Rapid Determination of Active Ingredient Contents in Rhizoma Gastrodiae Using Near-Infrared Spectroscopy Combined with Artificial Rabbits Optimization-Least Square Support Vector Regression
In order to rapidly and non-destructively detect gastrodin and 4-hydroxybenzyl alcohol in Rhizoma Gastrodiae, near infrared spectral data of the dried tuber of Gastrodia elata Bl. f. glauca S. Chow were collected in the wavelength range of 900–1 700 nm. First, convolutional smoothing (SG) and standa...
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| Format: | Article |
| Language: | English |
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China Food Publishing Company
2024-02-01
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| Series: | Shipin Kexue |
| Subjects: | |
| Online Access: | https://www.spkx.net.cn/fileup/1002-6630/PDF/2024-45-4-023.pdf |
| _version_ | 1797256599118544896 |
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| author | LI Shanshan, ZHANG Fujie, LI Lixia, ZHANG Hao, DUAN Xingwei, SHI Lei, CUI Xiuming, LI Xiaoqing |
| author_facet | LI Shanshan, ZHANG Fujie, LI Lixia, ZHANG Hao, DUAN Xingwei, SHI Lei, CUI Xiuming, LI Xiaoqing |
| author_sort | LI Shanshan, ZHANG Fujie, LI Lixia, ZHANG Hao, DUAN Xingwei, SHI Lei, CUI Xiuming, LI Xiaoqing |
| collection | DOAJ |
| description | In order to rapidly and non-destructively detect gastrodin and 4-hydroxybenzyl alcohol in Rhizoma Gastrodiae, near infrared spectral data of the dried tuber of Gastrodia elata Bl. f. glauca S. Chow were collected in the wavelength range of 900–1 700 nm. First, convolutional smoothing (SG) and standard normal variable transformation (SNV) were used for spectral data preprocessing. Second, feature wavelength extraction was carried out by competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). According to the results of establishing least squares support vector machine (LSSVR) model based on feature wavelength, the best method of feature wavelength extraction was selected. In order to improve the accuracy of the model, this study introduced the artificial rabbits optimization (ARO) algorithm to optimize the regularization parameter γ and the kernel function density σ2 in LSSVR and the superiority of ARO to particle swarm optimization (PSO) and grey wolf optimizer (GWO) was evaluated. The results showed that the ARO algorithm was superior to PSO and GWO in in terms of optimization speed and ability. The best prediction models for gastrodin and 4-hydroxybenzyl alcohol were CARS-ARO-LSSVR, with prediction correlation coefficient (R2p) of 0.969 6 and 0.957 7, and root mean square error of prediction (RMSEP) of 0.014 and 0.020, respectively. Therefore, this study shows that near-infrared spectroscopy can be used for quantitative detection of active components in Rhizoma Gastrodiae, which provides a theoretical basis for the development of rapid detection devices for Rhizoma Gastrodiae. |
| first_indexed | 2024-04-24T22:24:18Z |
| format | Article |
| id | doaj.art-01dc13de7f4d4ede8fcd09fcb4273f95 |
| institution | Directory Open Access Journal |
| issn | 1002-6630 |
| language | English |
| last_indexed | 2024-04-24T22:24:18Z |
| publishDate | 2024-02-01 |
| publisher | China Food Publishing Company |
| record_format | Article |
| series | Shipin Kexue |
| spelling | doaj.art-01dc13de7f4d4ede8fcd09fcb4273f952024-03-20T02:27:16ZengChina Food Publishing CompanyShipin Kexue1002-66302024-02-0145420721310.7506/spkx1002-6630-20230831-244Rapid Determination of Active Ingredient Contents in Rhizoma Gastrodiae Using Near-Infrared Spectroscopy Combined with Artificial Rabbits Optimization-Least Square Support Vector RegressionLI Shanshan, ZHANG Fujie, LI Lixia, ZHANG Hao, DUAN Xingwei, SHI Lei, CUI Xiuming, LI Xiaoqing0(1. Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China; 2. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212000, China; 3. Chinese People’s Liberation Army Unit 69223, Aksu 842300, China)In order to rapidly and non-destructively detect gastrodin and 4-hydroxybenzyl alcohol in Rhizoma Gastrodiae, near infrared spectral data of the dried tuber of Gastrodia elata Bl. f. glauca S. Chow were collected in the wavelength range of 900–1 700 nm. First, convolutional smoothing (SG) and standard normal variable transformation (SNV) were used for spectral data preprocessing. Second, feature wavelength extraction was carried out by competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). According to the results of establishing least squares support vector machine (LSSVR) model based on feature wavelength, the best method of feature wavelength extraction was selected. In order to improve the accuracy of the model, this study introduced the artificial rabbits optimization (ARO) algorithm to optimize the regularization parameter γ and the kernel function density σ2 in LSSVR and the superiority of ARO to particle swarm optimization (PSO) and grey wolf optimizer (GWO) was evaluated. The results showed that the ARO algorithm was superior to PSO and GWO in in terms of optimization speed and ability. The best prediction models for gastrodin and 4-hydroxybenzyl alcohol were CARS-ARO-LSSVR, with prediction correlation coefficient (R2p) of 0.969 6 and 0.957 7, and root mean square error of prediction (RMSEP) of 0.014 and 0.020, respectively. Therefore, this study shows that near-infrared spectroscopy can be used for quantitative detection of active components in Rhizoma Gastrodiae, which provides a theoretical basis for the development of rapid detection devices for Rhizoma Gastrodiae.https://www.spkx.net.cn/fileup/1002-6630/PDF/2024-45-4-023.pdfnear infrared spectroscopy; gastrodia; least squares support vector regression; artificial rabbits optimization algorithm |
| spellingShingle | LI Shanshan, ZHANG Fujie, LI Lixia, ZHANG Hao, DUAN Xingwei, SHI Lei, CUI Xiuming, LI Xiaoqing Rapid Determination of Active Ingredient Contents in Rhizoma Gastrodiae Using Near-Infrared Spectroscopy Combined with Artificial Rabbits Optimization-Least Square Support Vector Regression Shipin Kexue near infrared spectroscopy; gastrodia; least squares support vector regression; artificial rabbits optimization algorithm |
| title | Rapid Determination of Active Ingredient Contents in Rhizoma Gastrodiae Using Near-Infrared Spectroscopy Combined with Artificial Rabbits Optimization-Least Square Support Vector Regression |
| title_full | Rapid Determination of Active Ingredient Contents in Rhizoma Gastrodiae Using Near-Infrared Spectroscopy Combined with Artificial Rabbits Optimization-Least Square Support Vector Regression |
| title_fullStr | Rapid Determination of Active Ingredient Contents in Rhizoma Gastrodiae Using Near-Infrared Spectroscopy Combined with Artificial Rabbits Optimization-Least Square Support Vector Regression |
| title_full_unstemmed | Rapid Determination of Active Ingredient Contents in Rhizoma Gastrodiae Using Near-Infrared Spectroscopy Combined with Artificial Rabbits Optimization-Least Square Support Vector Regression |
| title_short | Rapid Determination of Active Ingredient Contents in Rhizoma Gastrodiae Using Near-Infrared Spectroscopy Combined with Artificial Rabbits Optimization-Least Square Support Vector Regression |
| title_sort | rapid determination of active ingredient contents in rhizoma gastrodiae using near infrared spectroscopy combined with artificial rabbits optimization least square support vector regression |
| topic | near infrared spectroscopy; gastrodia; least squares support vector regression; artificial rabbits optimization algorithm |
| url | https://www.spkx.net.cn/fileup/1002-6630/PDF/2024-45-4-023.pdf |
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