LAUSR

BACKGROUND The spirit drinks industry is one of the largest in the world. Fruit distillates require adequate analysis methods combined with statistical tools to build differentiation models, according to distinct criteria (geographical and botanical origin, producers' fingerprint, respectively). Over time a database of alcoholic beverages fingerprint can be generated, being very important for products safety and their authenticity control. RESULTS To control the distillates' geographical origin, LDA revealed that the cross-validation classification was correct for 88.2% of samples, but PLS-DA was slightly better suitable for this purpose, with a correct classification rate of 91.2%. LDA effectiveness was proven for the trademark fingerprint differentiation, which was achieved at 93.5%, compared to 89.1% for PLS-DA. The principal predictors obtained by LDA were the same both for geographical origin and producer differentiation: B, δ13 C, Na, Cu, Ca and Be, highlighting the fact that in the production process of distillates, each producer used fruits coming from respective specific region. Through PLS-DA, some of the discrimination markers were the same for geographical origin and producers identification, but others were completely specific: the Rare Earth Elements Eu and Er only for geographical origin differentiation, and Cu solely as predictor for producer's identification. Regarding distillates fruit variety, the correct discrimination rates of plum spirits from the rest were 84.2% for PLS-DA, and 63% for LDA. CONCLUSION LDA and PLS-DA were suitable for differentiation models development of fruits spirits according to geographical region, producer and the fruit variety based on isotopic and elemental fingerprint. This article is protected by copyright. All rights reserved.