文献类型：期刊文献

英文题名：LapRLSR for NIR spectral modeling and its application to online monitoring of the column separation of Salvianolate

作者：Yang, Hui Hua; Qin, Feng; Liang, Qiong Lin; Wang, Yong; Wang, Yi Ming; Luo, Guo An

机构：[1]Tsing Hua Univ, Anal Ctr, Beijing 100084, Peoples R China;[2]Guilin Univ Elect Technol, Coll Comp Sci & Technol, Guilin 541004, Peoples R China;[3]E China Univ Sci & Technol, Sch Pharm, Modern Engn Ctr Tradit Chinese Med, Shanghai 200237, Peoples R China

年份：2007

卷号：18

期号：7

起止页码：852

外文期刊名：CHINESE CHEMICAL LETTERS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000248207000026)】；

语种：英文

外文关键词：Laplacian regularized least squares regression (LapRLSR); PLS; NIR; Salvianolate

摘要：A novel near infrared (NIR) modeling method-Laplacian regularized least squares regression (LapRLSR) was presented, which can take the advantage of many unlabeled spectra to promote the prediction performance of the model even if there are only few calibration samples. Using LapRLSR modeling, NIR spectral analysis was applied to the online monitoring of the concentration of salvia acid B in the column separation of Salvianolate. The results demonstrated that LapRLSR outperformed partial least squares (PLS) significantly, and NIR online analysis was applicable. (C) 2007 Guo An Luo. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved.