Article ID Journal Published Year Pages File Type
1244027 Talanta 2007 6 Pages PDF
Abstract

The metal content of 54 commercialized wines (30 samples from Tacoronte-Acentejo DO (class T) and 24 Valle de la Orotava DO (class O) wines) was performed by ICP-OES (Al, Ba, Cu, Fe, Mn, Sr, Zn, Ca, K, Na and Mg) and GF-AAS (Ni and Pb). Wine samples were processed by dry ashing followed by solution with 5% nitric acid. Metals were considered as suitable descriptors to differentiate between T and O classes. Supervised learning pattern recognition procedures were applied. Linear discriminant analysis (LDA) led to good results up to about 90% of correct classification. In order to improve the results, another kind of algorithms able to model non-linear separation between classes was considered: Probabilistic Neural Networks. Accordingly, excellent results were obtained, leading to sensitivities and specificities higher than 95% for the two classes.

Related Topics
Physical Sciences and Engineering Chemistry Analytical Chemistry
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