| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1256054 | Chinese Chemical Letters | 2007 | 5 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Chemistry
Chemistry (General)
Authors
Hui Hua Yang, Feng Qin, Qiong Lin Liang, Yong Wang, Yi Ming Wang, Guo An Luo,