Interpretable AI-driven multidimensional chemical fingerprints for geographical authentication of Euryales Semen
Abstract Euryales Semen (ES, Euryale ferox Salisb.) is a valuable aquatic food in Asia. Its quality and price depend on its geographical origins. To ensure the authenticity of ES, a tracing strategy using stable isotopes, elements, and starch composition with interpretable algorithms was successfull...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Science of Food |
| Online Access: | https://doi.org/10.1038/s41538-025-00510-y |
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| author | Daixin Yu Cheng Qu Jing Nie Pei Wen Yuyang Zhao Caiyan Dai Hui Yan Yuwei Yuan Qinan Wu |
| author_facet | Daixin Yu Cheng Qu Jing Nie Pei Wen Yuyang Zhao Caiyan Dai Hui Yan Yuwei Yuan Qinan Wu |
| author_sort | Daixin Yu |
| collection | DOAJ |
| description | Abstract Euryales Semen (ES, Euryale ferox Salisb.) is a valuable aquatic food in Asia. Its quality and price depend on its geographical origins. To ensure the authenticity of ES, a tracing strategy using stable isotopes, elements, and starch composition with interpretable algorithms was successfully developed. Results indicated that ES from different regions exhibited different chemical fingerprinting profiles. Tree-based intelligent algorithms were introduced for classification, and light gradient boosting machine (LightGBM) achieved the highest accuracy of 97.67%. The SHapley Additive exPlanation (SHAP) interpreted the LightGBM output for feature impact. Notably, the top 10 significant variables, encompassing Na, V, Ba, Sb, Cu, Ti, Mn, %N, amylose, and ratio of amylose to amylopectin (SHAP value >1.0), were selected as the key factors. Moreover, environmental factors were found to be significantly related to these key variables (p < 0.05). Overall, this study offers an effective strategy for the geographical origin traceability of ES or other aquatic crops. |
| format | Article |
| id | doaj-art-e99a3fbfad23477c881ca8a452da8193 |
| institution | Kabale University |
| issn | 2396-8370 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Science of Food |
| spelling | doaj-art-e99a3fbfad23477c881ca8a452da81932025-08-20T04:02:45ZengNature Portfolionpj Science of Food2396-83702025-07-019111310.1038/s41538-025-00510-yInterpretable AI-driven multidimensional chemical fingerprints for geographical authentication of Euryales SemenDaixin Yu0Cheng Qu1Jing Nie2Pei Wen3Yuyang Zhao4Caiyan Dai5Hui Yan6Yuwei Yuan7Qinan Wu8Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese MedicineJiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese MedicineInstitute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of ChinaJiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese MedicineState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical SciencesSchool of Artificial Intelligence and Information Technology, Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing University of Chinese MedicineJiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese MedicineInstitute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of ChinaJiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese MedicineAbstract Euryales Semen (ES, Euryale ferox Salisb.) is a valuable aquatic food in Asia. Its quality and price depend on its geographical origins. To ensure the authenticity of ES, a tracing strategy using stable isotopes, elements, and starch composition with interpretable algorithms was successfully developed. Results indicated that ES from different regions exhibited different chemical fingerprinting profiles. Tree-based intelligent algorithms were introduced for classification, and light gradient boosting machine (LightGBM) achieved the highest accuracy of 97.67%. The SHapley Additive exPlanation (SHAP) interpreted the LightGBM output for feature impact. Notably, the top 10 significant variables, encompassing Na, V, Ba, Sb, Cu, Ti, Mn, %N, amylose, and ratio of amylose to amylopectin (SHAP value >1.0), were selected as the key factors. Moreover, environmental factors were found to be significantly related to these key variables (p < 0.05). Overall, this study offers an effective strategy for the geographical origin traceability of ES or other aquatic crops.https://doi.org/10.1038/s41538-025-00510-y |
| spellingShingle | Daixin Yu Cheng Qu Jing Nie Pei Wen Yuyang Zhao Caiyan Dai Hui Yan Yuwei Yuan Qinan Wu Interpretable AI-driven multidimensional chemical fingerprints for geographical authentication of Euryales Semen npj Science of Food |
| title | Interpretable AI-driven multidimensional chemical fingerprints for geographical authentication of Euryales Semen |
| title_full | Interpretable AI-driven multidimensional chemical fingerprints for geographical authentication of Euryales Semen |
| title_fullStr | Interpretable AI-driven multidimensional chemical fingerprints for geographical authentication of Euryales Semen |
| title_full_unstemmed | Interpretable AI-driven multidimensional chemical fingerprints for geographical authentication of Euryales Semen |
| title_short | Interpretable AI-driven multidimensional chemical fingerprints for geographical authentication of Euryales Semen |
| title_sort | interpretable ai driven multidimensional chemical fingerprints for geographical authentication of euryales semen |
| url | https://doi.org/10.1038/s41538-025-00510-y |
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