Abstract
Aim: The aim of this study was to investigate the application of mid-infrared (MIR) spectroscopy combined with multivariate analysis, to provide a rapid screening tool for discriminating among different Italian monovarietal red wines based on the relationship between grape variety and wine composition in particular phenolic compounds.Methods and results: The MIR spectra (from 4000 to 700 cm‒1) of 110 monovarietal Italian red wines, vintage 2016, were collected and evaluated by selected multivariate data analyses, including principal component analysis (PCA), linear discriminant analysis (DA), support vector machine (SVM), and soft intelligent modelling of class analogy (SIMCA). Samples were collected directly from companies across different regions of Italy and included 11 grape varieties: Sangiovese, Nebbiolo, Aglianico, Nerello Mascalese, Primitivo, Raboso, Cannonau, Teroldego, Sagrantino, Montepulciano and Corvina.
 PCA showed five wavelengths that mainly contributed to the PC1, including a much-closed peak at 1043 cm‒1, which correspond to the C–O stretch absorption bands that are important regions for glycerol, whereas the ethanol peaks at around 1085 cm‒1. The band at 877 cm‒1 are related to the C–C stretching vibration of organic molecules, whereas the asymmetric stretching for C–O in the aromatic –OH group of polyphenols is within spectral regions from 1050 to 1165 cm‒1. In particular, the (1175)–1100–1060 cm‒1 vibrational bands are combination bands, involving C–O stretching and O–H deformation of phenolic rings. The 1166–1168 cm‒1 peak is attributable to in-plane bending deformations of C–H and C–O groups of polyphenols, respectively, for which polymerisation may cause a slight peak shift due to the formation of H-bridges.The best result was obtained with the SVM, which achieved an overall correct classification for up to 72.2% of the training set, and 44.4% for the validation set of wines, respectively. The Sangiovese wines (n=19) were split into two sub-groups (Sang-Romagna, n=12 and Sang-Tuscany, n=7) considering the indeterminacy of its origins, which is disputed between Romagna and Tuscany. Although the classification of three grape varieties was problematic (Nerello Mascalese, Raboso and Primitivo), the remaining wines were almost correctly assigned to their actual classes.Conclusions: MIR spectroscopy coupled with chemometrics represents an interesting approach for the classification of monovarietal Italian red wines, which is important in quality control and authenticity monitoring.Significance and impact of the study: Authenticity is a main issue in winemaking in terms of quality evaluation and adulteration, in particular for origin certified/protected wines, for which the added marketing value is related to the link of grape variety with the area of origin. This study is part of the D-wine project “The diversity of tannins in Italian red wines”.
Highlights
The combination of MIR spectroscopy and linear discriminant analysis (LDA) made it possible to correctly classify more than 75% of the red and white wines sourced from organic (n=57) and non-organic (n=115) production systems from 13 growing regions in Australia (Cozzolino et al, 2009)
As part of the D-wine project “The diversity of tannins in Italian red wines”, this study aimed to investigate the application of MIR spectroscopy combined with multivariate analysis to provide a rapid screening tool for discriminating among different Italian monovarietal red wines
The support vector machine (SVM) classification of three grape varieties was problematic, the remaining wines were correctly assigned up to 100%. these accuracy values are comparable to those obtained for grapevine varietal classification using near infrared spectroscopy with cross-validation and SVM (Yu et al, 2017)
Summary
Multivariate characterisation of Italian monovarietal red wines using MIR spectroscopy. Susanna Rio Segade, Maurizio Ugliano and Andrea Versari1*. This article is published in cooperation with the 11th OenoIVAS International Symposium, June 25–28 2019, Bordeaux, France
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