Abstract
Provence rosé wines have gained global popularity, making them vulnerable to fraud. This study aimed to identify specific chemical markers to detect counterfeit Provence rosé wines. An untargeted LC-MS-based metabolomics analysis was performed on a set of 30 wines classified as “Provence,” “Non-Provence,” and “Provence imitations.” Using the Molnotator workflow, 1300 potential metabolites were generated, and five key chemomarkers were selected through a machine learning pipeline. Further targeted analysis and bioinformatics using in silico MS/MS fragmentation systems confidently annotated three specific chemomarkers for “Provence” rosé: acuminoside, tetrahydroxydimethoxyflavone, and 5′-methoxycastavinol. A composite score using a PLS model combining the 3 chemomarkers effectively distinguished authentic wines, with high accuracy (sensitivity 83.3 %, specificity 100 %, accuracy 93.3 %).
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