Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/13779
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dc.contributor.authorCankal, Yadigar Seyfi-
dc.contributor.authorÜnlütürk, Mehmet S.-
dc.contributor.authorÜnlütürk, Sevcan-
dc.date.accessioned2023-10-03T07:15:32Z-
dc.date.available2023-10-03T07:15:32Z-
dc.date.issued2023-
dc.identifier.issn1466-8564-
dc.identifier.issn1878-5522-
dc.identifier.urihttps://doi.org/10.1016/j.ifset.2023.103439-
dc.identifier.urihttps://hdl.handle.net/11147/13779-
dc.description.abstractUniform Fluence (UV Dose) distribution on food surfaces is essential for an effective UV process design. In this study, the use of radiochromic films (RCFs) with a computer vision system (CVS) integrating image processing and Convolutional Neural Network (CNN) is proposed as an alternative method to assess Fluence distribution of UV-C light at 254 nm on food surfaces. The color difference of RCFs exposed to different UV irradiance and exposure times was correlated with Fluence. The validity of the developed methodology was proved by applying it to the surface of apple fruits of different shapes and sizes. A linear relationship was found between the color difference of RCF and Fluence. The maximum Fluence to be determined using RCFs was similar to 60 mJ/cm(2). The color of the films after UV irradiation remained stable for up to 15 days in darkness when stored at room and refrigeration temperatures. The results showed that RCF can be used as an alternative UV dosimeter.en_US
dc.description.sponsorshipDepartment of Food Engineering, Izmir Institute of Technology, Izmir Turkey [2020IYTE0028]en_US
dc.description.sponsorshipFunding This study was supported by the Department of Food Engineering, Izmir Institute of Technology, Izmir Turkey (2020IYTE0028) .en_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofInnovative Food Science & Emerging Technologiesen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectUV irradiationen_US
dc.subjectUV doseen_US
dc.subjectRadiochromic filmsen_US
dc.subjectFluenceen_US
dc.subjectComputer visionen_US
dc.subjectFood surfacesen_US
dc.subjectCHEMICAL ACTINOMETERen_US
dc.subjectPOTASSIUM-IODIDEen_US
dc.subjectRADIATIONen_US
dc.subjectIODATEen_US
dc.subjectFRESHen_US
dc.subjectDECONTAMINATIONen_US
dc.subjectCOLORen_US
dc.subjectDYESen_US
dc.titleFluence (UV dose) distribution assessment of UV-C light at 254 nm on food surfaces using radiochromic film dosimetry integrated with image processing and convolutional neural network (CNN)en_US
dc.typeArticleen_US
dc.institutionauthor-
dc.departmentİzmir Institute of Technologyen_US
dc.identifier.volume88en_US
dc.identifier.wosWOS:001051030900001en_US
dc.identifier.scopus2-s2.0-85166176069en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.ifset.2023.103439-
dc.authorscopusid58512200300-
dc.authorscopusid6508114835-
dc.authorscopusid15063695700-
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.dept03.08. Department of Food Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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