Non-volatile molecular composition and discrimination of single grape white of chardonnay, riesling, sauvignon blanc and silvaner using untargeted GC-MS analysis

This study developed and applied a GC-MS method aiming at molecular fingerprinting of 120 commercial single grape white wines (Chardonnay, Riesling, Sauvignon Blanc and Silvaner) for possible authentication according to grape variety. The method allowed detection of 372 peaks and tentative identification of 146 metabolites including alcohols, organic acids, esters, amino acids and sugars. The grape variety effect explained 8.3% of the total metabolite variation. Univariate tests showed two-thirds of the metabolites being different between grape varieties. Partial least squares-discriminant analysis based classification models were developed for each grape variety and a panel of classifiers (42 metabolites) was established. All the classification models for grape variety showed a high certainty (>91%) for an independent test set. Riesling contained the highest relative concentrations of sugars and organic acids, while concentrations of hydroxytyrosol and gallic acid, common antioxidants in wine, decreased in the order of Chardonnay > Riesling > Sauvignon Blanc > Silvaner.

Automated summary

The study developed and applied a GC-MS method for molecular fingerprinting of commercial single grape white wines, showing significant metabolic variations between different grape varieties. Classification models based on partial least squares-discriminant analysis demonstrated high certainty for grape variety authentication in an independent test set.