[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125853-en":3,"doc-seo-125853-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125853,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning strategy for light lamb carcass classification using meat biomarkers","In Mediterranean areas, lamb meat holds high commercial value and customers increasingly want transparency about origin and production/breeding systems. Artificial-intelligence models can distinguish animal origin using dietary information. This study assessed a variable-reduction approach using multiple regression alongside SVM, KNN, and ANN to classify three commercial light lamb carcasses from three feeding diets (Mallorquina) based on fatty-acid and volatile biomarker profiles. Using 14 significant biomarkers, it achieved 86% (SVM) and 98% (KNN/ANN) prediction accuracy, with key discriminant biomarkers identified via stepwise forward regression.","Food Bioscience 59 (2024) 104104  \nContents lists available at ScienceDirect  \nFood Bioscience  \njournal [homepage:](homepage: www.elsevier.com/locate/fbio)[ www.elsevier.com/locate/fbio](homepage: www.elsevier.com/locate/fbio)  \n| Machine learning strategy for light lamb carcass classification using meat biomarkers\u003Cbr>M. García-Infantea, *, P. Castro-Valdecantosa, M. Delgado-Pertin˜ez a, A. Teixeira b, c,\u003Cbr>J.L. Guzma´nd, A. Horcadaa\u003Cbr>a Departamento de Agronomía, Escuela T´ecnica Superior de Ingeniería Agron´omica, Universidad de Sevilla, Ctra. Utrera km 1, Sevilla, 41013, Spain b Laborat´orio Para a Sustentabilidade e Tecnologia em Regi˜oes de Montanha, Instituto Polit´ecnico de Bragança, Campus de Santa Apol´onia, Bragança, 5300-253, Portugal\u003Cbr>c Centro de Investigaç˜ao de Montanha (CIMO), Instituto Polit´ecnico de Bragança, Campus de Santa Apol´onia, Bragança, 5300-253, Portugal\u003Cbr>d Departamento de Ciencias Agroforestales, Escuela T´ecnica Superior de Ingeniería, Universidad de Huelva, “Campus de Excelencia Internacional Agroalimentario, ceiA3”, Campus Universitario de la Ra´bida, Carretera Huelva-Palos de la Frontera, s/n, Palos de la Frontera, Huelva, 21819, Spain |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Meat traceability\u003Cbr>Lamb authentication Artificial neural Network Support Vector machine K-nearest neighbours Foodomic |  | In Mediterranean areas, lamb meat is considered to be of great commercial value. Moreover, consumers are becoming increasingly interested in understanding the origin of lamb meat and its associated production and breeding systems. Among many applications, algorithms based on artificial intelligence are used to identify the origin of food products, and in this context, algorithms such as the Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and the Artificial Neural Network (ANN) have been proposed to differentiate the origin of the animals according to their feeding diet. The objective of this study was to evaluate the performance of a variable reduction method based on a multiple regression model and three widely-used machine learning algorithms (SVM, KNN and ANN) for the classification of three commercial light lamb carcasses, from three feeding diets, inan indigenous Spanish breed (Mallorquina), using fatty acid and volatile compound biomarkers of meat. Machine learning algorithms were employed to discriminate lamb carcasses using 14 identified significant biomarkers, which were arranged based on an estimation of the relative importance (stepwise forward multiple regression Fscore) of the input variables. We achieved high performances for the SVM, KNN and ANN algorithms, with 86%, 98% and 98% prediction accuracy, respectively. Among the 14 biomarkers used, 7 were identified as showing the highest discriminant capacity. The F-scores indicate that C17:1 and C20:5 n-3 fatty acids, and 2,5-dimethylpyrazine and 3-methylbutanal volatile compounds are the four most relevant biomarkers for predicting three lamb feeding diets. |\n\n1. Introduction  \nThe lamb meat market is highly demanding in terms of the quality product and the traceability of the system production (Gracia & De-Magistris, 2013). In fact, each market has its preferences when choosing a specific meat product. While, in Northern Europe, consumers prefer meat from heavy lambs, in the Mediterranean countries of Europe, carcasses from light-weight animals are favoured (Campo et al., 2021). In these Mediterranean areas, consumers choose mainly two types of lamb meat: the consumers’ first choice is meat from suckling lambs slaughtered at one month of age and raised only on mother’s milk, and in second place, light lambs slaughtered around three months old  \n(\"Ternasco\" category), which are raised mainly on forage or grass and concentrate diets (Ferrer-Pe´rez & Gil, 2019).  \nRecently, consumers have become increasingly concerned about intensive meat production systems and the potential ","cbCaioGqnxXwjYBy","https://ap.wps.com/l/cbCaioGqnxXwjYBy","pdf",3090241,4,1,10,"English","en",105,"# Introduction\n## Objective and context\n# Materials and methods\n## Variable reduction and model setup\n# Results\n## Classification performance and key biomarkers\n# Discussion\n## Discriminant biomarkers interpretation","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To evaluate how well a variable-reduction method and three machine-learning algorithms can classify light lamb carcasses from different feeding diets using meat biomarker data.\"},{\"question\":\"Which machine learning algorithms were used for classification?\",\"answer\":\"Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Artificial Neural Network (ANN), combined with variable reduction based on multiple regression.\"},{\"question\":\"Which biomarkers were identified as most relevant for predicting feeding diets?\",\"answer\":\"Four top biomarkers: the fatty acids C17:1 and C20:5 n-3, and the volatile compounds 2,5-dimethylpyrazine and 3-methylbutanal.\"}]","Machine learning strategy for light lamb carcass classification using meat biomarkers | 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is the main goal of the study?","Question",{"text":76,"@type":77},"To evaluate how well a variable-reduction method and three machine-learning algorithms can classify light lamb carcasses from different feeding diets using meat biomarker data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms were used for classification?",{"text":81,"@type":77},"Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Artificial Neural Network (ANN), combined with variable reduction based on multiple regression.",{"name":83,"@type":74,"acceptedAnswer":84},"Which biomarkers were identified as most relevant for predicting feeding diets?",{"text":85,"@type":77},"Four top biomarkers: the fatty acids C17:1 and C20:5 n-3, and the volatile compounds 2,5-dimethylpyrazine and 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