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

• Published in: Computers and electronics in agriculture Vol. 150; pp. 455 - 464
• Main Authors: Peres, Louise Manha, Barbon Jr, Sylvio, Fuzyi, Estefânia Mayumi, Barbon, Ana Paula A.C., Barbin, Douglas Fernandes, Maeda Saito, Priscila Tiemi, Andreo, Nayara, Bridi, Ana Maria
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2018; Elsevier BV
• Subjects: Classical logic; Classification; Computational intelligence; Computational mathematics; Fuzzy Logic; Machine learning; Mathematical models; Meat; Pattern recognition; Pork; Quality; Sampling methods; Statistical analysis; Statistical methods; Pattern recognition; Classical logic; Fuzzy Logic; Computational intelligence; Classification; Meat
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2018.05.009

•Fuzzy solution is better than traditional method to classify pork into quality grades.•The fuzzy logic are able to handle infeasible samples in classification.•The fuzzy logic enables the industry to meet specific market requirements.•It was possible to detect the contribution of pH in infeasible samples composition. 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.