Concentration estimation of formaldehyde using metal oxide semiconductor gas sensor array-based e-noses
Abstract
Purpose
The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift.
Design/methodology/approach
In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with an embedded self-calibration module based on a threshold network is studied.
Findings
The nonlinear estimation problem of sensor array-based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network.
Originality/value
In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real-time indoor monitoring of formaldehyde.
Keywords
Acknowledgements
The authors would like to express their sincere appreciation to the anonymous reviewers for their insightful comments, which have greatly improved the quality of the paper. This work was funded by the Hong Kong Scholar Program (Grant No. XJ2013044) and China Postdoctoral Science Foundation (Grant No. 2014M550457).
Citation
Zhang, L., Tian, F., Peng, X., Yin, X., Li, G. and Dang, L. (2014), "Concentration estimation of formaldehyde using metal oxide semiconductor gas sensor array-based e-noses", Sensor Review, Vol. 34 No. 3, pp. 284-290. https://doi.org/10.1108/SR-05-2013-673
Publisher
:Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited