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

Sunil K. Jha, Kenshi Hayashi

研究成果: Contribution to journalArticle査読

15 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)269-287
ページ数19
ジャーナルSensors and Actuators, B: Chemical
200
DOI
出版ステータス出版済み - 9 2014

All Science Journal Classification (ASJC) codes

  • 電子材料、光学材料、および磁性材料
  • 器械工学
  • 凝縮系物理学
  • 表面、皮膜および薄膜
  • 金属および合金
  • 電子工学および電気工学
  • 材料化学

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