Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics
The bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactiv...
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2024-12-01
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| Series: | Foods |
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| author | Yunpeng Wei Huiqiang Hu Minghua Yuan Huaxing Xu Xiaobo Mao Yuping Zhao Luqi Huang |
| author_facet | Yunpeng Wei Huiqiang Hu Minghua Yuan Huaxing Xu Xiaobo Mao Yuping Zhao Luqi Huang |
| author_sort | Yunpeng Wei |
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| description | The bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactive components in chrysanthemums, including the total flavonoids (TFs) and chlorogenic acids (TCAs). To determine the informative wavelengths of hyperspectral images, we introduced a variable similarity regularization term into particle swarm optimization (denoted as VSPSO), which can focus on improving the combinatorial performance of key wavelengths and filtering out the features with higher collinearity simultaneously. Moreover, considering the underlying relevance of the phytochemical content and the exterior morphology characteristics, the spatial image features were also extracted. Finally, an ensemble learning model, LightGBM, was established to estimate the TF and TCA contents using the fused features. Experimental results indicated that the proposed VSPSO achieved a superior accuracy, with R<sup>2</sup> scores of 0.9280 and 0.8882 for TF and TCA prediction. Furthermore, after the involvement of spatial image information, the fused spectral–spatial features achieved the optimal model accuracy on LightGBM. The R<sup>2</sup> scores reached 0.9541 and 0.9137, increasing by 0.0308–0.1404 and 0.0181–0.1066 in comparison with classical wavelength-related methods and models. Overall, our research provides a novel method for estimating the bioactive components in chrysanthemum tea accurately and efficiently. These discoveries revealed the potential effectiveness for constructing feature fusion in HSI-based practical applications, such as nutritive value evaluation and heavy metal pollution detection, which will also facilitate the development of quality detection in the food industry. |
| format | Article |
| id | doaj-art-7085e7e23bee4bc6ac2b002b7caacff6 |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj-art-7085e7e23bee4bc6ac2b002b7caacff62024-12-27T14:26:39ZengMDPI AGFoods2304-81582024-12-011324414510.3390/foods13244145Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and ChemometricsYunpeng Wei0Huiqiang Hu1Minghua Yuan2Huaxing Xu3Xiaobo Mao4Yuping Zhao5Luqi Huang6School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaDepartment of Pharmacy, Zhengzhou Shuqing Medical College, Zhengzhou 450064, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaChina Academy of Chinese Medical Sciences, Beijing 100700, ChinaSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaThe bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactive components in chrysanthemums, including the total flavonoids (TFs) and chlorogenic acids (TCAs). To determine the informative wavelengths of hyperspectral images, we introduced a variable similarity regularization term into particle swarm optimization (denoted as VSPSO), which can focus on improving the combinatorial performance of key wavelengths and filtering out the features with higher collinearity simultaneously. Moreover, considering the underlying relevance of the phytochemical content and the exterior morphology characteristics, the spatial image features were also extracted. Finally, an ensemble learning model, LightGBM, was established to estimate the TF and TCA contents using the fused features. Experimental results indicated that the proposed VSPSO achieved a superior accuracy, with R<sup>2</sup> scores of 0.9280 and 0.8882 for TF and TCA prediction. Furthermore, after the involvement of spatial image information, the fused spectral–spatial features achieved the optimal model accuracy on LightGBM. The R<sup>2</sup> scores reached 0.9541 and 0.9137, increasing by 0.0308–0.1404 and 0.0181–0.1066 in comparison with classical wavelength-related methods and models. Overall, our research provides a novel method for estimating the bioactive components in chrysanthemum tea accurately and efficiently. These discoveries revealed the potential effectiveness for constructing feature fusion in HSI-based practical applications, such as nutritive value evaluation and heavy metal pollution detection, which will also facilitate the development of quality detection in the food industry.https://www.mdpi.com/2304-8158/13/24/4145chrysanthemum teahyperspectral imagingwavelength selectionparticle swarm optimizationensemble learning |
| spellingShingle | Yunpeng Wei Huiqiang Hu Minghua Yuan Huaxing Xu Xiaobo Mao Yuping Zhao Luqi Huang Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics Foods chrysanthemum tea hyperspectral imaging wavelength selection particle swarm optimization ensemble learning |
| title | Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics |
| title_full | Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics |
| title_fullStr | Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics |
| title_full_unstemmed | Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics |
| title_short | Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics |
| title_sort | determination of bioactive components in chrysanthemum tea gongju using hyperspectral imaging technique and chemometrics |
| topic | chrysanthemum tea hyperspectral imaging wavelength selection particle swarm optimization ensemble learning |
| url | https://www.mdpi.com/2304-8158/13/24/4145 |
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