Citation: Hui Hua Yang, Feng Qin, Qiong Lin Liang, Yong Wang, Yi Ming Wang, Guo An Luo. LapRLSR for NIR spectral modeling and its application to online monitoring of the column separation of Salvianolate[J]. Chinese Chemical Letters, ;2007, 18(7): 852-856. doi: 10.1016/j.cclet.2007.05.023 shu

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

a Analysis Center, Tsinghua University, Beijing 100084, China;
b College of Computer Science and Control, Guilin University of Electronic Technology, Guilin 541004, China;
c Modern Engineering Center for Traditional Chinese Medicine, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China

Received Date: 3 April 2007

Fund Project: The authors acknowledge Bruker Optics Inc. for providing NIR spectrometer for this experiment. This work was funded by National Key Technology R&D Program of China (No. 2004BA721A13), Major State Basic Research Development of China (973 Program) (No. 2005CB523503), Chinese Postdoctoral Fund (No. 20060390494) and Guangxi Science Fund (No. 0542037).

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.
- Laplacian regularized least squares regression (LapRLSR),
- PLS,
- NIR,
- Salvianolate
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