Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set
This study demonstrated particle size effect on the measurement of saikosaponin A in Bupleurum chinense DC. by near infrared reflectance (NIR) spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Analytical Methods in Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2015/583841 |
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| author | Zhisheng Wu Min Du Xinyuan Shi Bing Xu Yanjiang Qiao |
| author_facet | Zhisheng Wu Min Du Xinyuan Shi Bing Xu Yanjiang Qiao |
| author_sort | Zhisheng Wu |
| collection | DOAJ |
| description | This study demonstrated particle size effect on the measurement of saikosaponin A in Bupleurum chinense DC. by near infrared reflectance (NIR) spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granularity on NIR spectra were investigated, which showed to be wavelength dependent. NIR intensity was proportional to particle size in the first combination-overtone and combination region. Local partial least squares model was constructed separately for every kind of samples, and data-preprocessing techniques were performed to optimize calibration model. The 65-mesh model exhibited the best prediction ability with root mean of square error of prediction (RMSEP) = 0.492 mg·g−1, correlation coefficient RP=0.9221, and relative predictive determinant (RPD) = 2.58. Furthermore, a granularity-hybrid calibration model was developed by incorporating granularity variation. Granularity-hybrid model showed better performance than local model. The model performance with 65-mesh samples was still the most accurate with RMSEP = 0.481 mg·g−1, RP=0.9279, and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set. |
| format | Article |
| id | doaj-art-11dbd5576ce144d298c4731f7e9fe3a6 |
| institution | Kabale University |
| issn | 2090-8865 2090-8873 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Analytical Methods in Chemistry |
| spelling | doaj-art-11dbd5576ce144d298c4731f7e9fe3a62025-02-03T06:07:34ZengWileyJournal of Analytical Methods in Chemistry2090-88652090-88732015-01-01201510.1155/2015/583841583841Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration SetZhisheng Wu0Min Du1Xinyuan Shi2Bing Xu3Yanjiang Qiao4Beijing University of Chinese Medicine, Beijing 100102, ChinaWorld Federation of Chinese Medicine Societies, Beijing 100101, ChinaBeijing University of Chinese Medicine, Beijing 100102, ChinaBeijing University of Chinese Medicine, Beijing 100102, ChinaBeijing University of Chinese Medicine, Beijing 100102, ChinaThis study demonstrated particle size effect on the measurement of saikosaponin A in Bupleurum chinense DC. by near infrared reflectance (NIR) spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granularity on NIR spectra were investigated, which showed to be wavelength dependent. NIR intensity was proportional to particle size in the first combination-overtone and combination region. Local partial least squares model was constructed separately for every kind of samples, and data-preprocessing techniques were performed to optimize calibration model. The 65-mesh model exhibited the best prediction ability with root mean of square error of prediction (RMSEP) = 0.492 mg·g−1, correlation coefficient RP=0.9221, and relative predictive determinant (RPD) = 2.58. Furthermore, a granularity-hybrid calibration model was developed by incorporating granularity variation. Granularity-hybrid model showed better performance than local model. The model performance with 65-mesh samples was still the most accurate with RMSEP = 0.481 mg·g−1, RP=0.9279, and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set.http://dx.doi.org/10.1155/2015/583841 |
| spellingShingle | Zhisheng Wu Min Du Xinyuan Shi Bing Xu Yanjiang Qiao Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set Journal of Analytical Methods in Chemistry |
| title | Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set |
| title_full | Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set |
| title_fullStr | Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set |
| title_full_unstemmed | Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set |
| title_short | Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set |
| title_sort | robust pls prediction model for saikosaponin a in bupleurum chinense dc coupled with granularity hybrid calibration set |
| url | http://dx.doi.org/10.1155/2015/583841 |
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