Validation of the 2H-SNIF NMR and IRMS Methods for Vinegar and Vinegar Analysis: An International Collaborative Study.

This paper presents the results of a collaborative study involving seven laboratories and concerning two samples of wine vinegar, one of apple vinegar and four of balsamic vinegar. The aim of the study was to define standard deviations of repeatability (sr) and reproducibility (sR) for vinegar and balsamic vinegar stable isotope ratios of H (D/H), C (δ13C) and O (δ18O), in order to establish them as fully recognized official standards. Acetic acid was extracted and subjected to (D/H)CH3 and δ13C analysis. δ18O analysis was performed on whole samples. The grape must solution remained after distillation of balsamic vinegar was fermented and the resulting ethanol was subjected to (D/H)I, (D/H)II, R and δ13C analysis. The sr and sR were 0.6 ppm and 1.1 ppm for (D/H)CH3, 0.14‱ and 0.25‱ for δ13C of acetic acid, 0.1‱ and 0.17‱ for δ18O of water, 0.19 ppm and 0.64 ppm for ethanol (D/H)I, 1.14 and 1.31 ppm for (D/H)II, 0.09 and 0.11‱ for δ13C of ethanol. These data are in line with those in the literature or reported in corresponding official methods, and sr and sR of balsamic vinegar are in line with those of vinegar and must.

Synergistic effect of the simultaneous chemometric analysis of ¹H NMR spectroscopic and stable isotope (SNIF-NMR, ¹8O, ¹³C) data: application to wine analysis.

It is known that (1)H NMR spectroscopy represents a good tool for predicting the grape variety, the geographical origin, and the year of vintage of wine. In the present study we have shown that classification models can be improved when (1)H NMR profiles are fused with stable isotope (SNIF-NMR, (18)O, (13)C) data. Variable selection based on clustering of latent variables was performed on (1)H NMR data. Afterwards, the combined data of 718 wine samples from Germany were analyzed using linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), factorial discriminant analysis (FDA) and independent components analysis (ICA). Moreover, several specialized multiblock methods (common components and specific weights analysis (ComDim), consensus PCA and consensus PLS-DA) were applied to the data. The best improvement in comparison with (1)H NMR data was obtained for prediction of the geographical origin (up to 100% for the fused data, whereas stable isotope data resulted only in 60-70% correct prediction and (1)H NMR data alone in 82-89% respectively). Certain enhancement was obtained also for the year of vintage (from 88 to 97% for (1)H NMR to 99% for the fused data), whereas in case of grape varieties improved models were not obtained. The combination of (1)H NMR data with stable isotope data improves efficiency of classification models for geographical origin and vintage of wine and can be potentially used for other food products as well.