A novel odor filtering and sensing system combined with regression analysis for chemical vapor quantification

Abstract

An advanced odor filtering and sensing system based on polymers, carbon molecular sieves, micro-ceramic heaters and metal oxide semiconductor (MOS) gas sensor array has been designed for quantitative identification of volatile organic chemicals (VOCs). MOS sensor resistance due to chemical vapor adsorption in filtering material and after desorption are measured for five target VOCs including acetone, benzene, ethanol, pentanal, and propenoic acid at distinct concentrations in between 3 and 500 parts per million (ppm). Two kinds of regression methods specifically linear regression analysis based on least square criterion and kernel function based support vector regression (SVR) have been employed to model sensor resistance with VOCs concentration. Scatter plot and Spearman's rank correlation coefficient (ρ) are used to investigate the strength of dependence of sensor resistance on vapor concentration and to search optimal filtering material for VOCs quantification prior to the regression analysis. Quantitative recognition efficiency of regression methods have been evaluated on the basis of coefficient of determination R2 (R-squared) and correlation values. MOS sensor resistance after vapor desorption with carbon molecular sieve (carboxen–1012) as filtering material results the maximum values of R-squared (R2=0.9957) and correlation (ρ=1.00) between the actual and estimated concentration for propenoic acid using radial basis kernel based SVR method.