Multi-element analysis of wines by ICP-MS and ICP-OES and their classification according to geographical origin in Slovenia.
Inductively coupled plasma mass spectrometry and optical emission were used to determine the multi-element composition of 272 bottled Slovenian wines. To achieve geographical classification of the wines by their elemental composition, principal component analysis (PCA) and counter-propagation artificial neural networks (CPANN) have been used. From 49 elements measured, 19 were used to build the final classification models. CPANN was used for the final predictions because of its superior results. The best model gave 82% correct predictions for external set of the white wine samples. Taking into account the small size of whole Slovenian wine growing regions, we consider the classification results were very good. For the red wines, which were mostly represented from one region, even-sub region classification was possible with great precision. From the level maps of the CPANN model, some of the most important elements for classification were identified.
National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia. Electronic address: vid.selih@ki.si.
Vid S
VS
Selih
National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia.
Martin
M
Sala
National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia.
Viktor
V
Drgan
D016002:Discriminant Analysis; D005843:Geography; D013058:Mass Spectrometry / Q000379:methods*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D017524:Slovenia; D013057:Spectrum Analysis / Q000379:methods*; D014131:Trace Elements / Q000032:analysis*; D014920:Wine / Q000032:analysis* / Q000145:classification
D014131:Trace Elements
Geographical origin; ICP-MS; ICP-OES; Multi-element determination; Multivariate statistics; Neural networks; Wine