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.
Mafrica.SA, Paratge Can Canals Nou, S/N 08250, Sant Joan de Vilatorrada, BCN, Spain.
Meat Sci. 2019 Sep;155:1-7. doi: 10.1016/j.meatsci.2019.04.018. Epub 2019 Apr 25.
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.
皮下脂肪厚度（SFT）是火腿中非常重要的参数，因为它决定了火腿将经历的加工过程。本研究比较了两种在屠宰线上预测 SFT 的方法：一种是使用支持向量机（SVM）模型的自动系统，另一种是由经验丰富的操作人员进行的手动脂肪测量，就准确性和经济效益而言。这两种方法与通过在每个 SFT 等级内均等地分布的 400 个火腿样本中使用尺子测量 SFT 获得的黄金标准进行了比较。结果表明，SVM 模型对 SFT 的预测精度达到 75.3%，与手动测量相比提高了 5.5%。关于经济效益，SVM 模型可以提高 12%至 17%。可以得出结论，使用 SVM 进行分类比手动分类更准确，分类的经济效益更高。
