کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1244027 | 969679 | 2007 | 6 صفحه PDF | دانلود رایگان |
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.
Journal: Talanta - Volume 72, Issue 1, 15 April 2007, Pages 263–268