Modeling Quality Changes in Brined Bream (Megalobrama amblycephala) Fillets During Storage: Comparison of the Arrhenius Model, BP, and RBF Neural Network

Huiyi Wang, Chunli Kong, Dapeng Li, Na Qin, Hongbing Fan, Hui Hong, Yongkang Luo

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

To evaluate and predict the freshness of brined bream (Megalobrama amblycephala) fillets stored at different temperatures, changes in quality [nucleotide degradation products (IMP, HxR, Hx), K value, sensory assessment (SA), total aerobic counts (TAC), thiobarbituric acid reactive substances (TBARS), and total volatile base nitrogen (TVB-N)] were investigated. The Arrhenius model, back-propagation neural network (BP-NN), and radial basis function neural network (RBF-NN) were established and compared. The RBF-NN predicted changes of SA, TAC, K value, TVB-N, TBARS, and HxR of brined fillets during storage with relative errors all within ±5 %, while the BP-NN values were all within ±10 % (except for the values at day 2 for K value, day 2 and day 4 for HxR). For the Arrhenius model, the relative errors of TVB-N were all within ±10 %, and those of SA, TAC, K value, TBARS, and HxR ranged from 0.58 to 44.37 %. Thus, RBF-NN is a promising method for predicting the changes in the quality of bream during storage at 270–282 K.

Original languageEnglish
Pages (from-to)2429-2443
Number of pages15
JournalFood and Bioprocess Technology
Volume8
Issue number12
DOIs
StatePublished - Dec 1 2015

Bibliographical note

Publisher Copyright:
© 2015, Springer Science+Business Media New York.

Keywords

  • Arrhenius model
  • Back-propagation neural network
  • Fish fillets
  • Quality
  • Radial basis function neural network

ASJC Scopus subject areas

  • Food Science
  • Safety, Risk, Reliability and Quality
  • Process Chemistry and Technology
  • Industrial and Manufacturing Engineering

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