A Novel data preprocessing method for the modeling and prediction of freeze-drying behavior of apples: multiple output-dependent data scaling (MODDS)

dc.authorid0000-0001-7076-1911en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorPolat, Kemal
dc.contributor.authorKırmacı, Volkan
dc.date.accessioned2021-06-23T19:34:01Z
dc.date.available2021-06-23T19:34:01Z
dc.date.issued2012
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn the present study, the freeze drying behavior of apples have been modeled and predicted. Because freeze-drying is a very expensive and complex process, modeling of the freeze-drying process is a challenging task. In this study, a novel data scaling method called multiple output-dependent data scaling (MODDS) has been proposed and combined with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the moisture content (MC), moisture ratio (MR), and drying rate (DR) values, which are outputs of freeze-drying behavior of apples. The input parameters of the freeze drying system are the sample thicknesses, drying time, pressure, relative humidity, chamber temperature, and sample temperature. Using the input parameters, the outputs of the freeze-drying process of apples were predicted using a hybrid system based on MODDS and ANFIS. In the first stage, only input parameters were scaled using MODDS. In the second stage, the outputs of freeze drying of apples were predicted with the scaled input parameters using ANFIS algorithm. Ninety-two samples were included in the data set, including 10-, 7-, and 5-mm samples. In order to evaluate the performance of the proposed model, the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R-2), index of agreement (IA), and mean absolute percentage error (MAPE) were used. Though MSE values of 2.48, 0.035, and 0.011 and IA values of 0.887, 0.887, and 0.466 were obtained for MC, MR, and DR, respectively, using the ANFIS prediction algorithm the hybrid MODDS-ANFIS model achieved MSE values of 0.003, 0.00005, and 0.00007 and IA values of 0.999, 0.999, and 0.993 for the prediction of MC, MR, and DR, respectively. The results obtained demonstrate that the proposed hybrid system is a robust and efficient method for the modeling and prediction of freeze-drying behavior of apples.en_US
dc.identifier.doi10.1080/07373937.2011.630496
dc.identifier.endpage196en_US
dc.identifier.issn0737-3937
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-84859320519en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage185en_US
dc.identifier.urihttps://doi.org/10.1080/07373937.2011.630496
dc.identifier.urihttps://hdl.handle.net/20.500.12491/7336
dc.identifier.volume30en_US
dc.identifier.wosWOS:000301844500008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofDrying Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAppleen_US
dc.subjectDrying Behavioren_US
dc.subjectFreeze Dryingen_US
dc.subjectModelingen_US
dc.subjectMultiple output dependent data scalingen_US
dc.titleA Novel data preprocessing method for the modeling and prediction of freeze-drying behavior of apples: multiple output-dependent data scaling (MODDS)en_US
dc.typeArticleen_US

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