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

Fuzzy approach for classification of pork into quality grades: coping with unclassifiable samples

Full text
Author(s):
Peres, Louise Manha [1] ; Barbon, Jr., Sylvio [2] ; Fuzyi, Estelania Mayumi [3] ; Barbon, Ana Paula A. C. [1] ; Barbin, Douglas Fernandes [4] ; Maeda Saito, Priscila Tiemi [5] ; Andreo, Nayara [1] ; Bridi, Ana Maria [1]
Total Authors: 8
Affiliation:
[1] Londrina State Univ UEL, Dept Anim Sci, BR-86057970 Londrina - Brazil
[2] Londrina State Univ UEL, Dept Comp Sci, BR-86057970 Londrina - Brazil
[3] Pontificia Univ Catolica Parana PUCPR, Dept Inf, BR-80215901 Curitiba, Parana - Brazil
[4] Campinas State Univ UNICAMP, Dept Food Engn, BR-13083862 Campinas, SP - Brazil
[5] Fed Univ Tecn Parana UTFPR, Dept Comp, BR-86300000 Cornelio Procopio - Brazil
Total Affiliations: 5
Document type: Journal article
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 150, p. 455-464, JUL 2018.
Web of Science Citations: 1
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. (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 type: Research Grants - Young Investigators Grants