Recognition of benzene, toluene and xylene using TGS array integrated with linear and non-linear classifier

Andrzej Szczurek; Monika Maciejewska

doi:10.1016/j.talanta.2004.03.036

Recognition of benzene, toluene and xylene using TGS array integrated with linear and non-linear classifier

Talanta. 2004 Oct 20;64(3):609-17. doi: 10.1016/j.talanta.2004.03.036.

Authors

Andrzej Szczurek¹, Monika Maciejewska

Affiliation

¹ Ecologistics and Atmosphere Protection Group, Institute of Environmental Protection Engineering, Wroclaw University of Technology, Pl. Grunwaldzki 9, 50-377 Wroclaw, Poland.

PMID: 18969649
DOI: 10.1016/j.talanta.2004.03.036

Abstract

Three volatile organic compounds (VOCs): benzene, toluene and xylene were measured with an array of six Taguchi gas sensors in the air with variable humidity content. The recognition of single compounds was performed, based on measurement results. The principal component analysis (PCA) pointed at humidity as the main classification factor in the measurement data set. The linear discriminant analysis (LDA) was applied to overcome this drawback and enforce classification with respect to benzene, toluene or xylene. It was shown that discriminant function analysis (DFA), which is an LDA method allowed for 100% success rate in test samples recognition of benzene. It did not allow for accurate recognition of test samples of toluene or xylene. Following, the non-linear classifier, radial basis function neural network (RBFNN) was applied. A specific configuration of input 's was found, which provided for successful recognition of each single compound: benzene, toluene or xylene in air with variable humidity content.