Comparison of several chemometric techniques for the classification of orujo distillate alcoholic samples from Galicia (northwest Spain) according to their certified brand of origin

Rodríguez, Roberto Iglesias; Delgado, Manuel Fernández; García, Julia Barciela; Crecente, Rosa María Peña; Martín, Sagrario García; Latorre, Carlos Herrero
July 2010
Analytical & Bioanalytical Chemistry;Jul2010, Vol. 397 Issue 6, p2603
Academic Journal
This paper has a double objective. The first goal was to develop an authentication system to differentiate between traditional orujo alcoholic distillates with and without a certified brand of origin (CBO). Owing to their low price and quality, samples without a CBO can be used as substrates for falsification of genuine CBO ones. The second objective was to perform a comparison of the abilities of the different chemometric procedures employed for this classification. The classification was performed on the basis of the chemical information contained in the metal composition of the orujo distillates. Eight metals determined by electrothermal atomic absorption spectrometry and inductively coupled plasma optical emission spectrometry were considered (Ca, Cd, Cr, Cu, K, Mg, Na and Ni). After the appropriate pretreatment, the data were processed using different chemometric techniques. In the first stage, principal component analysis and cluster analysis were employed to reveal the latent structure contained in the data. Once it had been demonstrated that a relation exists between the metal composition and the raw materials, and not between the metal composition and the distillation systems employed for the orujo production, the second step consisted in the comparative application of different supervised pattern recognition procedures (such as linear discriminant analysis, K-nearest neighbours, soft independent modelling of class analogy, UNEQ and different artificial neural network approaches, including multilayer feed-forward, support vector machines, learning vector quantization and probabilistic neural networks). The results showed the different capabilities of the diverse classification techniques to discriminate between Galician orujo samples. The best results were those provided by probabilistic neural networks, in which the correct recognition abilities for CBO classes and without CBO classes were 98.6 ± 3.1 and 98.0 ± 4.5%; the prediction results were 87.7 ± 3.3 and 86.2 ± 5.0%, respectively. The usefulness of chemical metal analysis in combination with chemometric techniques to develop a classification procedure to authenticate Galician CBO orujo samples is demonstrated.


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