Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification
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Autor/a
Masferrer, Gerard
Carreras, Ricard
Font-i-Furnols, Maria
Serra, Moises
Marti-Puig, Pere
Fecha de publicación
2019-04-25ISSN
0309-1740
Resumen
The thickness of the subcutaneous fat (SFT) is a very important parameter in the ham, since determines the process the ham will be submitted. This study compares two methods to predict the SFT in slaughter line: an automatic system using an SVM model (Support Vector Machine) and a manual measurement of the fat carried out by an experienced operator, in terms of accuracy and economic benefit. These two methods were compared to the golden standard obtained by measuring SFT with a ruler in a sample of 400 hams equally distributed within each SFT class. The results show that the SFT prediction made by the SVM model achieves an accuracy of 75.3%, which represents an improvement of 5.5% compared to the manual measurement. Regarding economic benefits, SVM model can increase them between 12 and 17%. It can be concluded that the classification using SVM is more accurate than the one performed manually with an increase of the economic benefit for sorting.
Tipo de documento
Artículo
Versión del documento
Versión aceptada
Lengua
Inglés
Materias (CDU)
663/664 - Alimentos y nutrición. Enología. Aceites. Grasas
Páginas
17
Publicado por
Elsevier
Publicado en
Meat Science
Citación
Masferrer, Gerard, Ricard Carreras, Maria Font-i-Furnols, Marina Gispert, Moises Serra, and Pere Marti-Puig. 2019. "Automatic Ham Classification Method Based On Support Vector Machine Model Increases Accuracy And Benefits Compared To Manual Classification". Meat Science 155: 1-7. Elsevier BV. doi:10.1016/j.meatsci.2019.04.018.
Program
Qualitat i Tecnologia Alimentària
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