Optimization of Near Infrared Variable Selection Method Based on Multivariate Detection Limit
10.11842/wst.2014.05.003
- VernacularTitle:基于多变量检测限的模型变量筛选方法研究
- Author:
Yanfang PENG
;
Xinyuan SHI
;
Yang LI
;
Luwei ZHOU
;
Yanling PEI
;
Guodong HUA
;
Zhisheng WU
;
Yanjiang QIAO
- Publication Type:Journal Article
- Keywords:
Qing-Kai-Ling injection;
iPLS;
BiPLS;
mwPLS;
multivariate detection limit
- From:
World Science and Technology-Modernization of Traditional Chinese Medicine
2014;(5):960-965
- CountryChina
- Language:Chinese
-
Abstract:
This study was aimed to optimize the near infrared (NIR) variable selection method based on multivariate detection limit (MDL). Using Qing-Kai-Ling (QKL) injection as object, three variable selection methods (interval par-tial least-squares, iPLS; backward interval partial least squares, BiPLS; moving window interval partial least squares, mwPLS) were used to establish the PLS models of baicalin in QKL injection, respectively. The prediction ability of different variable selection method was compared. MDL of all models were calculated in contrast to the MDL value of full spectra PLS model, to select optimal variable selection method. The results showed that different variable selec-tion methods had different prediction ability. Among them, iPLS had the best performance which determination coef-ficient of prediction (Rpre2) and the root mean square errors of prediction (SEP) were 0.996 5 and 602.3 μg·mL-1, re-spectively. All MDLs of different variable selection methods were reduced compared with the full spectra PLS model. The value of iPLS was the lowest comes to be 1.19 μg·mL-1. The results above indicated that the best variable se-lection method for baicalin in QKL injection was iPLS. MDL theory took the error of calibration and validation set and the leverage of external sample into account, which can comprehensively evaluate model detection performance compared to the classic chemical indicator parameters. This method was particularly suitable for the variable selec-tion method optimization of NIR quantitative model of low concentration sample such as Chinese herbal medicine.
