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

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.

Preliminary chemometric study on the use of honey as an environmental marker in Galicia (northwestern Spain).

Thirteen metal elements were determined in 40 honey samples from Galicia with different environmental origins: rural, urban, and industrial areas. The data set of the honey metallic profiles was studied with a double purpose: first, to make a preliminary evaluation of honey as an environmental indicator in Galicia with the aim of monitoring pollution and, second, to compare the different capabilities of diverse pattern recognition prediction procedures for modeling the environmental surrounding of the hive. A certain level of similarity for urban and industrial samples was obtained using principal component analysis and cluster analysis, whereas significant differences for urban and industrial honeys were found in relation to rural honey samples. Different classification rules to associate metal content of honeys with their environmental surrounding were obtained by chemometric pattern recognition procedures. In general, the classification methods developed by neural networks provided better results than the traditional pattern recognition procedures. The metal profiles of honey seem to provide sufficient information to enable categorization criteria for classifying samples according to their environmental surrounding. Thus, honey could be a potential pollution indicator for the Galician area.