An explainable machine learning model for identifying geographical origins of sea cucumber Apostichopus japonicus based on multi-element profile
文献类型： 外文期刊
作者： Sun, Yong 1 ; Zhao, Yanfang 2 ; Wu, Jifa 3 ; Liu, Nan 2 ; Kang, Xuming 2 ; Wang, Shanshan 2 ; Zhou, Deqing 2 ;
作者机构： 1.Shanghai Ocean Univ, Coll Food Sci & Technol, Shanghai 201306, Peoples R China
2.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Qingdao 266071, Peoples R China
3.Qingdao Yihaifeng Aquat Prod Co Ltd, Qingdao 266414, Peoples R China
关键词： Geographical origins; Sea cucumber; Multi-element profile; Explainable machine learning; SHAP; XGBoost
期刊名称：FOOD CONTROL （ 2021影响因子：6.652； 五年影响因子：6.498 ）
ISSN： 0956-7135
年卷期： 2022 年 134 卷
收录情况： SCI
摘要： The geographical origin of sea cucumber Apostichopus japonicas plays an important role in determining its market value. This study investigated the feasibility of using multi-element profile combined with explainable machine learning to trace the origin of sea cucumber in China. Multi-element profile (23 elements) of 167 sea cucumber samples was determined with ICP-OES and ICP-MS, and used for construction and evaluation of 4 ensemble learning models. Extreme gradient boosting (XGBoost) model achieved superior performance with an overall accuracy, precision, recall, F1 score and AUC as 0.95, 0.93, 0.91 and 1, respectively. The Shapley Additive Explanations (SHAP) algorithm was subsequently applied to interpret the XGBoost model output for desirable geographical information. Se was identified as the most important elemental marker for discriminating sea cu-cumber origins. Therefore, with clarified scientific support, multi-element profile combined with machine learning model could serve as a powerful tool for identifying the provenance of sea cucumber.
- 相关文献
作者其他论文 更多>>
-
Quality changes of whitespotted conger (Conger myriaster) based physicochemical changes and label-free proteomics analysis during frozen storage
作者：Chen, Hui;Wang, Shanshan;Liu, Nan;Sun, Yong;Zhou, Deqing;Xi, Rui;Sun, Guohui;Ge, Yinggang;Yang, Ting;Sun, Guohui
关键词：Whitespotted conger; Frozen storage; Protein degradation; Quality deterioration; Proteomics analysis
-
Computer-Aided Rational Design Strategy to Improve the Thermal Stability of Alginate Lyase AlyMc
作者：Cui, Yongyan;Cui, Yongyan;Yang, Min;Liu, Nan;Wang, Shanshan;Sun, Yong;Sun, Guohui;Zhou, Deqing;Yang, Min;Mou, Haijin
关键词：alginate lyase; alginate oligosaccharides; rational design; thermal stability; moleculardynamicssimulation
-
Cultivation mode of Saccharina japonica impacts planktonic and epiphytic microbiomes
作者：Yan, Yongwei;Li, Jie;Yan, Yongwei;Rong, Xiaojun;Wang, Shanshan;Liu, Kuimei;Pang, Shaojun;Chang, Lirong;Li, Jie
关键词：Cultivation mode; Microbial community; Saccharina japonica; Seaweed
-
Explainable machine learning for predicting the geographical origin of Chinese Oysters via mineral elements analysis
作者：Kang, Xuming;Zhao, Yanfang;Yao, Lin;Tan, Zhijun;Tan, Zhijun;Tan, Zhijun
关键词：Oyster; Elemental profile; Explainable machine learning; SHAP; Geographical origin
-
Elemental profiling of red seaweed Neopyropia yezoensis used in fast authenticating the geographical origin and food safety assessment
作者：Zhao, Yanfang;Kang, Xuming;Guo, Yingying;Wang, Lianzhu;Sheng, Xiaofeng;Tan, Zhijun;Wu, Jifa;Tan, Zhijun;Tan, Zhijun
关键词：Food analysis; Food composition; Heavy metals; Traceability; Red seaweed
-
Attenuated total reflectance-flourier transformed infrared spectroscopy (ATR-FTIR) coupled with deep learning: A rapid method for geographical origin identification of sea cucumber Apostichopus japonicus
作者：Sun, Yong;Liu, Nan;Zhao, Ling;Liu, Qi;Wang, Shanshan;Sun, Guohui;Zhao, Yanfang;Zhou, Deqing;Cao, Rong
关键词：Apostichopus japonicas; ATR-FTIR spectroscopy; Deep learning; Geographical origin; Rapid discrimination
-
An explainable machine learning for geographical origin traceability of mussels Mytilus edulis based on stable isotope ratio and compositions of C, N, O and H
作者：Kang, Xuming;Zhao, Yanfang;Tan, Zhijun;Tan, Zhijun;Tan, Zhijun
关键词：Mussel; Stable isotopes; Elemental composition; Explainable machine learning; SHAP; Traceability
