Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression

Dry-cured ham is a traditional Mediterranean meat product consumed throughout the world. This product is very variable in {\color{red} terms} of composition and quality. Consumer's acceptability of this product is influenced by different factors, in particular, visual intramuscular fat and its distribution across the slice, also known as marbling. On-line marbling assessment is of great interest for the industry for classification purposes. However, until now this assessment has been traditionally carried out by panels of experts and this methodology cannot be implement in industry. We propose a complete automatic system to predict marbling degree of dry-cured ham slices, which combines: 1) the color texture features of regions of interest (ROIs) extracted automatically for each muscle; and 2) machine learning models to predict the marbling. For the ROIs extraction algorithm more than the 90% of pixels of the ROI fall into the true muscle. The proposed system achieves a correlation of 0.92 using the support vector regression and a set of color texture features including statistics of each channel of RGB color image and Haralick's coeficients of its grey-level version. The mean absolute error was 0.46, which is lower than the standard desviation (0.5) of the marbling scores evaluated by experts. This high accuracy in the marbling prediction for sliced dry-cured ham would allow to deploy its application in the dry-cured ham industry.

keywords: Dry cured ham, intramuscular fat, marbling, Support Vector Regression, texture analysis, Image Segmentation