Development of a New Modelling Approach and Performance Evaluation of Meta-heuristic Optimization Algorithms for the Prediction of Kinetic Growth Parameters for Pseudomonas spp. in Fish

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dc.authoridyücel, özgün/0000-0001-8916-2628
dc.authoridyücel, özgün/0000-0001-8916-2628
dc.authoridKhosravi-Darani, Kianoush/0000-0002-0269-6385
dc.authorwosidyücel, özgün/JAN-6493-2023
dc.authorwosidyücel, özgün/AAE-3071-2020
dc.authorwosidKhosravi-Darani, Kianoush/O-6955-2016
dc.contributor.authorTarlak, Fatih
dc.contributor.authorYucel, Ozgun
dc.contributor.authorKhosravi-Darani, Kianoush
dc.date.accessioned2024-06-13T20:18:28Z
dc.date.available2024-06-13T20:18:28Z
dc.date.issued2022
dc.departmentİstanbul Gedik Üniversitesi
dc.description.abstractThe 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.
dc.identifier.doi10.22207/JPAM.16.2.55
dc.identifier.endpage1273
dc.identifier.issn0973-7510
dc.identifier.issn2581-690X
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85131312546
dc.identifier.scopusqualityQ4
dc.identifier.startpage1263
dc.identifier.urihttps://doi.org/10.22207/JPAM.16.2.55
dc.identifier.urihttps://hdl.handle.net/11501/1382
dc.identifier.volume16
dc.identifier.wosWOS:000810925500013
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherDr M N Khan
dc.relation.ispartofJournal of Pure and Applied Microbiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectInverse Dynamic Modelling
dc.subjectMeta-Heuristic Optimization
dc.subjectGrowth Behaviour
dc.subjectPredictive Microbiology
dc.subjectLactobacillus-Plantarum
dc.subjectShelf-Life
dc.subjectValidation
dc.titleDevelopment of a New Modelling Approach and Performance Evaluation of Meta-heuristic Optimization Algorithms for the Prediction of Kinetic Growth Parameters for Pseudomonas spp. in Fish
dc.typeArticle

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