Machine learning-based discrimination of indoor pollutants using an oxide gas sensor array: High endurance against ambient humidity and temperature

Abstract

Machine learning (ML) methodologies were applied to detect and discriminate five indoor volatile organic compounds (VOCs) such as benzene, xylene, toluene, formaldehyde, and ethanol using a sensor array constructed of five In2O3-based semiconducting metal oxide (SMO) gas sensors. The sensor array was evaluated using principal component analysis (PCA) and neural network-based classification in terms of the gas sensor data type/amount, neural network algorithms, sensor combinations, and environmental factors. The PCA analyses indicated the limitations on the discrimination of VOCs under temperature- and/or humidity-interfered gas sensing environments. Gas detection/discrimination could be improved significantly by using three supervised algorithms, i.e., artificial neural networks (ANNs), deep neural networks (DNNs), and 1-dimensional convolutional neural networks (1D CNNs). The neural network algorithm prediction based on the entire gas sensing/purge transient data outperforms deep learning-assisted predictions based on partial gas sensing transients. Compared to 1D CNNs, DNNs are more appropriate in terms of training/validation/test datasets. The effects due to humidity variation are more significant than those due to temperature fluctuation. A 2-sensor mode combination can be exploited to replace the 5-sensor operation in ML-based applications. The indoor pollutants can be successfully discriminated even under the variation of ambient humidity and temperature by ML-based approaches.