Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/349693
Type: Artigo
Title: Fuzzy approach for classification of pork into quality grades: coping with unclassifiable samples
Author: Peres, Louise Manha
Barbon Jr, Sylvio
Fuzyi, Estefânia Mayumi
Barbon, Ana Paula A. C.
Barbin, Douglas Fernandes
Saito, Priscila Tiemi Maeda
Andreo, Nayara
Bridi, Ana Maria
Abstract: Meat classification methods are commonly based on quality parameters standardized by numeric ranges. However, some animal samples from different production chains do not match the current grades proposed. These unclassifiable samples are not capable to fit into a standard created by crisp range of values due to being infeasible toward its definition. An alternative to handle this kind of sample classification is the fuzzy logic, which could deal with uncertainty and ambiguity degree like human reasoning. In this work, we compare the traditional classification method and fuzzy approaches with the objective to handle the infeasible samples. This was compared to traditional pork standards using eleven real-life datasets with a total of 1798 samples described by pH, water holding capacity and/or L∗ value. The results demonstrated that traditional classification could not predict the unclassifiable samples. On the other hand, the fuzzy approaches improve significantly the number of classified samples. Performance of the fuzzy approaches were compared with several machine learning algorithms, but no significant statistical difference was observed. Finally, a real-life study case was explored, highlighting some advantages and further achievements of the fuzzy modeling
Subject: Lógica fuzzy
Country: Países Baixos
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.compag.2018.05.009
Address: https://www.sciencedirect.com/science/article/pii/S0168169917310773
Date Issue: 2018
Appears in Collections:FEA - Artigos e Outros Documentos

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