Authentication of the Geographical Origin of Shandong Scallop Chlamys farreri Using Mineral Elements Combined with Multivariate Data Analysis and Machine Learning Algorithm
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 o...
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| Veröffentlicht in: | Food analytical methods 2022-11, Vol.15 (11), p.2984-2993 |
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| creator | Kang, Xuming Zhao, Yanfang Peng, Jixing Ding, Haiyan Tan, Zhijun Han, Cui Sheng, Xiaofeng Liu, Xiyin Zhai, Yuxiu |
| description | 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
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| doi_str_mv | 10.1007/s12161-022-02346-8 |
| format | Article |
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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.</description><identifier>ISSN: 1936-9751</identifier><identifier>EISSN: 1936-976X</identifier><identifier>DOI: 10.1007/s12161-022-02346-8</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accuracy ; Algorithms ; Analytical Chemistry ; Authentication ; Chemical composition ; Chemistry ; Chemistry and Materials Science ; Chemistry/Food Science ; Data analysis ; Discriminant analysis ; Food Science ; Fraud ; Geography ; Learning algorithms ; Machine learning ; Microbiology ; Multivariate analysis ; Scallops ; Seafood ; Support vector machines</subject><ispartof>Food analytical methods, 2022-11, Vol.15 (11), p.2984-2993</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-71f742410e8aee50aa4589072b4740f10bebec82d5696b63cc50092950dc6b143</citedby><cites>FETCH-LOGICAL-c319t-71f742410e8aee50aa4589072b4740f10bebec82d5696b63cc50092950dc6b143</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12161-022-02346-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12161-022-02346-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27928,27929,41492,42561,51323</link.rule.ids></links><search><creatorcontrib>Kang, Xuming</creatorcontrib><creatorcontrib>Zhao, Yanfang</creatorcontrib><creatorcontrib>Peng, Jixing</creatorcontrib><creatorcontrib>Ding, Haiyan</creatorcontrib><creatorcontrib>Tan, Zhijun</creatorcontrib><creatorcontrib>Han, Cui</creatorcontrib><creatorcontrib>Sheng, Xiaofeng</creatorcontrib><creatorcontrib>Liu, Xiyin</creatorcontrib><creatorcontrib>Zhai, Yuxiu</creatorcontrib><title>Authentication of the Geographical Origin of Shandong Scallop Chlamys farreri Using Mineral Elements Combined with Multivariate Data Analysis and Machine Learning Algorithm</title><title>Food analytical methods</title><addtitle>Food Anal. Methods</addtitle><description>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.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Analytical Chemistry</subject><subject>Authentication</subject><subject>Chemical composition</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Chemistry/Food Science</subject><subject>Data analysis</subject><subject>Discriminant analysis</subject><subject>Food Science</subject><subject>Fraud</subject><subject>Geography</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Microbiology</subject><subject>Multivariate analysis</subject><subject>Scallops</subject><subject>Seafood</subject><subject>Support vector machines</subject><issn>1936-9751</issn><issn>1936-976X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kVFLwzAUhYsoOKd_wKeAz9UkTdP2scw5hY09zIFv4bZL24y2mUmq7D_5I8020TcfQnLPPd8hcILgluB7gnHyYAklnISYUn8ixsP0LBiRLOJhlvC38993TC6DK2u3GHPMCB0FX_ngGtk7VYJTuke6Qn5GM6lrA7vGyy1aGlWr42rVQL_RfY1WXm_1Dk2aFrq9RRUYI41Ca6v8dqF6aTw4bWXnsy2a6K7w2gZ9KtegxdA69QFGgZPoERygvId2b5VFPh4toGy8Gc0lmP4Ql7e1Nh7sroOLClorb37ucbB-mr5OnsP5cvYyyedhGZHMhQmpEkYZwTIFKWMMwOI0wwktWMJwRXAhC1mmdBPzjBc8KssY44xmMd6UvCAsGgd3p9yd0e-DtE5s9WD8H62gCU0JZ0nGvYueXKXR1hpZiZ1RHZi9IFgcWhGnVoRvRRxbEamHohNkvbmvpfmL_of6Bl4jkmc</recordid><startdate>20221101</startdate><enddate>20221101</enddate><creator>Kang, Xuming</creator><creator>Zhao, Yanfang</creator><creator>Peng, Jixing</creator><creator>Ding, Haiyan</creator><creator>Tan, Zhijun</creator><creator>Han, Cui</creator><creator>Sheng, Xiaofeng</creator><creator>Liu, Xiyin</creator><creator>Zhai, Yuxiu</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20221101</creationdate><title>Authentication of the Geographical Origin of Shandong Scallop Chlamys farreri Using Mineral Elements Combined with Multivariate Data Analysis and Machine Learning Algorithm</title><author>Kang, Xuming ; Zhao, Yanfang ; Peng, Jixing ; Ding, Haiyan ; Tan, Zhijun ; Han, Cui ; Sheng, Xiaofeng ; Liu, Xiyin ; Zhai, Yuxiu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-71f742410e8aee50aa4589072b4740f10bebec82d5696b63cc50092950dc6b143</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Analytical Chemistry</topic><topic>Authentication</topic><topic>Chemical composition</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Chemistry/Food Science</topic><topic>Data analysis</topic><topic>Discriminant analysis</topic><topic>Food Science</topic><topic>Fraud</topic><topic>Geography</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Microbiology</topic><topic>Multivariate analysis</topic><topic>Scallops</topic><topic>Seafood</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kang, Xuming</creatorcontrib><creatorcontrib>Zhao, Yanfang</creatorcontrib><creatorcontrib>Peng, Jixing</creatorcontrib><creatorcontrib>Ding, Haiyan</creatorcontrib><creatorcontrib>Tan, Zhijun</creatorcontrib><creatorcontrib>Han, Cui</creatorcontrib><creatorcontrib>Sheng, Xiaofeng</creatorcontrib><creatorcontrib>Liu, Xiyin</creatorcontrib><creatorcontrib>Zhai, Yuxiu</creatorcontrib><collection>CrossRef</collection><jtitle>Food analytical methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kang, Xuming</au><au>Zhao, Yanfang</au><au>Peng, Jixing</au><au>Ding, Haiyan</au><au>Tan, Zhijun</au><au>Han, Cui</au><au>Sheng, Xiaofeng</au><au>Liu, Xiyin</au><au>Zhai, Yuxiu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Authentication of the Geographical Origin of Shandong Scallop Chlamys farreri Using Mineral Elements Combined with Multivariate Data Analysis and Machine Learning Algorithm</atitle><jtitle>Food analytical methods</jtitle><stitle>Food Anal. Methods</stitle><date>2022-11-01</date><risdate>2022</risdate><volume>15</volume><issue>11</issue><spage>2984</spage><epage>2993</epage><pages>2984-2993</pages><issn>1936-9751</issn><eissn>1936-976X</eissn><abstract>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.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s12161-022-02346-8</doi><tpages>10</tpages></addata></record> |
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| subjects | Accuracy Algorithms Analytical Chemistry Authentication Chemical composition Chemistry Chemistry and Materials Science Chemistry/Food Science Data analysis Discriminant analysis Food Science Fraud Geography Learning algorithms Machine learning Microbiology Multivariate analysis Scallops Seafood Support vector machines |
| title | Authentication of the Geographical Origin of Shandong Scallop Chlamys farreri Using Mineral Elements Combined with Multivariate Data Analysis and Machine Learning Algorithm |
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