Multi-feature Optimization of a Gas Sensor Array to Improve Component and Concentration Detection Performance of VOC Mixtures

Abstract

Many studies focus on feature extraction and selection of gas sensor arrays for gas identification. In this work, we intended to find a feature subset obtained by selecting the most important features for simultaneously improving component and concentration detection performance of a gas sensor array to three harmful VOCs (toluene, methanol, and ethanol) and their mixtures. First, 30 features were extracted from 6 sensors’ responses to form a multi-feature set. Then, two feature selection methods based on Wilks’ Λ-statistic and random forest were employed to obtain the best feature combination. Seven out of 30 features were finally selected to form the optimal feature set. The gas identification accuracy is 94.3%, and the concentration estimation error is 0.79 ppm (RMSE). Through feature selection, not only qualitative and quantitative analyses performance of VOCs mixtures are significantly improved, but also system complexity (6 to 4 sensors) and computation cost (by about 15%) are effectively reduced. Wilks’ Λ-statistic- and random forest-based methods were established to obtain the most important features for VOCs mixtures detection with gas sensor array. 7 out of 30 features are selected and both component and concentration detection performances were remarkably improved. The detection accuracy and RMSE of detecting harmful VOCs and their mixtures are 94.3% and 0.79 ppm.