A new combined transient extraction method coupled with WO3 gas sensors for polluting gases classification. Issue 5 (1st October 2021)

• Record Type: Journal Article
• Authors: Faleh, Rabeb; Gomri, Sami; Aguir, Khalifa; Kachouri, Abdennaceur
• Is Part Of: Sensor review. Volume 41:Issue 5(2021)
• Page Start: 437
• Page End: 448
• Publication Date: 2021-10-01
• Subjects: Electronic nose -- Combined features -- Gas sensors -- ELM classifier -- Support vector machine SVM; Sensor systems -- Periodicals; Detectors -- Industrial applications -- Periodicals; Engineering instruments -- Periodicals; 681.2
• DOI: 10.1108/SR-02-2021-0066
• Languages: English
• ISSNs: 0260-2288

Abstract

Abstract : Purpose: The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors. Design/methodology/approach: To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array. Findings: The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF. Originality/value: Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.