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.

Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques.

A method has been developed to authenticate aged high-quality wines and to quantify their potential adulterations through multivariate analysis and regression techniques applied to the obtained RGB digital images. Wines of pure Gran Reserva, Crianza, and Joven Rioja as well as synthetic adulterated Gran Reserva samples were studied. Digital images were obtained by a single and inexpensive lab-made device. Each sample was characterized by means of the 256 channels intensities from the RGB-colorgram. Multivariate image analysis revealed differences among the wine classes, and between genuine-aged and adulterated samples. Partial least squares regression was used to develop a model for estimating the adulteration degree of Gran Reserva wines. The model achieved good prediction (RMSEP = 1.6), appropriate precision (RSD = 2.5%) and suitable LOD (2.3%) to quantify cost-effective adulterations. The present method, due to simplicity and low cost, could provide an appropriate alternative to the traditional chemical authentication methods.

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.

Chemometric classification of potatoes with protected designation of origin according to their producing area and variety.

Potatoes from Galicia (northwestern Spain) are subjected to a Protected Geographic Indication (PGI) according to European legislation. Ten trace elements (Li, Na, K, Rb, Ca, Fe, Mg, Mn, Cu, and Zn) have been determined by atomic spectrometry in two sets of potato samples: Geo-Origin.set and Variety.set. The first data set is composed of samples of the only variety authorized by PGI (Kennebec) with two geographical origins: Galician and non-Galician. The second set corresponds to samples from different varieties but with only Galician geographical origin. Chemometric pattern recognition techniques have been applied to the study of potato geographical and varietal origins in relation to their capability for translocating metals from soil to tuber. Also, authentication models for classifying potato samples with Galician PGI based on metal fingerprints have been developed. The results obtained showed that samples of the same variety, Kennebec, have different metal fingerprints when they have been produced in different geographic locations. Also, diverse potato varieties cultivated on equal geographic Galician origin presented different metal profiles as well. Therefore, it can be concluded that classification studies on the differentiation of geographical origin of foods should take into account information of production area together with varietal data. Otherwise, classification obtained on the basis of the geographical origin could be due to the different variety or vice versa. Finally, two models were constructed for Kennebec Galician samples against Kennebec from other origins as well as against other varieties cultivated in Galicia (Liseta and Baraka). Both models achieved adequate classification rates (93-100%), good sensitivities, and total specificities (100%), allowing the fraud detection in the PGI label.