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Authentication of the Geographical Origin of Shandong Scallop Chlamys farreri Using Mineral Elements Combined with Multivariate Data Analysis and Machine Learning Algorithm

文献类型： 外文期刊

作者： Kang, Xuming 1 ; Zhao, Yanfang 1 ; Peng, Jixing 1 ; Ding, Haiyan 1 ; Tan, Zhijun 1 ; Han, Cui 3 ; Sheng, Xiaofeng 1 ; Liu, Xiyin 4 ; Zhai, Yuxiu 1 ;

作者机构： 1.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Key Lab Testing & Evaluat Aquat Prod Safety & Qua, Minist Agr & Rural Affairs, Qingdao 266071, Peoples R China

2.Pilot Natl Lab Marine Sci & Technol Qingdao, Qingdao 266071, Peoples R China

3.Nanjing Normal Univ, Sch Marine Sci & Engn, Nanjing 210023, Peoples R China

4.Yantai Marine Econ Res Inst, Yantai 264003, Peoples R China

关键词： Scallop; Geographical origin; Mineral elements; Multivariate analysis; Machine learning

期刊名称：FOOD ANALYTICAL METHODS （ 影响因子：3.498； 五年影响因子：3.226 ）

ISSN： 1936-9751

年卷期：

页码：

收录情况： SCI

摘要： Geographical traceability of seafood is a global concern for both consumers and importers. It is urgent to develop a scientific approach for identifying the geographic origin of seafood to combat labeling fraud. This study verified 14 mineral elements as a tracer for identify the geographic origin of scallops in Shandong Province of China. Multivariate data analysis and machine learning algorithm including linear discriminate analysis (LDA), k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) were used to evaluate their performance in terms of classification or predictive ability. Thirteen elements in scallop samples with different regions showed significant differences (p < 0.05), which proved that the elemental composition was an effective tool for distinguishing the origins of scallops. The overall discrimination accuracy and predictive accuracy obtained from the LDA, KNN, RF, and SVM analysis was over 98.96% and 97.78%, respectively. Among these models, LDA model was the most recommended for the origin identification of scallops based on its high discriminant accuracy rate (100%), cross-validated accuracy rate (100%), and predictive accuracy rate (100%). Present results indicated the feasibility of element fingerprints combined with multivariate data analysis and machine learning algorithm in authenticating the geographical origin of scallops in China.

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