Elsevier

Analytica Chimica Acta

Volume 701, Issue 2, 9 September 2011, Pages 139-151
Analytica Chimica Acta

Adulteration of the anthocyanin content of red wines: Perspectives for authentication by Fourier Transform-Near InfraRed and 1H NMR spectroscopies

https://doi.org/10.1016/j.aca.2011.05.053Get rights and content

Abstract

In the Italian oenological industry, the regular practice used to naturally increase the colour of red wines consists in blending them with a wine very rich in anthocyanins, namely Rossissimo. In the Asian market, on the other hand, anthocyanins extracted by black rice are frequently used as correctors for wine colour. This practice does not produce negative effects on health; however, in many countries, it is considered as a food adulteration.

The present study is therefore aimed to discriminate wines containing anthocyanins originated from black rice and grapevine by using reliable spectroscopic techniques requiring minimum sample preparation. Two series of samples have been prepared from five original wines, that were added with different amounts of Rossissimo or of black rice anthocyanins solution, until the desired Colour Index was reached. The samples have been analysed by FT-NIR and 1H NMR spectroscopies and the resulting spectra matrices were subjected to multivariate classification. Initially, PLS-DA was used as classification method, then also variable selection/classification methods were applied, i.e. iPLS-DA and WILMA-D. The classification with variable selection of NIR spectra permitted to classify the test set samples with an efficiency of about 70%. Probably these not excellent performances are due to the matrix effect, together with the lack of sensitivity of NIR with respect to minor compounds. On the contrary, very satisfactory results were obtained on NMR spectra in the aromatic region between 6.5 and 9.5 ppm. The classification method based on wavelet-based variables selection, permitted to reach an efficiency in validation greater than 95%.

Finally, 2D correlation analysis was applied to FT-NIR and 1H NMR matrices, in order to recognise the spectral zones bringing the same chemical information.

Highlights

• Aim of the work: to discriminate anthocyanins from black rice or grapevine in wine. • Two series of adulterated samples were analysed by FT-NIR and 1H NMR spectroscopies. • Feature selection-classification on NIR spectra reached a 70% prediction efficiency. • Feature selection-classification on NMR spectra reached a 95% prediction efficiency. • 2D correlation analysis recognised the zones bringing the same chemical information.

Introduction

The wine-making process for red wines production involves the extraction of different phenolic compounds. Among them, the anthocyanins are particularly relevant, because they are the mainly responsible substances for red wines’ colour. In Vitis vinifera grapes and in the corresponding wines are present malvidin, peonidin, delphinidin, petunidin and cyanidin – the aglycone moieties of anthocyanin molecules – which differ from one another for the number of hydroxyl and methoxyl groups that can serve as substituents of the aromatic rings. Such aglycones occur in nature in glycosylated forms with one or more sugars [1], [2]. Anthocyanins are present in Italian wines in widely varying amounts – usually 100–1500 mg L−1 – depending on the grape variety, various seasonal and environmental factors as well as the techniques of vinification and the length of the aging process [3].

In the Italian oenological industry, the common and standard practice used to naturally increase the colour of red wines consists in blending them with a wine very rich in anthocyanins, namely Rossissimo dell’Emilia or, simply, Rossissimo [4]. In the Asian market, on the other hand, anthocyanins extracted by black rice are frequently used as correctors for wine colour. This practice is boosted by the fact that the extraction of anthocyanins from black rice is very advantageous, since the total content of anthocyanins in the whole seed can reach up to 500 mg/100 g [5], and anthocyanins are mainly located in the husk, that is usually removed as refuse. Such as for the types of anthocyanins extracted by black rice, different studies have shown that the aglycone moieties mainly represented are cyanidin and peonidin [5], [6], [7], [8]. Since the anthocyanins family consists of molecules structurally very similar and of proven salutary effect, it is easy to understand that the problem of using anthocyanins extracted from black rice to increase the colour of wine is not a problem of toxicity or of food safety, but it is a merely legislative one. In any case, in many countries, Italy included, this procedure is considered in effect as a food adulteration.

The present study is therefore aimed at discriminating wines containing anthocyanins added with Rossissimo from wines added with black rice solution.

Currently, the total anthocyanins content in wine – usually expressed as malvidin-3-glucoside equivalents – is determined by UV–vis analysis at 520 nm [9], that is definitely a rapid and reliable method, but it does not permit the speciation of the single anthocyanins that is indeed necessary to identify their origin.

The provenance of anthocyanins from plant species different from grapevine, on the contrary, can be assessed by chromatographic techniques, as widely confirmed by literature. A lot of works, in fact, deal with the identification and quantification of specific anthocyanins by means of reverse-phase HPLC [1], [2], [9], [10], [11], [12], [13] or liquid–liquid counter-current chromatography [14], [15]. Liquid chromatography is a very sensitive technique but, unfortunately, it is rather time-consuming, because it usually needs a long time for the preparation of samples and standard solutions, besides the fact that it requires skilled personnel.

In the winemaking sector, at the present time, the infrared spectroscopies are widely used for routine analysis of musts and wines since they offer the advantage that they do not need sample preparation. The infrared spectrum is, in fact, acquired on the complex matrix in a way to obtain a sample fingerprint, then the simultaneous determination of different analytes from the spectra is made by applying multivariate calibration models, previously developed on a set of reference samples.

