Prediction of the Growth Rates of Pseudomonas sp. in Seafood Based on Artificial Neural Network (ANN) Model

GENÇ İ. Y.

Journal of Aquatic Food Product Technology, vol.32, no.3, pp.359-371, 2023 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.1080/10498850.2023.2219675
  • Journal Name: Journal of Aquatic Food Product Technology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Environment Index, Food Science & Technology Abstracts, Veterinary Science Database
  • Page Numbers: pp.359-371
  • Keywords: ANN, mathematical modeling, Predictive microbiology, seafood
  • Isparta University of Applied Sciences Affiliated: Yes

Abstract

The aim of this study is to predict the growth rate (µmax) of Pseudomonas sp. in seafood under different temperature -2 - 25°C and modified atmosphere packaging (MAP) conditions. At total 52 µmax were compiled from the literature to develop and 68 different µmax values to validate the ANN model. For the development of ANN model different transfer functions were applied and based on the bias (Bf) (0.91) and accuracy factors (Af) (1.60) 3 layers 10 neurons with purelin transfer function was assumed to be best topology to predict the µmax of Pseudomonas sp. in seafood.