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

Guangfen Wei; Zhenan Tang; Jun Yu; Philip C.H. Chan

doi:10.1117/12.444708

15 October 2001 Study of signal process of gas sensor array with nonlinear principal component analysis

Guangfen Wei, Zhenan Tang, Jun Yu, Philip C.H. Chan

Proceedings Volume 4601, Micromachining and Microfabrication Process Technology and Devices; (2001) https://doi.org/10.1117/12.444708
Event: International Symposium on Optoelectonics and Microelectronics, 2001, Nanjing, China

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.

Citation Download Citation

Guangfen Wei, Zhenan Tang, Jun Yu, and Philip C.H. Chan "Study of signal process of gas sensor array with nonlinear principal component analysis", Proc. SPIE 4601, Micromachining and Microfabrication Process Technology and Devices, (15 October 2001); https://doi.org/10.1117/12.444708

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