Development of prediction software to describe total mesophilic bacteria in spinach using a machine learning-based regression approach

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dc.authoridyücel, özgün/0000-0001-8916-2628
dc.authoridYILDIRIM YALCIN, Meral/0000-0002-5885-8849
dc.authoridyücel, özgün/0000-0001-8916-2628
dc.authorwosidyücel, özgün/JAN-6493-2023
dc.authorwosidYILDIRIM YALCIN, Meral/AFP-2337-2022
dc.authorwosidyücel, özgün/AAE-3071-2020
dc.contributor.authorYildirim-Yalcin, Meral
dc.contributor.authorYucel, Ozgun
dc.contributor.authorTarlak, Fatih
dc.date.accessioned2024-06-13T20:18:19Z
dc.date.available2024-06-13T20:18:19Z
dc.date.issued2023
dc.departmentİstanbul Gedik Üniversitesi
dc.description.abstractThe purpose of this study was to create a tool for predicting the growth of total mesophilic bacteria in spinach using machine learning-based regression models such as support vector regression, decision tree regression, and Gaussian process regression. The performance of these models was compared to traditionally used models (modified Gompertz, Baranyi, and Huang models) using statistical indices like the coefficient of determination (R-2) and root mean square error (RMSE). The results showed that the machine learning-based regression models provided more accurate predictions with an R-2 of at least 0.960 and an RMSE of at most 0.154, indicating that they can be used as an alternative to traditional approaches for predictive total mesophilic. Therefore, the developed software in this work has a significant potential to be used as an alternative simulation method to traditionally used approach in the predictive food microbiology field.
dc.identifier.doi10.1177/10820132231170286
dc.identifier.issn1082-0132
dc.identifier.issn1532-1738
dc.identifier.pmid37073088
dc.identifier.scopus2-s2.0-85153588423
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1177/10820132231170286
dc.identifier.urihttps://hdl.handle.net/11501/1327
dc.identifier.wosWOS:000972066100001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofFood Science and Technology International
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.subjectRandom Forest
dc.subjectGrowth-Rate
dc.subjectLag Phase
dc.subjectContamination
dc.subjectEnvironment
dc.titleDevelopment of prediction software to describe total mesophilic bacteria in spinach using a machine learning-based regression approach
dc.typeArticle

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