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dc.contributor.authorBoyacı, İsmail Hakkı
dc.contributor.authorŞümnü, Servet Gülüm
dc.contributor.authorSakiyan, Ozge
dc.date.accessioned2020-01-02T12:50:36Z
dc.date.available2020-01-02T12:50:36Z
dc.date.issued2008-2-20
dc.identifier.issn1935-5130
dc.identifier.urihttps://open.metu.edu.tr/handle/11511/28583
dc.description.abstractDielectric constant (DC) and dielectric loss factor (DLF) are the two principal parameters that determine the coupling and distribution of electromagnetic energy during radiofrequency and microwave processing. In this study, chemometric methods [classical least square (CLS), principle component regression (PCR), partial least square (PLS), and artificial neural networks (ANN)] were investigated for estimation of DC and DLF values of cakes by using porosity, moisture content and main formulation components, fat content, emulsifier type (Purawave (TM), Lecigran (TM)), and fat replacer type (maltodextrin, Simplesse). Chemometric methods were calibrated firstly using training data set, and then they were tested using test data set to determine estimation capability of the method. Although statistical methods (CLS, PCR and PLS) were not successful for estimation of DC and DLF values, ANN estimated the dielectric properties accurately (R (2), 0.940 for DC and 0.953 for DLF). The variation of DC and DLF of the cakes when the porosity value, moisture content, and formulation components were changed were also visualized using the data predicted by trained network.
dc.publisherSpringer Science and Business Media LLC
dc.titleEstimation of Dielectric Properties of Cakes Based on Porosity, Moisture Content, and Formulations Using Statistical Methods and Artificial Neural Networks
relationship.typePublication
dc.publisher.journalFood and Bioprocess Technology
dc.identifier.volume2
dc.identifier.issue4
dc.identifier.startpage353
dc.identifier.endpage360
dc.identifier.doi10.1007/s11947-008-0064-z


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