Abstract
In 2008, the European Commission highlighted the risk of wine mislabelling regarding the geographical origin and varietal identification. While analytical methods for the identification of wine by geographical origin exist, a reliable strategy for authentication of wine variety is still missing. Here, we investigate the suitability of the metabolomic fingerprinting of ethyl acetate wine extracts, using ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. In total, 43 white wine samples (three varieties) were analysed within our study. The generated data were processed by principal component analysis and then by partial least squares discriminant analysis. The resulting statistical models were validated and assessed according to their R2 (cum) and Q2 (cum) parameters. The most promising models were based on positive ionisation data, enabling successful classification of 92% of wine samples.
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