Application and improvement of partial-least-squares in Fourier transform infrared spectroscopy

被引：0
|
作者：
Zhang, L [1 ]
Zhang, LM [1 ]
Li, Y [1 ]
Liu, BP [1 ]
Wang, XF [1 ]
Wang, JD [1 ]
机构：
[1] Nanjing Univ Sci & Technol, Lab Adv Spect, Nanjing 210014, Peoples R China
来源：
SPECTROSCOPY AND SPECTRAL ANALYSIS | 2005年 / 25卷 / 10期
关键词：
PLS; FTIR; improved algorithms;
D O I：
暂无
中图分类号：
O433 [光谱学];
学科分类号：
0703 ; 070302 ;
摘要：
Partial least squares(PLS) algorithm is an effective chemometric tool. It takes the advantages of multipal linear regression (NILR) and principal component regression (PCR), which makes Fourier transform infrared spectrometry (F-FIR) more powerful and useful. Accompanied with increasing use of FTIR, the algorithm is modified and corrected under different circumstances. The applications of PLS to FTIR were mentioned. Improved algorithms were presented, such as moving windows PLS(MWPLS), robust PLS (RPLS), weighted PLS(WPLS), and non-linear PLS. Data pre-processing, selection of variable, noise elimination and non-linear model of PLS were introduced.
引用
收藏
页码：1610 / 1613
页数：4
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