa Salamino Sorbara Extraterritorial

s0041 s0044 s0042 s0044 s0035 s0037 x0003 x0007 x0011 x0012 x0016 x0019

Gallic Ac.

(+)-Catechin

Syringic Ac.

Caffeic Ac.

p-Coumaric Ac.

Vanillin

Mirecetin

Quercetin

4.12 5.38 10.45 9.28 12.18 19.79 24.29 13.26 11.42 15.18 19.79 22.94

3.20 3.06 3.09 3.07 3.09 3.10 3.18 3.13 3.03 3.78 3.75 3.15

1.78 3.28 2.37 2.08 2.21 3.26 1.68 0.72 0.77 2.43 0.18(
1.45 2.69 3.69 2.88 3.09 3.09 2.69 1.39 1.71 4.77 1.57 1.94

3.01 2.65 1.92 1.88 0.93 1.22 5.11 3.12 4.67 1.71 2.13 2.30

– – – – – – – – – – – –

0.94(
1.79 18.96 10.10 6,27 4.99 3.60 6.00 1.94 3.26 13.43 2.87 1.97

4.6. Applicability of the model To verify the reliability and robustness of the model, an additional set of samples has been analyzed after several months applying the same experimental conditions and taking advantage of the previous MCR models for the resolution of the chromatographic peaks. The samples of this validation dataset are in total 12, six wines are of the same Lambrusco wine varieties as the original dataset, but collected in a second time, namely: 2 Grasparossa, 2 Salamino and 2 Sorbara; and the other six samples, here named ‘extraterritorial’ wine, belong to the same grape variety, namely Lambrusco, but the cultivation of the grape is located outside the district of Modena. Hence, since these latter wines do not respect the suitable geographical characteristics, they are not recognized with the ‘Protected Designation of Origin’. The multiset obtained for these samples has been analyzed in the same way as for the 110 wines. Each pure spectral profile obtained from the resolution of the first multisets is used as initial estimation for the resolution of these validation sets, obtaining a very satisfactory matching among the first and second set of resolved spectra. In this case the peak areas allow the quantification of the phenolic compounds using the same calibration strategy as in Table 2. The concentration values obtained for the theses samples are reported in Table 3. The concentration values of the external samples of PDO wines are comprised in the range previously determined for the related wine class, which confirms the reliability of the quantitative information obtained. Some expected differences in concentration levels of polyphenols are observed for the samples belonging to the ‘extraterritorial’ samples, which seems promising in the perspective of deriving authentication models. Moreover, standards solution at the maximum and minimum concentration values used to build the calibration lines were also analyzed and values of RMSEP between 0.13 and 0.72 were obtained, fairly similar to those related to the calibration models in Table 2.

The use of resolved peak areas instead of the entire chromatographic run as fingerprinting information permitted to circumvent the fundamental problem of chromatographic alignment, always present when full chromatographic traces are used in authentication analysis. As far as the characterization of the three Lambrusco varieties is concerned, it seems possible to differentiate the three wine varieties by using the sole information contained in the peak area values of the different eluted species obtained as a fingerprinting information. The PCA model has shown that the three wine varieties have substantial differences in terms of relative amount of phenolic compounds. However, the relevant variables for wine differentiation are both the target phenolic compounds quantified and species eluted near the target compounds, which are solved in the same MCR resolution window. Both kinds of compounds (targets and unidentified resolved neighbors) are of main interest for the fingerprinting analysis, since the Sorbara class is easily separated considering only the information of the phenolic compounds quantified, because of its lower content of all the species, but the additional information provided by the other unidentified components is essential to differentiate between Grasparossa and Salamino classes. In this respect, in a following study, mass spectrometry detection will be applied for the identification of these unknown relevant species. The estimation of quantitative classification models by application of specific chemometric tools will be also pursued. Acknowledgments This work was supported by the AGER, Agroalimentare e Ricerca, cooperative project between grant-making foundations under the section “wine growing and producing”: project New analytical methodologies for varietal and geographical traceability of oenological products; contract n. 2011-0285. We are also grateful to Consorzio Marchio Storico Lambruschi Modenesi for the use of their facilities during sampling procedures.

5. Conclusions References In this paper we developed an HPLC-DAD method for the determination of some phenolic compounds in wine, which is rapid and simple and allows the determination of the target compounds in the presence of overlapping interferences. The method was also verified on a set of external samples and standards acquired further on in time. MCR has been shown to be a powerful tool for the resolution of the species of interest, giving optimal results also in the case of severe elution and spectral overlap among compounds. Figures of merit related to quantitative analysis made the methodology acceptable for analytical determination purposes. To achieve the quality of the results presented, use of local rank constraints and suitable multiset structures (formed by standards and samples) were mandatory.

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