Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/13779
Title: Fluence (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)
Authors: Cankal, Yadigar Seyfi
Ünlütürk, Mehmet S.
Ünlütürk, Sevcan
Keywords: UV irradiation
UV dose
Radiochromic films
Fluence
Computer vision
Food surfaces
CHEMICAL ACTINOMETER
POTASSIUM-IODIDE
RADIATION
IODATE
FRESH
DECONTAMINATION
COLOR
DYES
Issue Date: 2023
Publisher: Elsevier Sci Ltd
Abstract: Uniform 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.
URI: https://doi.org/10.1016/j.ifset.2023.103439
https://hdl.handle.net/11147/13779
ISSN: 1466-8564
1878-5522
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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