Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine Learning
The Chinese mitten crab (<i>Eriocheir sinensis</i>) industry is currently facing the challenges of origin fraud, as well as a lack of precision and interpretability of existing traceability methods. Here, we propose a high-precision origin traceability method based on a combination of st...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/14/2458 |
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| author | Danhe Wang Chunxia Yao Yangyang Lu Di Huang Yameng Li Xugan Wu Weiguo Song Qinxiong Rao |
| author_facet | Danhe Wang Chunxia Yao Yangyang Lu Di Huang Yameng Li Xugan Wu Weiguo Song Qinxiong Rao |
| author_sort | Danhe Wang |
| collection | DOAJ |
| description | The Chinese mitten crab (<i>Eriocheir sinensis</i>) industry is currently facing the challenges of origin fraud, as well as a lack of precision and interpretability of existing traceability methods. Here, we propose a high-precision origin traceability method based on a combination of stable isotope analysis and interpretable machine learning. We sampled Chinese mitten crabs from six origins representing diverse aquatic environments and farming practices, and analyzed their <i>δ</i><sup>13</sup>C, <i>δ</i><sup>15</sup>N, <i>δ</i><sup>2</sup>H, and <i>δ</i><sup>18</sup>O stable isotope compositions in different sexes and tissues (hepatopancreas, muscle, and gonad). By comparing the classification performance of Random Forest, XGBoost, and Logistic Regression models, we found that the Random Forest model outperformed the others, achieving high accuracy (91.3%) in distinguishing samples from different origins. Interpretation of the optimal Random Forest model, using SHAP (SHapley Additive exPlanations) analysis, identified <i>δ</i><sup>2</sup>H in male muscle, <i>δ</i><sup>15</sup>N in female hepatopancreas, and <i>δ</i><sup>13</sup>C in female hepatopancreas as the most influential features for discriminating geographic origin. This analysis highlighted the crucial role of environmental factors, such as water source, diet, and trophic level, in origin discrimination and demonstrated that isotopic characteristics of different tissues provide unique discriminatory information. This study offers a novel paradigm for stable isotope traceability based on explainable machine learning, significantly enhancing the identification capability and reliability of Chinese mitten crab origin traceability, and holds significant implications for food safety assurance. |
| format | Article |
| id | doaj-art-59cea9f1bf3f43769da79e23dad9a4d4 |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-59cea9f1bf3f43769da79e23dad9a4d42025-08-20T03:58:31ZengMDPI AGFoods2304-81582025-07-011414245810.3390/foods14142458Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine LearningDanhe Wang0Chunxia Yao1Yangyang Lu2Di Huang3Yameng Li4Xugan Wu5Weiguo Song6Qinxiong Rao7The Institute of Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaThe Institute of Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaThe Institute of Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaThe Institute of Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaThe Institute of Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaResearch Centre on Fish Nutrition and Environmental Ecology of Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai 201306, ChinaThe Institute of Agro-Food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaShanghai Co-Elite Agri-Food Testing Technical Service Co., Ltd., Shanghai 201403, ChinaThe Chinese mitten crab (<i>Eriocheir sinensis</i>) industry is currently facing the challenges of origin fraud, as well as a lack of precision and interpretability of existing traceability methods. Here, we propose a high-precision origin traceability method based on a combination of stable isotope analysis and interpretable machine learning. We sampled Chinese mitten crabs from six origins representing diverse aquatic environments and farming practices, and analyzed their <i>δ</i><sup>13</sup>C, <i>δ</i><sup>15</sup>N, <i>δ</i><sup>2</sup>H, and <i>δ</i><sup>18</sup>O stable isotope compositions in different sexes and tissues (hepatopancreas, muscle, and gonad). By comparing the classification performance of Random Forest, XGBoost, and Logistic Regression models, we found that the Random Forest model outperformed the others, achieving high accuracy (91.3%) in distinguishing samples from different origins. Interpretation of the optimal Random Forest model, using SHAP (SHapley Additive exPlanations) analysis, identified <i>δ</i><sup>2</sup>H in male muscle, <i>δ</i><sup>15</sup>N in female hepatopancreas, and <i>δ</i><sup>13</sup>C in female hepatopancreas as the most influential features for discriminating geographic origin. This analysis highlighted the crucial role of environmental factors, such as water source, diet, and trophic level, in origin discrimination and demonstrated that isotopic characteristics of different tissues provide unique discriminatory information. This study offers a novel paradigm for stable isotope traceability based on explainable machine learning, significantly enhancing the identification capability and reliability of Chinese mitten crab origin traceability, and holds significant implications for food safety assurance.https://www.mdpi.com/2304-8158/14/14/2458Chinese mitten craborigin traceabilitystable isotopesexplainable machine learningtissue specificity |
| spellingShingle | Danhe Wang Chunxia Yao Yangyang Lu Di Huang Yameng Li Xugan Wu Weiguo Song Qinxiong Rao Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine Learning Foods Chinese mitten crab origin traceability stable isotopes explainable machine learning tissue specificity |
| title | Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine Learning |
| title_full | Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine Learning |
| title_fullStr | Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine Learning |
| title_full_unstemmed | Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine Learning |
| title_short | Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine Learning |
| title_sort | origin traceability of chinese mitten crab i eriocheir sinensis i using multi stable isotopes and explainable machine learning |
| topic | Chinese mitten crab origin traceability stable isotopes explainable machine learning tissue specificity |
| url | https://www.mdpi.com/2304-8158/14/14/2458 |
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