A practical approach for near infrared spectral quantitative analysis of complex samples using partial least squares modeling  

作　　者：ZhiChao Liu  Xiang Ma  YaDong Wen  Yi Wang  WenSheng Cai XueGuang Shao 

机构地区：[1] Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin, 300071, China [2] Hongta Group, R&D Center, Yuxi, 653100, China

出　　处：《Science China Chemistry》2009年第7期1021-1027,共7页中国科学（化学英文版）

基　　金：Supported by the National Natural Science Foundation of China (Grant Nos. 20775036 & 20835002)

摘　　要：The number of latent variables (LVs) or the factor number is a key parameter in PLS modeling to obtain a correct prediction. Although lots of work have been done on this issue, it is still a difficult task to determine a suitable LV number in practical uses. A method named independent factor diagnostics (IFD) is proposed for investigation of the contribution of each LV to the predicted results on the basis of discussion about the determination of LV number in PLS modeling for near infrared (NIR) spectra of complex samples. The NIR spectra of three data sets of complex samples, including a public data set and two tobacco lamina ones, are investigated. It is shown that several high order LVs constitute main contributions to the predicted results, albeit the contribution of the low order LVs should not be neglected in the PLS models. Therefore, in practical uses of PLS for analysis of complex samples, it may be better to use a slightly large LV number for NIR spectral analysis of complex samples.The number of latent variables (LVs) or the factor number is a key parameter in PLS modeling to obtain a correct prediction. Although lots of work have been done on this issue, it is still a difficult task to determine a suitable LV number in practical uses. A method named independent factor diagnostics (IFD) is proposed for investigation of the contribution of each LV to the predicted results on the basis of discussion about the determination of LV number in PLS modeling for near infrared (NIR) spectra of complex samples. The NIR spectra of three data sets of complex samples, including a public data set and two tobacco lamina ones, are investigated. It is shown that several high order LVs constitute main contributions to the predicted results, albeit the contribution of the low order LVs should not be neglected in the PLS models. Therefore, in practical uses of PLS for analysis of complex samples, it may be better to use a slightly large LV number for NIR spectral analysis of complex samples.

关 键 词：number of LATENT variables partial least SQUARES (PLS) regression NEAR-INFRARED (NIR) spectroscopy TOBACCO LAMINA cross validation (CV) 

分 类 号：O657.33[理学—分析化学]