Comparative study of sampling systems combined with gas sensors for wine discrimination

Abstract

A comparison among several sampling systems usually employed in an electronic nose is performed in this paper in order to improve the performance of this instrument for wine discrimination. Three different sampling methods have been studied: static headspace with dynamic injection (HS), purge and trap (P&T) and solid-phase micro-extraction (SPME). These electronic noses have been developed in order to discriminate five different Spanish wines coming from different grape varieties and elaboration processes. Linear techniques as principal component analysis (PCA) and nonlinear ones as probabilistic neural networks (PNN) have been used for pattern recognition. Results show that the best discrimination is achieved with P&T and SPME, although the highest response of sensors is obtained by the HS method.