Study of signal process of gas sensor array with nonlinear principal component analysis

Abstract

Gas sensor array is a useful device to enhance the selectivity of gas detectors and to identify the components of gas mixture. The key step for processing signal from a gas sensor array is to extract the signal feature and make pre-classification for tested gases. Conventional Principal Component Analysis (PCA) is widely used for this purpose. However, conventional PCA is a linear and variance-covariance matrix based technique and it is therefore not strictly applicable for processing the gas sensor array signals that exhibit significant non-linear behavior. Thus, in this paper, non-linear PCA (NPCA) algorithm is introduced to process the gas sensor array signals to adapt to the non-linear characteristics. The signals we processed are the responses of a micro-hotplate (MHP) based integrated gas sensor array to a CO and NO2 binary gas mixture. The gas sensor array, consisted of four SnO2 thin-film sensing elements, was fabricated with integrated circuit (IC) technology and micromachining on silicon substrate. The recognition results of NPCA and conventional PCA are compared in this paper.