Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis
文献类型： 外文期刊
作者： Kang, Xuming 1 ; Tan, Zhijun 1 ; Zhao, Yanfang 1 ; Yao, Lin 1 ; Sheng, Xiaofeng 1 ; Guo, Yingying 1 ;
作者机构： 1.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Key Lab Testing & Evaluat Aquat Prod Safety & Qual, Minist Agr & Rural Affairs, Qingdao 266071, Peoples R China
2.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, State Key Lab Mariculture Biobreeding & Sustainabl, Qingdao 266071, Peoples R China
关键词： kelp; volatile organic compounds; explainable deep learning; SHAP; geographical origin
期刊名称：FOODS （ 影响因子：5.1； 五年影响因子：5.6 ）
ISSN：
年卷期： 2025 年 14 卷 7 期
页码：
收录情况： SCI
摘要： 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 (p < 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 (E)-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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