Identification of Counterfeit Vodka by Synchronous Fluorescence Spectroscopy and Chemometric Analysis
Rômulo R. Facci, Paulo Sergio De Oliveira Cezario, Jefferson Santos de Gois, Aderval S. Luna, Wagner F. Pacheco
Abstract
This work presents the development and application of a method based on synchronous fluorescence coupled with chemometric tools to classify different vodka samples. The chemometric methods used encompasses partial least squares – discriminant analysis (PLS-DA), k-nearest neighbor (KNN), and support vector machine (SVM). A total of 18 authentic vodkas (3 brands, 6 of each brand, all donated by the producers) and six counterfeit vodka samples were used to validate the method. The samples suspect of falsification were seized in a police action that has occurred in the state of São Paulo, Brazil named Operation Chicago. The spectrofluorescence dataset was processing using the centering on the mean before to apply principal component analysis (PCA), which did not correctly discriminate the samples. Considering that the PCA suffers from the presence of outliers, robust PCA was applied, which detected outliers. After this undesirable detection, the transformation of the spatial signal was applied, and the robust PCA has applied again and did not detect outliers. The PLS-DA and SVM were able to classify all the vodka samples correctly in authentic and counterfeit. Both methods showed the highest values for the accuracy and Kappa index, as well the sensitivity and specificity, respectively. However, the KNN has not been able to correctly classify the samples. Finally, only the SVM based on radial base function was able to classify all brands of vodka samples correctly using synchronous fluorescence spectroscopy.