Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification

Gerard Masferrer; Ricard Carreras; Maria Font-I-Furnols; Marina Gispert; Moises Serra; Pere Marti-Puig

doi:10.1016/j.meatsci.2019.04.018

Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification

Meat Sci. 2019 Sep:155:1-7. doi: 10.1016/j.meatsci.2019.04.018. Epub 2019 Apr 25.

Authors

Gerard Masferrer¹, Ricard Carreras², Maria Font-I-Furnols³, Marina Gispert³, Moises Serra⁴, Pere Marti-Puig⁴

Affiliations

¹ Information and Digital Technology Department, UVic-UCC, Vic, Barcelona, Spain; Mafrica.SA, Paratge Can Canals Nou, S/N 08250, Sant Joan de Vilatorrada, BCN, Spain. Electronic address: gerard.masferrer@uvic.cat.
² Mafrica.SA, Paratge Can Canals Nou, S/N 08250, Sant Joan de Vilatorrada, BCN, Spain.
³ IRTA, Product Quality, 17121 Monells, Girona, Spain.
⁴ Information and Digital Technology Department, UVic-UCC, Vic, Barcelona, Spain.

PMID: 31039465
DOI: 10.1016/j.meatsci.2019.04.018

Abstract

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.

Keywords: Dry-cured hams; Ham-fat grading; Pattern recognition; Sorting; Subcutaneous fat thickness.

MeSH terms

Abattoirs
Animals
Female
Male
Pattern Recognition, Automated / economics
Pattern Recognition, Automated / methods*
Red Meat / classification*
Red Meat / standards
Spain
Subcutaneous Fat*
Sus scrofa