Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine Algorithm
To investigate the feasibility of rapid identification and quality evaluation of Chinese medicinal centipedes using NIR spectroscopy, the qualitative and quantitative analysis models were explored. A PCA-SVC model was optimized to differentiate five species of the genus Scolopendra. When the model w...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2019/9636823 |
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| _version_ | 1832549572111499264 |
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| author | Sihe Kang Haiying Deng Long Chen Xiaoxuan Zeng Yimei Liu Keli Chen |
| author_facet | Sihe Kang Haiying Deng Long Chen Xiaoxuan Zeng Yimei Liu Keli Chen |
| author_sort | Sihe Kang |
| collection | DOAJ |
| description | To investigate the feasibility of rapid identification and quality evaluation of Chinese medicinal centipedes using NIR spectroscopy, the qualitative and quantitative analysis models were explored. A PCA-SVC model was optimized to differentiate five species of the genus Scolopendra. When the model was validated with the calibration and prediction sets, the prediction accuracy was 100% and 81.82%, respectively; it can meet the requirement for rapid and preliminary identification. Based on nitrogen content detected by the chemical method, and the dimensionality of spectral data reduced with PLS, the quantitative analysis models were successfully built by PLSR and SVR algorithms. After spectra were pretreated and parameters were optimized, the performance, rationality, and prediction ability of the models were validated and evaluated with RMSECV, RMSEP, RMSEE, R2, and RPD. Compared with the features and advantages of these two models, the PLS-SVR model had better performance and stronger prediction capacity, and it was finally regarded as the optimal quantitative analysis model to predict nitrogen content. The relative deviation between the predictive value and the reference was 2.69%, and the average recovery was 99.02%, which indicated it has potential for rapid prediction and evaluation of the quality of medicinal centipedes. This research suggested that NIR spectroscopy can be used as a rapid detection method to identify species and evaluate the quality of medicinal centipedes in China. |
| format | Article |
| id | doaj-art-4316fd2de21249d3a822d7c8dfb1c28b |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-4316fd2de21249d3a822d7c8dfb1c28b2025-02-03T06:11:01ZengWileyJournal of Spectroscopy2314-49202314-49392019-01-01201910.1155/2019/96368239636823Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine AlgorithmSihe Kang0Haiying Deng1Long Chen2Xiaoxuan Zeng3Yimei Liu4Keli Chen5Key Laboratory of Ministry of Education on Traditional Chinese Medicine Resource and Compound Prescription, Hubei University of Chinese Medicine, Wuhan 430065, ChinaMedical College of Wuhan University of Science and Technology, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan 430065, ChinaKey Laboratory of Ministry of Education on Traditional Chinese Medicine Resource and Compound Prescription, Hubei University of Chinese Medicine, Wuhan 430065, ChinaKey Laboratory of Ministry of Education on Traditional Chinese Medicine Resource and Compound Prescription, Hubei University of Chinese Medicine, Wuhan 430065, ChinaKey Laboratory of Ministry of Education on Traditional Chinese Medicine Resource and Compound Prescription, Hubei University of Chinese Medicine, Wuhan 430065, ChinaKey Laboratory of Ministry of Education on Traditional Chinese Medicine Resource and Compound Prescription, Hubei University of Chinese Medicine, Wuhan 430065, ChinaTo investigate the feasibility of rapid identification and quality evaluation of Chinese medicinal centipedes using NIR spectroscopy, the qualitative and quantitative analysis models were explored. A PCA-SVC model was optimized to differentiate five species of the genus Scolopendra. When the model was validated with the calibration and prediction sets, the prediction accuracy was 100% and 81.82%, respectively; it can meet the requirement for rapid and preliminary identification. Based on nitrogen content detected by the chemical method, and the dimensionality of spectral data reduced with PLS, the quantitative analysis models were successfully built by PLSR and SVR algorithms. After spectra were pretreated and parameters were optimized, the performance, rationality, and prediction ability of the models were validated and evaluated with RMSECV, RMSEP, RMSEE, R2, and RPD. Compared with the features and advantages of these two models, the PLS-SVR model had better performance and stronger prediction capacity, and it was finally regarded as the optimal quantitative analysis model to predict nitrogen content. The relative deviation between the predictive value and the reference was 2.69%, and the average recovery was 99.02%, which indicated it has potential for rapid prediction and evaluation of the quality of medicinal centipedes. This research suggested that NIR spectroscopy can be used as a rapid detection method to identify species and evaluate the quality of medicinal centipedes in China.http://dx.doi.org/10.1155/2019/9636823 |
| spellingShingle | Sihe Kang Haiying Deng Long Chen Xiaoxuan Zeng Yimei Liu Keli Chen Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine Algorithm Journal of Spectroscopy |
| title | Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine Algorithm |
| title_full | Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine Algorithm |
| title_fullStr | Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine Algorithm |
| title_full_unstemmed | Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine Algorithm |
| title_short | Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine Algorithm |
| title_sort | rapid identification and quality evaluation of medicinal centipedes in china using near infrared spectroscopy integrated with support vector machine algorithm |
| url | http://dx.doi.org/10.1155/2019/9636823 |
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