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The potential association between stable isotope ratios of light elements and mineral content, in conjunction with unsupervised and supervised statistical methods, for differentiation of spirits, with respect to some previously defined criteria, is reviewed in this work. Thus, based on linear discriminant analysis (LDA), it was possible to differentiate the geographical origin of distillates in a percentage of 96.2% for the initial validation, and the cross-validation step of the method returned 84.6% of correctly classified samples. An excellent separation was also obtained for the differentiation of spirits producers, 100% in initial classification, and 95.7% in cross-validation, respectively. For the varietal recognition, the best differentiation was achieved for apricot and pear distillates, a 100% discrimination being obtained in both classifications (initial and cross-validation). Good classification percentages were also obtained for plum and apple distillates, where models with 88.2% and 82.4% in initial and cross-validation, respectively, were achieved for plum differentiation. A similar value in the cross-validation procedure was reached for the apple spirits. The lowest classification percent was obtained for quince distillates (76.5% in initial classification followed by 70.4% in cross-validation). Our results have high practical importance, especially for trademark recognition, taking into account that fruit distillates are high-value commodities; therefore, the temptation of “fraud”, i.e., by passing regular distillates as branded ones, could occur.

FT-Raman spectroscopy represents an environmentally friendly technique, suitable for the analysis of high-water content food matrices, like wines, due to its relatively weak water bending mode in the fingerprint region. Based on metabolomics applied to FT-Raman spectra, this study presents the classifications achieved for a sample set comprising 126 wines, originated from Romania and France, with respect to cultivar, geographical origin and vintage. Cultivar recognition was successfully performed among four varieties (Sauvignon, Riesling, Chardonnay, Pinot Gris) while subtle particularities exiting between the Chardonnay wines, coming from the two countries, because of terroir influences were pointed out. The obtained separations of 100% in both initial and cross-validation procedure for geographical differentiation between the two origin countries, as well as, among the three Romanian areas (Transylvania, Muntenia and Moldova) were also discussed. Apart of this, the limitations and the importance of choosing a meaningful data set, in terms of representativity for each classification criterion, are addressed in the present work.

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