Application of a machine learning-based regression method to describe Listeria monocytogenes behaviour in milk

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dc.authoridyücel, özgün/0000-0001-8916-2628
dc.authoridyücel, özgün/0000-0001-8916-2628
dc.authorwosidyücel, özgün/JAN-6493-2023
dc.authorwosidyücel, özgün/AAE-3071-2020
dc.contributor.authorTarlak, Fatih
dc.contributor.authorYucel, Ozgun
dc.date.accessioned2024-06-13T20:18:41Z
dc.date.available2024-06-13T20:18:41Z
dc.date.issued2022
dc.departmentİstanbul Gedik Üniversitesi
dc.description.abstractThe main aim of the present study was to develop a prediction tool to describe Listeria monocytogenes behaviour in milk by employing traditionally used models (the re-parametrized Gompertz, Baranyi and Huang models) and an alterna-tively proposed machine learning-based regression model. The fitting capability of both groups of models was evaluated and compared considering their statistical indices (coefficient of determination R-2, root mean square error RMSE). The machine learning-based regression model provided better predictions (with R-2 of 0.958 and RMSE of 0.407) than the traditionally used models. The prediction capability of both methodologies was tested considering externally collected data from the literature. The machine learning-based regression model in the validation process gave satisfactory sta-tistical indices (bias factor of 1.016 and accuracy factor of 1.056), which is better prediction power than the traditionally used models. These results indicated that the machine learning-based regression method can be reliably employed as an alternative way of describing the growth behaviour of L. monocytogenes in milk. Therefore, the software developed in this work has a significant potential to be used as an alternative simulation method to traditionally used approach in the field of predictive microbiology.
dc.identifier.endpage388
dc.identifier.issn1336-8672
dc.identifier.issn1338-4260
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85144220362
dc.identifier.scopusqualityQ3
dc.identifier.startpage380
dc.identifier.urihttps://hdl.handle.net/11501/1488
dc.identifier.volume61
dc.identifier.wosWOS:000897993300008
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherVup Food Research Inst, Bratislava
dc.relation.ispartofJournal of Food and Nutrition Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectData Mining
dc.subjectPrediction Tool
dc.subjectGaussian Process Regression
dc.subjectPredictive Microbiology
dc.subjectStep Modeling Approach
dc.subjectBacterial-Growth
dc.subjectChicken Meat
dc.subjectLag Phase
dc.subjectPrediction
dc.subjectEnvironment
dc.subjectValidation
dc.subjectKinetics
dc.titleApplication of a machine learning-based regression method to describe Listeria monocytogenes behaviour in milk
dc.typeArticle

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