Identification of Tentative Traceability Markers with Direct Implications in Polyphenol Fingerprinting of Red Wines: Application of LC-MS and Chemometrics Methods

This study investigated the potential of using the changes in polyphenol composition of red wine to enable a more comprehensive chemometric differentiation and suitable identification of authentication markers. Based on high performance liquid chromatography-mass spectrometry (HPLC-MS) data collected from Feteasca Neagra, Merlot, and Cabernet Sauvignon finished wines, phenolic profiles of relevant classes were investigated immediately after vinification (Stage 1), after three months (Stage 2) and six months (Stage 3) of storage, respectively. The data were subjected to multivariate analysis, and resulted in an initial vintage differentiation by principal component analysis (PCA), and variety grouping by canonical discriminant analysis (CDA). Based on polyphenol common biosynthesis route and on the PCA correlation matrix, additional descriptors were investigated. We observed that the inclusion of specific compositional ratios into the data matrix allowed for improved sample differentiation. We obtained simultaneous discrimination according to the considered oenological factors (variety, vintage, and geographical origin) as well as the respective clustering applied during the storage period. Subsequently, further discriminatory investigations to assign wine samples to their corresponding classes relied on partial least squares-discriminant analysis (PLS-DA); the classification models confirmed the clustering initially obtained by PCA. The benefits of the presented fingerprinting approach might justify its selection and warrant its potential as an applicable tool with improved authentication capabilities in red wines.