Storage time prediction of pork by Computational Intelligence

• Published in: Computers and electronics in agriculture Vol. 127; pp. 368 - 375
• Main Authors: Barbon, Ana Paula A.C., Barbon, Sylvio, Mantovani, Rafael Gomes, Fuzyi, Estefânia Mayumi, Peres, Louise Manha, Bridi, Ana Maria
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2016
• Subjects: Classification; Computation; Fuzzy; Fuzzy rule based system; Intelligence; Machine learning; Mathematical models; Meat quality; Parameters; Pork; Post mortem; Support vector machines; Fuzzy rule based system; Post mortem; Machine learning; Classification; Meat quality
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2016.06.028

•We use of CI for storage time prediction only by pH, CIELab and WHC parameters.•Our approach has pork assessment by a non-destructive, fast, accurate analysis.•We evaluate the pork aging, accurately, without the analysis of lipid oxidation.•We test prediction by J48, Naïve Bayes, kNN, RF, SVM, MLP and Fuzzy. 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, Naïve 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 14days of post mortem. 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.