Validated modeling for German white wine varietal authentication based on headspace solid-phase microextraction online coupled with gas chromatography mass spectrometry fingerprinting.

An untargeted analytical approach combined with chemometrics using the volatiles of German white wine was investigated regarding the usefulness for verifying botanical origin. A total of 198 wine samples of Riesling, Müller-Thurgau, Silvaner, Pinot Gris, and Pinot Blanc were examined applying headspace solid-phase microextraction online coupled with gas chromatography mass spectrometry. The resultant three-dimensional raw data were processed by available metabolomics software. After data treatment, a partial least-squares discriminant analysis (PLS-DA) model was validated. External samples were correctly classified for 97% Silvaner, 93% Riesling, 91% Pinot Gris/Blanc, and 80% Müller-Thurgau. This model was related to monoterpenoids, C13-norisoprenoids, and esters. Further, 100% prediction for a two-class model of Riesling versus Pinot Gris/Blanc was confirmed by 74 additional samples measured independently. Hence, the strategy applied was, in particular, reliable and relevant for white wine varietal classification. In addition, the superior classification performance of the Riesling class was revealed.