Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy
The intelligent perception of moisture content (MC) for tea leaves during the black tea withering process is an unsolved task because of the acquisition of limited sample characteristic information. In this study, both the external and internal features of withering samples were simultaneously acqui...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/9/1442 |
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| author | Ting An Yongwen Jiang Hanting Zou Xuan Xuan Jian Zhang Haibo Yuan |
| author_facet | Ting An Yongwen Jiang Hanting Zou Xuan Xuan Jian Zhang Haibo Yuan |
| author_sort | Ting An |
| collection | DOAJ |
| description | The intelligent perception of moisture content (MC) for tea leaves during the black tea withering process is an unsolved task because of the acquisition of limited sample characteristic information. In this study, both the external and internal features of withering samples were simultaneously acquired based on near-infrared spectroscopy (NIRS) and machine vision (MV) technology. Different data fusion strategies, including low-, middle- and high-level strategies, were employed to integrate two types of heterogeneous information. Subsequently, the different fused features were combined with a support vector regression (SVR) algorithm to establish the moisture perception models of withering leaves. The middle-level-variable iterative space shrinkage approach (VISSA) displayed the best performance with 5.7705 for the relative percent deviation (RPD). Therefore, the proposed multi-information fusion strategy could achieve an intelligent perception of tea leaves in the black tea withering process. The integration of NIRS and MV technology overcomes the limitations of single-technology approaches in black tea withering assessment, providing a robust methodology for precision processing and targeted quality control of black tea. |
| format | Article |
| id | doaj-art-21dc49dedb734d5eba345071bfff77f5 |
| institution | OA Journals |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-21dc49dedb734d5eba345071bfff77f52025-08-20T01:50:45ZengMDPI AGFoods2304-81582025-04-01149144210.3390/foods14091442Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion StrategyTing An0Yongwen Jiang1Hanting Zou2Xuan Xuan3Jian Zhang4Haibo Yuan5National Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, ChinaNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, ChinaNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, ChinaNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, ChinaSchool of Intelligent Manufacturing, Huzhou College, Huzhou 313000, ChinaNational Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, ChinaThe intelligent perception of moisture content (MC) for tea leaves during the black tea withering process is an unsolved task because of the acquisition of limited sample characteristic information. In this study, both the external and internal features of withering samples were simultaneously acquired based on near-infrared spectroscopy (NIRS) and machine vision (MV) technology. Different data fusion strategies, including low-, middle- and high-level strategies, were employed to integrate two types of heterogeneous information. Subsequently, the different fused features were combined with a support vector regression (SVR) algorithm to establish the moisture perception models of withering leaves. The middle-level-variable iterative space shrinkage approach (VISSA) displayed the best performance with 5.7705 for the relative percent deviation (RPD). Therefore, the proposed multi-information fusion strategy could achieve an intelligent perception of tea leaves in the black tea withering process. The integration of NIRS and MV technology overcomes the limitations of single-technology approaches in black tea withering assessment, providing a robust methodology for precision processing and targeted quality control of black tea.https://www.mdpi.com/2304-8158/14/9/1442MVNIRSdata fusion strategymoisture contentblack tea withering |
| spellingShingle | Ting An Yongwen Jiang Hanting Zou Xuan Xuan Jian Zhang Haibo Yuan Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy Foods MV NIRS data fusion strategy moisture content black tea withering |
| title | Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy |
| title_full | Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy |
| title_fullStr | Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy |
| title_full_unstemmed | Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy |
| title_short | Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy |
| title_sort | evaluation of withering quality of black tea based on multi information fusion strategy |
| topic | MV NIRS data fusion strategy moisture content black tea withering |
| url | https://www.mdpi.com/2304-8158/14/9/1442 |
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