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Journal of Sensors
Volume 2017 (2017), Article ID 9851406, 8 pages
Research Article

Orthogonal Signal Correction to Improve Stability Regression Model in Gas Sensor Systems

University of Lorraine, LCOMS, EA7306, 57070 Metz, France

Correspondence should be addressed to Maryam Siadat; rf.eniarrol-vinu@tadais.mayram

Received 5 March 2017; Revised 15 June 2017; Accepted 20 June 2017; Published 1 August 2017

Academic Editor: Heinz C. Neitzert

Copyright © 2017 Rachid Laref et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Metal oxide sensors are the most often used in electronic nose devices because of their high sensitivity, long lifetime, and low cost. However, these sensors suffer from a lack of response stability making the electronic nose systems useless in industrial applications. The sensor instabilities are particularly caused by incomplete recovery process producing gradual drifts in the sensor responses. This paper focuses on a signal processing method combining baseline manipulation and orthogonal signal correction technique in order to reduce effectively the drift impact from the sensor outputs. The proposed signal processing is explored using experimental data obtained from a gas sensor array responding to various concentrations of pine essential oil vapors. Partial Least Square method is then applied on the corrected dataset to establish a regression model for the estimation of gas concentration. In this work, we show essentially how our drift correction approach can help to improve significantly the stability of the regression model, while ensuring good accuracy.