Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM
In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm<sup>−1</sup> using near-infrared spectroscopy. The spectral da...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/11/11/1658 |
| _version_ | 1797493370724024320 |
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| author | Yuhan Ding Yuli Yan Jun Li Xu Chen Hui Jiang |
| author_facet | Yuhan Ding Yuli Yan Jun Li Xu Chen Hui Jiang |
| author_sort | Yuhan Ding |
| collection | DOAJ |
| description | In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm<sup>−1</sup> using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor <i>c</i> and the kernel function parameter <i>g</i> in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%. |
| first_indexed | 2024-03-10T01:19:04Z |
| format | Article |
| id | doaj.art-79a447768e434e0e940c0d18d53f6145 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T01:19:04Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj.art-79a447768e434e0e940c0d18d53f61452023-11-23T14:03:50ZengMDPI AGFoods2304-81582022-06-011111165810.3390/foods11111658Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVMYuhan Ding0Yuli Yan1Jun Li2Xu Chen3Hui Jiang4Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaKey Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaIn this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm<sup>−1</sup> using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor <i>c</i> and the kernel function parameter <i>g</i> in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.https://www.mdpi.com/2304-8158/11/11/1658Huangshan Maofeng teanear-infrared spectroscopytea quality levelclassificationCLPSO-SVM |
| spellingShingle | Yuhan Ding Yuli Yan Jun Li Xu Chen Hui Jiang Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM Foods Huangshan Maofeng tea near-infrared spectroscopy tea quality level classification CLPSO-SVM |
| title | Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM |
| title_full | Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM |
| title_fullStr | Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM |
| title_full_unstemmed | Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM |
| title_short | Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM |
| title_sort | classification of tea quality levels using near infrared spectroscopy based on clpso svm |
| topic | Huangshan Maofeng tea near-infrared spectroscopy tea quality level classification CLPSO-SVM |
| url | https://www.mdpi.com/2304-8158/11/11/1658 |
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