Critical assessment of chemometric models employed for varietal authentication of wine based on UHPLC-HRMS data

• Published in: Food control 2023-01, Vol.143, p.109336, Article 109336
• Main Authors: Uttl, Leos, Bechynska, Kamila, Ehlers, Mona, Kadlec, Vaclav, Navratilova, Klara, Dzuman, Zbynek, Fauhl-Hassek, Carsten, Hajslova, Jana
• Format: Article
• Language: English
• Subjects: chemometrics; decision support systems; discriminant analysis; flavonoids; food safety; grapes; tandem mass spectrometry; ultra-performance liquid chromatography; white wines
• ISSN: 0956-7135
• DOI: 10.1016/j.foodcont.2022.109336

The use of metabolic fingerprinting combined with advanced chemometric tools for wine authentication has increased in recent years. Although numerous studies, showing different authentication strategies, have been published, rarely any attention has been paid to the stability of used classification models over a longer time period. Here, we present a reliable and robust metabolic fingerprinting-based multiclass strategy for varietal authentication of wine. Analysis was conducted using ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. Two sets of commercial wine samples, one for the creation of classification models (201 wines, five red and five white grape varieties) and one for the verification of their validity over a longer time period (138 wines, three white varieties), were analysed. The generated data from the first sample set were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA). The resulting models were validated and used to build decision trees, which enabled the classification of wine samples according to the grape variety. The individual classification rates of the OPLS-DA models were 90–100%. Overall classification rates of the decision trees were 94 and 96% for red and white wines, respectively. In case of the white wine decision tree, verification of its validity over a longer time period was performed using an additional sample set, analysed four months after the original sample set. From the additional sample set, 87% of samples were correctly classified, thus, the stability of the OPLS-DA classification models over a longer time period was verified. In addition, 25 varietal markers of significant statistical importance, mostly flavonoids, phenolic acids and their derivatives, were tentatively identified.