Classification of vegetable oils by linear discriminant analysis of Electronic Nose data

Abstract

The purpose of this work was to attempt to classify edible vegetable oils by chemometric treatment of the data obtained from an array of gas sensors. A commercial Electronic Nose (FOX 2000) comprising six metal oxide semiconductor sensors was used to generate a pattern of the volatile compounds present in the samples. Linear discriminant analysis (LDA) was applied to the patterns generated to achieve several classification tasks. The procedure for obtaining the signals and the chemometric treatment are rapid and simple, and provide classification and prediction capabilities higher than 95%.