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 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...

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Autores: Cernadas García, Eva, Fernández Delgado, Manuel, Fulladosa, Elena, Muñoz Moreno, Israel
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/29398
Acceso en línea:http://hdl.handle.net/10347/29398
Access Level:acceso abierto
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spelling Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regressionCernadas García, EvaFernández Delgado, ManuelFulladosa, ElenaMuñoz Moreno, IsraelDry-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 industryElsevierUniversidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información20222022-01-0120222022-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10347/29398reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostelainstname:Universidad de Santiago de Compostela (USC)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI-2018- 096883-R-C41 SISTEMAS DE CARACTERIZACION Y COMUNICACION DE LA CALIDAD Y LA COMPOSICION NUTRICIONAL DE LOS ALIMENTOS PARA LOS CONSUMIDORES Y LA INDUSTRIA ALIMENTARIAopen accesshttp://purl.org/coar/access_right/c_abf2© 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/)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:minerva.usc.gal:10347/293982026-06-15T12:47:27Z
dc.title.none.fl_str_mv Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression
title Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression
spellingShingle Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression
Cernadas García, Eva
title_short Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression
title_full Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression
title_fullStr Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression
title_full_unstemmed Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression
title_sort Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression
dc.creator.none.fl_str_mv Cernadas García, Eva
Fernández Delgado, Manuel
Fulladosa, Elena
Muñoz Moreno, Israel
author Cernadas García, Eva
author_facet Cernadas García, Eva
Fernández Delgado, Manuel
Fulladosa, Elena
Muñoz Moreno, Israel
author_role author
author2 Fernández Delgado, Manuel
Fulladosa, Elena
Muñoz Moreno, Israel
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información
description 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
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01
2022
2022-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10347/29398
url http://hdl.handle.net/10347/29398
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI-2018- 096883-R-C41 SISTEMAS DE CARACTERIZACION Y COMUNICACION DE LA CALIDAD Y LA COMPOSICION NUTRICIONAL DE LOS ALIMENTOS PARA LOS CONSUMIDORES Y LA INDUSTRIA ALIMENTARIA
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname:Universidad de Santiago de Compostela (USC)
instname_str Universidad de Santiago de Compostela (USC)
reponame_str Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
collection Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
repository.name.fl_str_mv
repository.mail.fl_str_mv
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