Kinetic and artificial neural network modeling techniques to predict the drying kinetics of Mentha spicata L.

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dc.authoridGoz, Eda/0000-0002-3111-9042
dc.authoridYuceer, Mehmet/0000-0002-2648-3931
dc.authoridTOSUN, Emir/0000-0002-2555-1840
dc.authorwosidGoz, Eda/AAH-3388-2020
dc.authorwosidYuceer, Mehmet/E-5110-2012
dc.authorwosidTOSUN, Emir/S-5125-2018
dc.contributor.authorKarakaplan, Nihan
dc.contributor.authorGoz, Eda
dc.contributor.authorTosun, Emir
dc.contributor.authorYuceer, Mehmet
dc.date.accessioned2024-08-04T20:46:44Z
dc.date.available2024-08-04T20:46:44Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis study presented both the empirical and artificial neural network (ANN) approaches to estimate the moisture content of Mentha spicata. Two different types of drying methods (in shade and in oven (35 and 50 degrees C)) were used to investigate the drying kinetics of the Mentha spicata samples. The effects of drying methods on effective diffusion coefficient, moisture ratio (MR), drying rate, and activation energy were investigated. Moreover, six different thin layer drying models (Page, Diffusion approach, Newton, Modified Henderson, Henderson and Pabis and Pabis and Midilli) and an ANN with feed forward structure were used to define the drying kinetics of these samples. In order to estimate the kinetic model parameters, sequential quadratic programming (SQP) was used. Model performances were evaluated based on the coefficient of determination (R-2), root mean square error (RMSE) and mean absolute percentage error (MAPE%) values. In the kinetic part of the modeling study, the Midilli model provided better results than the others. However, the ANN had the best results when a total assessment was made. The effective diffusion coefficient values were found in the range between 1.31 x 10(-12) and 4.43 x 10(-12) m(2)/s. The activation energy was obtained as 44.31 kJ/kmol. The R-2, MAPE%, and RMSE values for the ANN test data were 1.00, 0.2257, and 5.9447 x 10(-4), respectively. In the future, different modeling approaches will be applied to describe this drying process. Practical applications Drying is a process where heat transfer and mass transfer take place together. Modeling is an innovative approach used in evaluation of experimental data and has increasing popularity in recent years. ANNs are a powerful data-driven method, and they have a very broad area of usage from medicine to engineering issues. Empirical models are another approach for describing experimental data. In this study, these two modeling approaches were used to obtain the MR. Humidity is a condition that needs to be checked in food safety and protection. Therefore, it is very important to ensure control with robust modeling techniques. In this study, the developed ANN model had a high R-2 value (R-2 = 1.00). This indicated that it may be used successfully in real applications.en_US
dc.description.sponsorshipInonu University Research Fund [I.U.B.A.P.2016/24]en_US
dc.description.sponsorshipInonu University Research Fund, Grant/Award Number: I.U.B.A.P.2016/24en_US
dc.identifier.doi10.1111/jfpp.14142
dc.identifier.issn0145-8892
dc.identifier.issn1745-4549
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85070526484en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1111/jfpp.14142
dc.identifier.urihttps://hdl.handle.net/11616/98909
dc.identifier.volume43en_US
dc.identifier.wosWOS:000478972300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofJournal of Food Processing and Preservationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnn Methodologyen_US
dc.subjectLeavesen_US
dc.subjectMicrowaveen_US
dc.subjectBehavioren_US
dc.subjectExtractionen_US
dc.subjectRiveren_US
dc.titleKinetic and artificial neural network modeling techniques to predict the drying kinetics of Mentha spicata L.en_US
dc.typeArticleen_US

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