主成分-线性判别法对大气易挥发性有机化合物的预警  

Alarm on Volatile Organic Compounds in the Atmosphere with Principal Component Analaysis-Linear Discriminat Analysis

作　　者：胡兰萍[1,2] 张琳[1] 李燕[1] 张黎明[1] 王俊德[1] 

机构地区：[1]南京理工大学现代光谱研究室 [2]南通大学化学化工学院分析化学实验室,南通226006

出　　处：《分析化学》2007年第3期345-349,共5页Chinese Journal of Analytical Chemistry

基　　金：国家自然科学基金(No.20175008);中国博士后科学基金(No.2003034386);南通市科技项目基金(No.K2006007)资助

摘　　要：应用遥感傅里叶变换红外光谱,采用主成分提取-线性判别分析(PCA-LDA)技术,对丙酮、二氯甲烷、甲苯、苯、氯仿和甲醇等六组分的任意混合体系进行定性鉴别。被选用的这6种大气有毒有机化合物的红外光谱图相互间存在着严重的混叠,并和反向传播人工神经网络(BP-ANN)的预测结果进行了比较。PCA-LDA的鉴别判对率达92.2%,识别率94.4%,误判率7.8%;BP-ANN分别为91.1%、95.6%和8.9%。结果表明PCA处理克服了LDA对多变量数据预测的局限性,预测性能和BP-ANN相当。鉴于BP-ANN计算耗时和繁琐,PCA-LDA模型被确定为建立VOCs预警模型最适当的方法。The system, which contained one to six components including acetone, methylene chloride, toluene, benzene, chloroform and methanol, was analyzed qualitatively with the combination of principal component analasis-linear discriminate analysis（PCA-LDA） and remote sensing FTIR technique. There are FTIR spectra overlap ped seriously each other for the six air toxic organic compounds selected, The prediction results of PCA-LDA and BP-ANN were compared. The ratio of correct recognition ratio, recognition ratio and error recognition ratio of PCA-LDA were 92.2% , 94.4% and 7.8% respectively. The corresponding values of BP-ANN were 91.1%, 95.6% and 8.9% , respectively. The results demonstrated that limitations of LDA were overcome with PCA and then the performance of LDA was improved by PCA. The prediction performance of PCA-LDA was comparable to BP-ANN. Considering time-consuming and fussy operation of BP-ANN, PCA-LDA was determined as the suitable method for the alarm on volatile organic compounds in the atmosphere.

关 键 词：主成分-线性判别分析 反向传播人工神经网络 定性分析 易挥发性有机化合物 遥感傅里叶变换红外光谱 

分 类 号：X831[环境科学与工程—环境工程]