ISSN: 0973-7510

E-ISSN: 2581-690X

Research Article | Open Access
Fatih Tarlak1, Ozgun Yucel2 and Kianoush Khosravi-Darani3
1Department of Nutrition and Dietetics, Faculty of Health Science, Istanbul Gedik University, Cumhuriyet Street Number: 1, 34876 Kartal, Istanbul, Turkey.
2Department of Chemical Engineering, Faculty of Engineering, Gebze Technical University, Cumhuriyet Street Number: 2254, 41400 Gebze, Kocaeli, Turkey.
3Department of Food Technology Research, Faculty of Nutrition Sciences and Food Technology/National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Velenjak Street Shahid Chamran Highway 2, 19839-63113 Tehran, Iran.
J Pure Appl Microbiol. 2022;16(2):1263-1273 | Article Number: 7569
https://doi.org/10.22207/JPAM.16.2.55 | © The Author(s). 2022
Received: 26/01/2022 | Accepted: 25/04/2022 | Published online: 01/06/2022
Issue online: June 2022
Abstract

The main aim of the current work was to build up a new mathematical modelling approach in predictive food microbiology field for the prediction of growth kinetics of microorganisms. For this purpose, the bacterial growth data of Pseudomonas spp. in whole fish (gilt-head seabream) subjected to isothermal and non-isothermal storage temperatures were collected from previously published growth curves. Maximum specific growth rate (1/h) and lag phase duration (h) were described as a function of storage temperature using the direct two-step, direct one-step and inverse dynamic modelling approaches based on various meta-heuristic optimization algorithms. The fitting capability of the modelling approaches and employed optimization algorithms was separately compared, and the one-step modelling approach for the direct methods and the Bayesian optimization method for the used algorithms provided the best goodness of fit results. These two were then further processed in validation step. The inverse dynamic modelling approach based on the Bayesian optimization algorithm yielded satisfactorily statistical indexes (1.02 > Bias factor > 1.09 and 1.07 > Accuracy factor > 1.13), which indicates it can be reliably used as an alternative way of describing the growth behaviour of Pseudomonas spp. in fish in a fast and efficient manner with minimum labour effort.

Keywords

Inverse dynamic modelling; meta-heuristic optimization; growth behaviour; predictive microbiology

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