Multivariate data visualization methods based on elemental analysis of wines by atomic absorption spectrometry

Abstract

The contents of five metals (Cu, Mn, Fe, Cd, and Pb) in several red and white wines originating from different regions of Serbia were determined by flame and graphite furnace atomic absorption spectrometry. The data were processed using chemometric techniques. Principal component and factor analysis were applied in order to highlight the relations between the elements and, after data reduction, three main factors controlling variability were identified. Application of hierarchical cluster analysis to the studied wines indicated differentiation of the samples belonging to different origins. No discrimination between red and white wines was found.

Keywords

• principal component analysis
• factor analysis
• cluster analysis
• metals
• wines
• classification