Impact of feature reduction and operating temperature on gas identification

Abstract

Tin-oxide based gas sensor requires an operating temperature typically in the range of 200 °C to 400 °C and its performance dependents on this temperature. In this paper a deep examination has been made to analyze the best operating temperature suitable for gas identification application in which an array of sensors is used along with an appropriate feature reduction algorithm. The two most common feature reduction algorithms for gas classification are principal component analysis (PCA) and linear discriminant analysis (LDA); both of them have been used in this analytical work. The feature reduction is followed by a binary decision tree (BDT) or K-nearest neighbor (KNN) based classifier. Results obtained with data from an array of sensors used for detecting C6H6, CH2O, CO, NO2 and SO2 indicates that at 400 °C the BDT can classify 100% of gases after LDA based feature reduction, whereas KNN can classify 100% of gases at 200 °C and 300 °C using data before and after feature reduction. Furthermore, experimental results from the given sensor data suggest that with and without considering the operating temperature the BDT can classify 96% of gases using first four LDA components. While KNN can classify 98% to 99% of gases using first four LDA or first five PCA components of resulting data obtained after feature reduction. Thus, after LDA-based feature reduction both classifiers provide superior identification with minimum number of components. 2006-2015 Asian Research Publishing Network (ARPN).