Optimal configuration of a thermally cycled gas sensor array with neural network pattern recognition

Abstract

Abstract Through the application of novel thermal modulation, pre-processing and feature extraction techniques the performance of an 8-element tin oxide gas sensor array has been significantly enhanced when applied to the task of classifying the aromas of three loose leaf teas. Array signatures were generated by thermally cycling the sensor array over the temperature range 250–500°C, whilst exposing the array to the odorous headspace of the three teas. The application of thermal modulation and an enhanced feature extraction algorithm, generating 208 parameters, proved to be highly successful giving a cross-validated classification rate of 90% for unseen samples of the three classes of tea. In comparison, a fixed temperature steady state metric, based upon the same array of sensors, yielded a cross-validated classification rate of only 69%. Furthermore, using a novel genetic algorithm optimisation technique to identify a near-optimal sensor parameter configuration for the task of tea classification, it was shown that a correct classification rate of 93% could be achieved with only 21 dynamic parameters.