A novel odor filtering and sensing system combined with regression analysis for chemical vapor quantification
A novel odor filtering and sensing system combined with regression analysis for chemical vapor quantification
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DOI：
10.1016/j.snb.2014.04.022
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发表时间：
2014-09-01
影响因子：
8.4
通讯作者：
Hayashi, Kenshi
中科院分区：
文献类型：
--
作者：
Jha, Sunil K.;Hayashi, Kenshi
设计了一种基于聚合物、碳分子筛、微陶瓷加热器和金属氧化物半导体（MOS）气体传感器阵列的先进气味过滤和传感系统，用于挥发性有机化合物（VOCs）的定量识别。MOS传感器电阻由于化学蒸气吸附在过滤材料和解吸后，测量五个目标挥发性有机化合物，包括丙酮，苯，乙醇，戊醛，丙烯酸在不同的浓度在3和500份每百万份（ppm）。两种回归方法，特别是基于最小二乘准则的线性回归分析和基于核函数的支持向量回归（SVR）已被用于建模传感器电阻与VOCs浓度。使用散点图和斯皮尔曼等级相关系数（rho）来研究传感器电阻对蒸汽浓度的依赖性强度，并在回归分析之前寻找用于VOC量化的最佳过滤材料。定量识别效率的回归方法已被评估的基础上的决定系数R-2（R平方）和相关值。以碳分子筛（carboxen-1012）为过滤材料进行蒸汽解吸后的MOS传感器电阻导致使用基于径向基核的SVR方法的丙烯酸实际浓度与估计浓度之间的R平方（R-2=0.9957）和相关性（rho=1.00）的最大值。(C)2014爱思唯尔有限公司版权所有。
An advanced odor filtering and sensing system based on polymers, carbon molecular sieves, microceramic 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 (rho) 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 R-2 (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 (R-2=0.9957) and correlation (rho=1.00) between the actual and estimated concentration for propenoic acid using radial basis kernel based SVR method. (C) 2014 Elsevier B.V. All rights reserved.