Cernadas García, EvaFernández Delgado, ManuelFulladosa, ElenaMuñoz Moreno, Israel2022-11-092022-11-092022Expert Systems with Applications 206 (2022) 117765http://hdl.handle.net/10347/29398Dry-cured ham is a traditional Mediterranean meat product consumed throughout the world. This product is very variable in 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 coefficients of its gray-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 industryeng© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Dry-cured hamIntramuscular fatMarblingSupport vector regressionTexture analysisImage segmentationjournal article10.1016/j.eswa.2022.1177650957-4174open access