RT Journal Article
T1 Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression
A1 Cernadas García, Eva
A1 Fernández Delgado, Manuel
A1 Fulladosa, Elena
A1 Muñoz Moreno, Israel
K1 Dry-cured ham
K1 Intramuscular fat
K1 Marbling
K1 Support vector regression
K1 Texture analysis
K1 Image segmentation
AB Dry-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 industry
PB Elsevier
YR 2022
FD 2022
LK http://hdl.handle.net/10347/29398
UL http://hdl.handle.net/10347/29398
LA eng
NO Expert Systems with Applications  206  (2022) 117765
NO This work has received financial support from the Xunta de Galicia (Centro singular de investigación de Galicia, accreditation 2020– 2023) and the European Union (European Regional Development Fund–ERDF), Project ED431G-2019/04. IRTA’s contribution was also funded by the CCLabel project (RTI-2018- 096883-R-C41) and the CERCA programme from Generalitat de Catalunya
DS Minerva
RD 23 abr 2026