多项式偏最小二乘法对非线性体系红外谱图的分析  

Multi-Component Analysis of FTIR Spectra of Non-Linear System Using Polynomial Partial Least Squares Method

作　　者：张琳[1] 张黎明[1] 李燕[1] 王晓斐[1] 胡兰萍[1] 王俊德[1] 

机构地区：[1]南京理工大学化工学院现代光谱研究室,江苏南京210014

出　　处：《光谱学与光谱分析》2006年第4期620-623,共4页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金(20175008);教育部博士后科学基金;南京理工大学青年学者基金(Njust200303)资助

摘　　要：文章利用了一种非线性模型多项式偏最小二乘法(PPLS),结合傅里叶变换红外光谱遥感技术,对大气中的五组分混合体系进行了同时分析。并与偏最小二乘法(PLS)得到的结果进行了比较,PPLS显示出较好的处理非线性数据的能力。尤其是对混合物中的苯和氯仿的预测,均方根预测误差(RMSEP)分别是0.043和0.087,用PLS预测相应的RMSEP为0.402和0.842。PPLS的这一预测精度,可以满足遥感傅里叶变换红外光谱对大气中有毒气体的实时、在线监测的需要。同时PPLS可以用较少的潜变量对变量进行解释,显示出PPLS模型的稳健性和简单化。A non-linear algorithm, polynomial PLS was applied to the simultaneous analysis of OP-FTIR spectra of a five-component system whose FTIR spectra were seriously overlapped. The results were compared with the one obtained from PLS. PPLS yielded good performance, especially for the prediction of benzene and chloroform. RMSEP（root mean squared error of prediction） of benzene and chloroform in PPLS model were 0. 043 and 0. 087 and the corresponding values in PLS were 0. 402 and 0, 842, respectively. Meanwhile, variance was accounted by PPLS with fewer latent variables, which indicates the simplicity and robustness of the model. The successful application of PPLS to non-linear system was meaningful for the use of remote sensing FTIR in air monitoring.

关 键 词：多项式偏最小二乘法 非线性模型 多组分分析 FTIR 大气监测 

分 类 号：O657.3[理学—分析化学]