A number of applications for determining the total anthocyanins content in wine by NIR/vis–NIR can be found in literature, i.e. the works by Bauer et al. [16], Janik et al. [17], Di Egidio et al. [18] and Cozzolino et al. [19], who also demonstrated the feasibility of using NIR spectroscopy to determine other grape phenolic compounds [20], while Sinelli et al. [21] and Zsivanovits et al. [22] predicted the total anthocyanins content by NIR in berries.

Another suitable technique for the characterisation of the anthocyanic component of wines is NMR spectroscopy, since it shares the main advantage of NIR spectroscopy, i.e. the possibility to analyse the sample “as it is”, but avoids its main defect, i.e. the scarce sensitivity in determining compounds present at low concentrations [23], [24], such as anthocyanins, that are present in wine in amounts close to the NIR limit of detection. With respect to chromatographic analyses, NMR is a little less sensitive, but it is non-destructive, simpler and shorter as sample preparation, more reproducible and capable of simultaneous detection of a great number of low molecular mass components in complex mixtures [25], [26], [27].

In wine analysis context do exist some works about the application of NMR spectroscopy for the analysis of wine samples as complex matrices, i.e. without the need for sample preparation or isolation of analytes. Some authors have analysed wine samples without extraction or purification, then they calculated from the NMR signals a certain number of parameters to be used for building the data matrix for chemometric analysis [28], [29]. In a different way, most of the authors preferred to apply chemometric techniques on the whole NMR spectra, presenting overlapping peaks as well, following the so-called blind analysis of signals to face classification or calibration problems [30], [31], [32], [33].

With the aim of discriminating wines containing anthocyanins added with Rossissimo from wines added with black rice solution, we have initially chosen to assess the applicability of the simplest and fastest technique, i.e. the NIR spectroscopy. The replicate NIR spectra of samples taken from the two classes were preprocessed and then analysed by means of PLS-DA in order to build the corresponding classification model. Since the anthocyanins-related information could be overwhelmed by uninformative variation, it was also applied a novel classification method of variable selection in the wavelet domain, called WILMA-D (Wavelet Iterative Linear Modelling Approach-Discriminant version). The aim of WILMA-D – introduced for the first time in the present work – is the discrimination between objects of different classes and it is a modified version of the algorithm WILMA [34], [35], which was originally devoted to solving regression problems.

The obtained results have shown that NIR spectroscopy seems to reveal some useful information to distinguish between adulterated and non-adulterated samples, but the matrix effect together with the lack of sensitivity of NIR with respect to minor compounds, hindered to obtain high performances in validation.

As a consequence, we also turned our attention to the 1H NMR spectroscopy, since it is a more sensitive technique but rather simple as for sample preparation. After the alignment of spectra, the aromatic compound region between 6.5 and 9.5 ppm was retained for the successive classification. Initially, PLS-DA was used as classification method on signals preprocessed in different ways then, also in this case, the variable selection was attempted. In addition to WILMA-D, another method of variable selection, i.e. iPLS-DA, was investigated. Since the NMR spectra present less overlapped bands and more resolved peaks with respect to NIR, the use of a rather simple method for variable selection that works in the same domain of the original signal could be suitable. The spectral regions retained by feature selection algorithms were confirmed as significant for anthocyanins speciation by a deeper NMR investigation on selected samples, conducted also with 2D experiments.

Finally, 2D correlation analysis was applied to FT-NIR and 1H NMR matrices in order to recognise the spectral zones bringing the same chemical information, especially those that were associated to the presence of anthocyanins.

Section snippets

Materials

Adulterated wine samples were prepared starting from five different types of wines: a white wine (Trebbiano dell’Emilia), a rosé wine (Lambrusco Salamino), and three red wines (Sangiovese di Romagna, Lambrusco dell’Emilia, Montepulciano).

All these “natural” wines, i.e. wines without any addiction, were used as a basis to prepare two sets of samples by adding two different liquids: an aqueous solution of anthocyanins extracted from black rice (ZuhHai Golden Land Natural Colors Co., Ltd) having

Explorative PCA

Exploratory analysis PCA on meancentered NIR spectra has shown similar patterns for the spectra acquired with both S (0.2 mm) and L (1 mm) cuvettes. In particular, the scores plot of the first two PCs (not shown) shows that the B and R sets are overlapped in the PCs space. The analysis of the following components does not show a separation of objects based on the origin of anthocyanins in the sample too. On the contrary, from the PCA model, we can partly distinguish the spectra based on the date

Conclusions

In this work is proposed a novel application of FT-NIR and 1H NMR-based blind analyses, aimed to discriminate wines added with the blending wine Rossissimo from wines adulterated with anthocyanins extracted from black rice to increase their Colour Index.

The results achieved by NIR spectroscopy showed that a relationship between near infrared spectra and anthocyanins composition of wine does exist, even if the classification did not reach excellent results. In particular, the performance of the

Acknowledgements

The authors wish to thank Dr. Ilaria Gibertini for providing significant assistance during the experimental phase. We are also thankful to “Centro Interdipartimentale Grandi Strumenti (CIGS)” of the University of Modena and Reggio Emilia and to the “Fondazione Cassa di Risparmio di Modena” which supplied NMR spectrometer.

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