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| 多项式偏最小二乘法对非线性体系红外谱图的分析 | |
| 引用本文： | 张琳,张黎明,李燕,王晓斐,胡兰萍,王俊德.多项式偏最小二乘法对非线性体系红外谱图的分析[J].光谱学与光谱分析,2006,26(4):620-623. |
| 作者姓名： | 张琳 张黎明 李燕 王晓斐 胡兰萍 王俊德 |
| 作者单位： | 1. 南京理工大学化工学院现代光谱研究室,江苏,南京,210014 2. 南京理工大学化工学院现代光谱研究室,江苏,南京,210014;南京大学化学化工学院,江苏,南京,210092 3. 南京理工大学化工学院现代光谱研究室,江苏,南京,210014;南通大学化学化工学院,江苏,南通,226007 |
| 基金项目： | 国家高技术研究发展计划(863计划) , 教育部博士后科学基金 , 南京理工大学校科研和教改项目 |
| 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. |
| 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 |
| 文章编号： | 1000-0593（2006）04-0620-04 |
| 收稿时间： | 2004-12-10 |
| 修稿时间： | 2005-04-20 |
Multi-Component Analysis of FTIR Spectra of Non-Linear System Using Polynomial Partial Least Squares Method |
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| ZHANG Lin,ZHANG Li-ming,LI Yan,WANG Xiao-fei,HU Lan-ping,WANG Jun-de.Multi-Component Analysis of FTIR Spectra of Non-Linear System Using Polynomial Partial Least Squares Method[J].Spectroscopy and Spectral Analysis,2006,26(4):620-623. | |
| Authors: | ZHANG Lin ZHANG Li-ming LI Yan WANG Xiao-fei HU Lan-ping WANG Jun-de |
| Institution: | 1. Laboratory of Advanced Spectroscopy, Nanjing University of Science and Technology, Nanjing 210014, China; 2. Department of Chemistry, Nanjing University, Nanjing 210092, China; 3. Department of Chemistry, Nantong University, Nantong 226007, China |
| Abstract: | 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. |
| Keywords: | Polynomial PLS Non-linear system Multi-component analysis FTIR Air monitoring |
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