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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.)

Storage time prediction of pork by Computational Intelligence

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Author(s):
Barbon, Ana Paula A. C. ; Barbon, Jr., Sylvio ; Mantovani, Rafael Gomes ; Fuzyi, Estefania Mayumi ; Peres, Louise Manha ; Bridi, Ana Maria
Total Authors: 6
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 type: Scholarships in Brazil - Doctorate