Browsing by Author "Unluturk, Sevcan"

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Automated Detection and Quantification of Honey Adulteration Using Thermal Imaging and Convolutional Neural Networks
(Pergamon-Elsevier Science Ltd, 2026) Unluturk, Mehmet S.; Unluturk, Sevcan; Berk, Berkay
Honey is a valuable natural food rich in bioactive substances beneficial to health. