Storage time prediction of pork by Computational Intelligence

Author(s)

Barbon, Ana Paula A. C. ; Barbon, Jr., Sylvio ; Mantovani, Rafael Gomes ; Fuzyi, Estefania Mayumi ; Peres, Louise Manha ; Bridi, Ana Maria

• Document type: Journal article
• Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 127, p. 368-375, SEP 2016.
• Web of Science Citations: 12

Abstract

In this paper, a storage time prediction of pork using Computational Intelligence (CI) model was reported. We investigated a solution based on traditional pork assessment towards a low time-cost parameters acquisition and high accurate CI models by selection of appropriate parameters. The models investigated were built by J48, Naive Bayes (NB), k-NN, Random Forest (RF), SVM, MLP and Fuzzy approaches. CI input were traditional quality parameters, including pH, water holding capacity (WHC), color and lipid oxidation extracted from 250 samples of 0, 7 and 14 days of postmortem. Five parameters (pH, WHC, L{*}, a{*} and b{*}) were found superior results to determine the storage time and corroborate with identification in minutes. Results showed RF (94.41%), 3-NN (93.57%), Fuzzy Chi (93.23%), Fuzzy W (92.35%), MLP (88.35%), J48 (83.64%), SVM (82.03%) and NB (78.26%) were modeled by the five parameters. One important observation is about the ease of 0-day identification, followed by 14-day and 7-day independently of CI approach. Result of this paper offers the potential of CI for implementation in real scenarios, inclusive for fraud detection and pork quality assessment based on a non-destructive, fast, accurate analysis of the storage time. (C) 2016 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 12/23114-9 - Use of meta-learning for parameter tuning for classification problems
• Grantee: Rafael Gomes Mantovani
• Support Opportunities: Scholarships in Brazil - Doctorate