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)

Yadigar Seyfi CankalMehmet Suleyman ÜnlütürkSevcan Mehmet UnluturkCankal, Yadigar SeyfiUnluturk, Mehmet S.Unluturk, Sevcan2025-10-062023146685641466-85641878-552210.1016/j.ifset.2023.1034392-s2.0-85166176069https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166176069&doi=10.1016%2Fj.ifset.2023.103439&partnerID=40&md5=9a89fed44dfa16df62b5450ef1bcaf0bhttps://gcris.yasar.edu.tr/handle/123456789/8423https://doi.org/10.1016/j.ifset.2023.103439Uniform 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 ∼60 mJ/cm2. 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. © 2023 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessComputer Vision, Fluence, Food Surfaces, Radiochromic Films, Uv Dose, Uv Irradiation, Color, Colorimetry, Computer Vision, Convolutional Neural Networks, Fruits, Irradiation, Color Difference, Convolutional Neural Network, Dose Distributions, Fluences, Food Surfaces, Images Processing, Radiochromic Film, Uv Dose, Uv Irradiation, Uv-c Lights, ConvolutionColor, Colorimetry, Computer vision, Convolutional neural networks, Fruits, Irradiation, Color difference, Convolutional neural network, Dose distributions, Fluences, Food surfaces, Images processing, Radiochromic film, UV dose, UV irradiation, UV-C lights, ConvolutionUV DoseUV IrradiationFluenceRadiochromic FilmsFood SurfacesComputer VisionFluence (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)Article