Effectiveness of machine learning algorithms as a tool to meat traceability system. A case study to classify Spanish Mediterranean lamb carcasses

García-Infante, Manuel; Castro-Valdecantos, Pedro; Delgado-Pertíñez, Manuel; Teixeira, Alfredo; Guzmán, José Luis; Horcada-Ibáñez, Alberto

Publication

Effectiveness of machine learning algorithms as a tool to meat traceability system. A case study to classify Spanish Mediterranean lamb carcasses

2024Journal article

dc.contributor.author	García-Infante, Manuel
dc.contributor.author	Castro-Valdecantos, Pedro
dc.contributor.author	Delgado-Pertíñez, Manuel
dc.contributor.author	Teixeira, Alfredo
dc.contributor.author	Guzmán, José Luis
dc.contributor.author	Horcada-Ibáñez, Alberto
dc.date.accessioned	2024-06-13T10:54:46Z
dc.date.available	2024-06-13T10:54:46Z
dc.date.issued	2024
dc.description.abstract	Establishing the traceability of meat products has been a major focus of food science in recent decades. In this context, recent advances in food nutritional biomarker identification and improvements in statistical technology have allowed for more accurate identification and classification of food products. Moreover, artificial intelligence has now provided a new opportunity for optimizing existing methods to identify animal products. This study presents a comparative analysis of the effectiveness of different machine learning algorithms based on raw data from analyses of organoleptic, sensory and nutritional meat traits to differentiate categories of commercial lamb from an indigenous Spanish breed (Mallorquina breed) obtained from the following production systems: suckling lambs; light lambs from grazing; and light lambs from grazing supplemented with grain. Six machine learning algorithms were evaluated: Artificial Neural Network (ANN), Decision Tree, K-Nearest Neighbours (KNN), Naive Bayes, Multinomial Logistic Regression, and Support Vector Machine (SVM). For each algorithm, we tested three datasets, namely organoleptic traits and sensorial traits (CIELAB colour, water holding capacity, Warner-Bratzler shear force, volatile compounds and trained tasters), and nutritional traits (proximate composition and fatty acid profile). We also tested a combination of all three datasets. All the data were combined into a dataset with 144 variables resulting from the meat characterization, which included 11,232 event records. The ANN algorithm stood out for its high score with each of the three datasets used. In fact, we obtained an overall accuracy of 0.88, 0.83, and 0.88 for the organoleptic-sensory, nutritional, and combined datasets, respectively. The effectiveness of using the SVM algorithm to assign categories of lambs according to its production system performed better with nutritional traits and the full characterization, with performances equal to those obtained with ANN. The KNN algorithm showed the worst performance, with overall accuracies of 0.54 or lower for each of the datasets used. The results of this study demonstrate that machine learning is a useful tool for classifying commercial lamb carcasses. In fact, the ANN and SVM algorithms could be proposed as tools for differentiating categories of lamb production based on the organoleptic, sensory and nutritional characteristics of Mediterranean light lambs' meat. However, in order to improve the traceability methods of lamb meat production systems as a guarantee for consumers and to improve the learning processes used by these algorithms, more studies along these lines with other lamb breeds are required.	pt_PT
dc.description.sponsorship	This research has been financed by the Institute for Agricultural and Fisheries Research and Training (IRFAP) of the Government of the Balearic Islands (PRJ201502671-0781), the Spanish National Institute of Agricultural and Food Research and Technology and the European Social Fund (FPI2014-00013). Particular gratefulness to PhD Oliva Polvillo Polo (CITIUS, University of Seville’s Centre for Research) for contributing her knowledge in chromatography analysis	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	García-Infante, Manuel; Castro-Valdecantos, Pedro; Delgado-Pertíñez, Manuel; Teixeira, Alfredo; Guzmán, José Luis; Horcada, Alberto (2024). Effectiveness of machine learning algorithms as a tool to meat traceability system. A case study to classify Spanish Mediterranean lamb carcasses. Food Control. ISSN 0956-7135. 164, p. 1-12	pt_PT
dc.identifier.doi	10.1016/j.foodcont.2024.110604	pt_PT
dc.identifier.issn	0956-7135
dc.identifier.uri	http://hdl.handle.net/10198/29894
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Elsevier	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Artificial neural network	pt_PT
dc.subject	Carcass classification	pt_PT
dc.subject	Lamb system production	pt_PT
dc.subject	Meat traceability	pt_PT
dc.subject	Nutritive traits	pt_PT
dc.subject	Organoleptic traits	pt_PT
dc.title	Effectiveness of machine learning algorithms as a tool to meat traceability system. A case study to classify Spanish Mediterranean lamb carcasses	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.endPage	12	pt_PT
oaire.citation.startPage	1	pt_PT
oaire.citation.title	Food Control	pt_PT
oaire.citation.volume	164	pt_PT
person.familyName	Teixeira
person.givenName	Alfredo
person.identifier	958487
person.identifier.ciencia-id	2A1A-FF0C-185B
person.identifier.orcid	0000-0003-4607-4796
person.identifier.rid	G-4118-2011
person.identifier.scopus-author-id	56195849200
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
relation.isAuthorOfPublication	27cc89a2-6661-4d8d-a727-21109c04a74e
relation.isAuthorOfPublication.latestForDiscovery	27cc89a2-6661-4d8d-a727-21109c04a74e