Development of an Electronic Nose to Identify and Classify Odours from Spirits Beverages

Abstract

This paper presents the development of an electronic nose (e-nose) prototype for classifying odours from alcoholic beverages, as well as butanol and methanol vapours. The e-nose mainly comprises an array of commercial gas sensors based on metal oxide semiconductor (MOS) technology. Instrumental measurements were based on obtaining the voltage response from the sensor array for each odorant sample. Then, these signals were pre-processed by Principal Component Analysis (PCA) and processed to achieve qualitative results. Three classification tools were used: a Multi-Layer Perceptron (MLP), a Self-Organizing Map (SOM) and Clustering Analysis (CA). The analysis by MLP showed a suitable prediction capability of 92.5 %, while, SOM and CA, being unsupervised techniques, were applied to establish certain odour patterns among the odorants samples. The results of this study support the potential of e-noses as reliable electronic instruments for qualitative analyses of odorants from different alcoholic beverages.