Digital Image Analyses As An Alternative Tool For Chicken Quality Assessment

dc.creatorBarbin
dc.creatorDF; Mastelini
dc.creatorSM; Barbon
dc.creatorS; Campos
dc.creatorGFC; Barbon
dc.creatorAPAC; Shimokomaki
dc.creatorM
dc.date2016
dc.date2016-12-06T18:31:40Z
dc.date2016-12-06T18:31:40Z
dc.date.accessioned2018-03-29T02:04:17Z
dc.date.available2018-03-29T02:04:17Z
dc.identifier1537-5129
dc.identifierBiosystems Engineering. ACADEMIC PRESS INC ELSEVIER SCIENCE, n. 144, p. 85 - 93.
dc.identifier1537-5110
dc.identifierWOS:000374708500008
dc.identifier10.1016/j.biosystemseng.2016.01.015
dc.identifierhttp://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S153751101530060X
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/320350
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1311116
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionPoultry meat colour is an important quality attribute for the rapid detection of "pale poultry syndrome", as it is affected by conditions of animal welfare during pre-mortem period. The meat processing industry demands a fast and non-contact method for accurate meat colour assessment. In the present study, computer vision was tested as a potential tool to predict colour measurements compared to CIELab attributes of chicken breast (pectoralis major) obtained by analytical reference measurements. The proposed approach using computer vision was successful in avoiding pixels with little information (specular reflection) and based on an illumination normalisation step it was obtained an acceptable correlation between colorimeter measurements and the proposed framework (Delta E = 5.2). High correlation coefficients obtained between computer vision and colorimeter validate the approach for measuring L* colour component. Results for determination coefficient was R-2 = 0.99 for L*. In addition, our framework reach R-2 = 0.74 for a*, and R-2 = 0.88 for b* component. Results suggest that computer vision methods base on an RGB device can become useful tool for fast quality assessment of chicken meat in large-scale processing plants. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.
dc.description144
dc.description
dc.description85
dc.description93
dc.descriptionCAPES
dc.descriptionFundacao Araucaria
dc.descriptionNational Council for Scientific and Technological Development (CNPq) [502241/2013-6]
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description
dc.description
dc.description
dc.languageEnglish
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCE
dc.publisherSAN DIEGO
dc.relationBiosystems Engineering
dc.rightsfechado
dc.sourceWOS
dc.subjectPale Poultry Muscle
dc.subjectPse
dc.subjectComputer Vision
dc.subjectClassification
dc.subjectMultivariate Statistical Analyses
dc.titleDigital Image Analyses As An Alternative Tool For Chicken Quality Assessment
dc.typeArtículos de revistas


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