DETECTION OF VAPORS AND ODORS FROM A MULTISENSOR ARRAY USING PATTERN-RECOGNITION .1. PRINCIPAL COMPONENT AND CLUSTER-ANALYSIS

Mathematical expressions describing the response of individual sensors and arrays of tin oxide gas sensors are derived from a barrier-limited electron mobility model. From these expressions, the fractional change in conductance is identified as the optimal response parameter with which to characterize sensor array performance instead of the more usual relative conductance. In an experimental study, twelve tin oxide gas sensors are exposed to five alcohols and six beverages, and the responses are studied using pattern-recognition methods. Results of regression and supervised learning analysis show a high degree of colinearity in the data with a subset of only five sensors needed for classification. Principal component analysis and clustering methods are applied to the response of the tin oxide sensors to all the vapours. The results show that the theoretically derived normalization of the data set substantially improves the classification of vapours and beverages. The individual alcohols are separated out into five distinct clusters, whereas the beverages cluster into only three distinct classes, namely, beers, lagers and spirits. It is suggested that the separation may be improved further by employing other sensor types or processing techniques.