Storage time prediction of pork by Computational Intelligence
•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...
Saved in:
Published in | Computers and electronics in agriculture Vol. 127; pp. 368 - 375 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.09.2016
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | •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. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0168-1699 1872-7107 |
DOI: | 10.1016/j.compag.2016.06.028 |