| LapRLSR for NIR spectral modeling and its application to online monitoring of the column separation of Salvianolate |
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| 作者姓名： | Hui Hua Yang Feng Qin Qiong Lin Liang Yong Wang Yi Ming Wang Guo An Lu |
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| 作者单位： | Hui Hua Yang a，b，Feng Qin b，Qiong Lin Liang a，Yong Wang a，c，Yi Ming Wang a，Guo An Luo a，* 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 |
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| 基金项目： | 国家重点科技研究发展项目;国家重点基础研究发展计划(973计划);中国博士后科学基金;广西科学基金 |
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| 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.
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| 关 键 词： | 拉普拉斯算子 光谱分析 PLS NIR 丹参多酚酸盐 中药化学 |
| 收稿时间： | 3 April 2007 |
| 修稿时间： | 2007-04-03 |
LapRLSR for NIR spectral modeling and its application to online monitoring of the column separation of Salvianolate |
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| Hui Hua Yang Feng Qin Qiong Lin Liang Yong Wang Yi Ming Wang Guo An Lu.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. |
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| Authors: | Hui Hua Yang Feng Qin Qiong Lin Liang Yong Wang Yi Ming Wang Guo An Luo |
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| Institution: | 1. Analysis Center, Tsinghua University, Beijing 100084, China;2. College of Computer Science and Control, Guilin University of Electronic Technology, Guilin 541004, China;3. Modern Engineering Center for Traditional Chinese Medicine, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China |
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| 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. |
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| Keywords: | Laplacian regularized least squares regression (LapRLSR) PLS NIR Salvianolate |
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