Meta-recommendation of pork technological quality standards

Autor(es)

Peres, Louise M. ^[1] ; Barbon Junior, Sylvio ^{[2, 3]} ; Lopes, Jessica F. ^[3] ; Fuzyi, Estefania M. ^[4] ; Barbon, Ana P. A. C. ^[1] ; Armangue, Joel G. ^[5] ; Bridi, Ana M. ^[1]

Afiliação do(s) autor(es)

^[1] Londrina State Univ UEL, Dept Anim Sci, BR-86057970 Londrina, Parana - Brazil

^[2] Londrina State Univ UEL, Dept Comp Sci, BR-86057970 Londrina, Parana - Brazil

^[3] Londrina State Univ UEL, Dept Elect Engn, BR-86057970 Londrina, Parana - Brazil

^[4] Pontificia Univ Catolica Parana PUCPR, Dept Inf, BR-80215901 Curitiba, Parana - Brazil

^[5] Inst Recerca & Tecnol Agroalimentaries IRTA, Dept Prod Qual, Monells 17121 - Spain

• Tipo de documento: Artigo Científico
• Fonte: BIOSYSTEMS ENGINEERING; v. 210, p. 13-19, OCT 2021.
• Citações Web of Science: 0

Resumo

Pork quality classification is supported by different reference standards that are widely reported in the literature. However, selecting the most suitable standard for each type of meat samples remains a challenge, due to their intrinsic variation according to the quality parameters' interval. The usage of meta-learning was proposed to automatically recommend the most adequate standard for a determined sample collection, leading to a more accurate classification. The meta-learning procedure has emerged from the machine learning research field to solve the algorithm selection dilemma, outlining a new method for pork quality classification. The applicability and advantages of using a suitable classification standard for pork quality were addressed using the J48 Decision Tree (DT) algorithm, which serves as the meta-recommender. Experiments conducted with six pork standards revealed promising results based on a few meta-attributes (L{*}, water hold capacity, and dataset entropy) as the approach successfully recommended all scenarios. (C) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 15/24351-2 - Análise de imagens e espectroscopia de infravermelho próximo (NIR) na avaliação de qualidade e autenticação de alimentos
• Beneficiário: Douglas Fernandes Barbin
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores
• Processo FAPESP: 18/02500-4 - Análise de alimentos: uma abordagem utilizando imagens NIR
• Beneficiário: Luis Jam Pier Cruz Tirado
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 19/04833-3 - Uma nova abordagem baseada em imagens hiperespectrais para autenticação do sistema de cultivo de sementes
• Beneficiário: Luis Jam Pier Cruz Tirado
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Mestrado