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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Meta-recommendation of pork technological quality standards

Full text
Author(s):
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]
Total Authors: 7
Affiliation:
[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
Total Affiliations: 5
Document type: Journal article
Source: BIOSYSTEMS ENGINEERING; v. 210, p. 13-19, OCT 2021.
Web of Science Citations: 0
Abstract

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)

FAPESP's process: 15/24351-2 - Applications of image analyses and NIR spectroscopy for quality assessment and authentication of food products
Grantee:Douglas Fernandes Barbin
Support Opportunities: Research Grants - Young Investigators Grants
FAPESP's process: 18/02500-4 - Food analyses using NIR spectral imaging
Grantee:Luis Jam Pier Cruz Tirado
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 19/04833-3 - A new approach based on hyperspectral imaging for farming system authentication of seeds
Grantee:Luis Jam Pier Cruz Tirado
Support Opportunities: Scholarships abroad - Research Internship - Master's degree