Publication
Intramuscular fat prediction using color and image analysis of bísaro pork breed
| dc.contributor.author | Teixeira, Alfredo | |
| dc.contributor.author | Silva, Severiano | |
| dc.contributor.author | Hasse, Marianne Cristina Goncalves | |
| dc.contributor.author | Almeida, José M.H. | |
| dc.contributor.author | Dias, L.G. | |
| dc.date.accessioned | 2018-02-19T10:00:00Z | |
| dc.date.accessioned | 2021-11-22T14:46:40Z | |
| dc.date.available | 2018-01-19T10:00:00Z | |
| dc.date.available | 2021-11-22T14:46:40Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight and parameters from color and image analysis. These are non-invasive and non-destructive techniques which allow development of rapid, easy and inexpensive methodologies to evaluate pork meat quality in a slaughterhouse. The proposed predictive supervised multivariate models were non-linear. Discriminant mixture analysis to evaluate meat juiciness by classified samples into three groups—0.6 to 1.1%; 1.25 to 1.5%; and, greater than 1.5%. The obtained model allowed 100% of correct classifications (92% in cross-validation with seven-folds with five repetitions). Polynomial support vector machine regression to determine the intramuscular fat presented R2 and RMSE values of 0.88 and 0.12, respectively in cross-validation with seven-folds with five repetitions. This quantitative model (model’s polynomial kernel optimized to degree of three with a scale factor of 0.1 and a cost value of one) presented R2 and RSE values of 0.999 and 0.04, respectively. The overall predictive results demonstrated the relevance of photographic image and color measurements of the muscle to evaluate the intramuscular fat, rarther than the usual time-consuming and expensive chemical analysis. | en_EN |
| dc.description.sponsorship | Work included in the Portuguese PRODER research Project BISOPORC—Pork extensive production of Bísara breed, in two alternative systems: fattening on concentrate vs. chesnut. The CIMO authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support by national funds FCT/MCTES to CIMO (UIDB/00690/2020) and are members of the Healthy Meat network by (CYTED-119RT0568). The CECAV authors are thankful to the project UIDB/CVT/00772/2020 funded by the Foundation for Science and Technology (FCT, Portugal). The authors are grateful to Laboratory of Carcass and Meat Quality of Agriculture School of Polytechnic Institute of Bragança “Cantinho do Alfredo”. | |
| dc.description.version | info:eu-repo/semantics/publishedVersion | en_EN |
| dc.identifier.citation | Teixeira, Alfredo; Silva, Severiano R.; Hasse, Marianne; Almeida, José M.H.; Dias, Luis (2021). Intramuscular fat prediction using color and image analysis of bísaro pork breed. Foods. ISSN 2304-8158 . 10:1, p. 1-12 | en_EN |
| dc.identifier.doi | 10.3390/foods10010143 | en_EN |
| dc.identifier.issn | 2304-8158 | |
| dc.identifier.uri | http://hdl.handle.net/10198/24275 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | en_EN |
| dc.relation | UIDB/CVT/00772/2020 | |
| dc.relation | Mountain Research Center | |
| dc.subject | Bísaro pork | en_EN |
| dc.subject | Image analysis | en_EN |
| dc.subject | Intramuscular fat | en_EN |
| dc.subject | Prediction | en_EN |
| dc.title | Intramuscular fat prediction using color and image analysis of bísaro pork breed | en_EN |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Mountain Research Center | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Teixeira | |
| person.familyName | Dias | |
| person.givenName | Alfredo | |
| person.givenName | Luís G. | |
| person.identifier | 958487 | |
| person.identifier.ciencia-id | 2A1A-FF0C-185B | |
| person.identifier.ciencia-id | 2F11-9092-FAAF | |
| person.identifier.orcid | 0000-0003-4607-4796 | |
| person.identifier.orcid | 0000-0002-1210-4259 | |
| person.identifier.rid | G-4118-2011 | |
| person.identifier.scopus-author-id | 56195849200 | |
| person.identifier.scopus-author-id | 23569169900 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | en_EN |
| rcaap.type | article | en_EN |
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