Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis
In addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp’s origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field remains scarce...
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MDPI AG
2025-04-01
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| author | Xuming Kang Zhijun Tan Yanfang Zhao Lin Yao Xiaofeng Sheng Yingying Guo |
| author_facet | Xuming Kang Zhijun Tan Yanfang Zhao Lin Yao Xiaofeng Sheng Yingying Guo |
| author_sort | Xuming Kang |
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| description | In addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp’s origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field remains scarce due to its black-box nature. To address this gap, we attempted to identify the origin of kelp by analyzing its VOCs in conjunction with explainable deep learning. In this work, we identified 115 distinct VOCs in kelp samples using gas chromatography coupled with ion mobility spectroscopy (GC-IMS), of which 68 categories were discernible. Consequently, we developed a comprehensible one-dimensional convolutional neural network (1D-CNN) model that incorporated 107 VOCs exhibiting significant regional disparities (<i>p</i> < 0.05). The model successfully discerns the origin of kelp, achieving perfect metrics across accuracy (100%), precision (100%), recall (100%), F1 score (100%), and AUC (1.0). SHapley Additive exPlanations (SHAP) analysis highlighted the impact of features such as 1-Octen-3-ol-M, (+)-limonene, allyl sulfide-D, 1-hydroxy-2-propanone-D, and (<i>E</i>)-2-hexen-1-al-M on the model output. This research provides deeper insights into how critical product features correlate with specific geographic information, which in turn boosts consumer trust and promotes practical utilization in actual settings. |
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| issn | 2304-8158 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-d3ad60b0ec134228934d8849dedd70d02025-08-20T02:15:58ZengMDPI AGFoods2304-81582025-04-01147126910.3390/foods14071269Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound AnalysisXuming Kang0Zhijun Tan1Yanfang Zhao2Lin Yao3Xiaofeng Sheng4Yingying Guo5Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, ChinaKey Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, ChinaKey Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, ChinaKey Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, ChinaKey Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, ChinaKey Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, ChinaIn addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp’s origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field remains scarce due to its black-box nature. To address this gap, we attempted to identify the origin of kelp by analyzing its VOCs in conjunction with explainable deep learning. In this work, we identified 115 distinct VOCs in kelp samples using gas chromatography coupled with ion mobility spectroscopy (GC-IMS), of which 68 categories were discernible. Consequently, we developed a comprehensible one-dimensional convolutional neural network (1D-CNN) model that incorporated 107 VOCs exhibiting significant regional disparities (<i>p</i> < 0.05). The model successfully discerns the origin of kelp, achieving perfect metrics across accuracy (100%), precision (100%), recall (100%), F1 score (100%), and AUC (1.0). SHapley Additive exPlanations (SHAP) analysis highlighted the impact of features such as 1-Octen-3-ol-M, (+)-limonene, allyl sulfide-D, 1-hydroxy-2-propanone-D, and (<i>E</i>)-2-hexen-1-al-M on the model output. This research provides deeper insights into how critical product features correlate with specific geographic information, which in turn boosts consumer trust and promotes practical utilization in actual settings.https://www.mdpi.com/2304-8158/14/7/1269kelpvolatile organic compoundsexplainable deep learningSHAPgeographical origin |
| spellingShingle | Xuming Kang Zhijun Tan Yanfang Zhao Lin Yao Xiaofeng Sheng Yingying Guo Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis Foods kelp volatile organic compounds explainable deep learning SHAP geographical origin |
| title | Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis |
| title_full | Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis |
| title_fullStr | Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis |
| title_full_unstemmed | Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis |
| title_short | Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis |
| title_sort | explainable deep learning to predict kelp geographical origin from volatile organic compound analysis |
| topic | kelp volatile organic compounds explainable deep learning SHAP geographical origin |
| url | https://www.mdpi.com/2304-8158/14/7/1269 |
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