Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy
A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400–6200 cm-1) was identified, then the first deri...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2014-11-01
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| Series: | Journal of Innovative Optical Health Sciences |
| Subjects: | |
| Online Access: | http://www.worldscientific.com/doi/pdf/10.1142/S1793545814500114 |
| _version_ | 1818340383811174400 |
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| author | Weijian Lou Kai Yang Miaoqin Zhu Yongjiang Wu Xuesong Liu Ye Jin |
| author_facet | Weijian Lou Kai Yang Miaoqin Zhu Yongjiang Wu Xuesong Liu Ye Jin |
| author_sort | Weijian Lou |
| collection | DOAJ |
| description | A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400–6200 cm-1) was identified, then the first derivative spectra smoothed using a Savitzky–Golay filter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total flavonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSO-based LS-SVM models, the determination coefficients (R2) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has definite practice significance and application value. |
| first_indexed | 2024-12-13T15:42:02Z |
| format | Article |
| id | doaj.art-97e6299b43bf4bf5ba38d9e757c2e08a |
| institution | Directory Open Access Journal |
| issn | 1793-5458 1793-7205 |
| language | English |
| last_indexed | 2024-12-13T15:42:02Z |
| publishDate | 2014-11-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj.art-97e6299b43bf4bf5ba38d9e757c2e08a2022-12-21T23:39:49ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052014-11-01761450011-11450011-910.1142/S179354581450011410.1142/S1793545814500114Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopyWeijian Lou0Kai Yang1Miaoqin Zhu2Yongjiang Wu3Xuesong Liu4Ye Jin5Department of Pharmacy, Sir Run Run Shaw Hospital of School of Medicine, Zhejiang University, Hangzhou 310016, P. R. ChinaCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. ChinaDepartment of Chemistry, Zhejiang International Studies University, Hangzhou 310012, P. R. ChinaCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. ChinaCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. ChinaCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. ChinaA particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400–6200 cm-1) was identified, then the first derivative spectra smoothed using a Savitzky–Golay filter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total flavonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSO-based LS-SVM models, the determination coefficients (R2) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has definite practice significance and application value.http://www.worldscientific.com/doi/pdf/10.1142/S1793545814500114Near infrared spectroscopyextractionparticle swarm optimizationleast square support vector machines |
| spellingShingle | Weijian Lou Kai Yang Miaoqin Zhu Yongjiang Wu Xuesong Liu Ye Jin Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy Journal of Innovative Optical Health Sciences Near infrared spectroscopy extraction particle swarm optimization least square support vector machines |
| title | Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy |
| title_full | Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy |
| title_fullStr | Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy |
| title_full_unstemmed | Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy |
| title_short | Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy |
| title_sort | application of particle swarm optimization based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy |
| topic | Near infrared spectroscopy extraction particle swarm optimization least square support vector machines |
| url | http://www.worldscientific.com/doi/pdf/10.1142/S1793545814500114 |
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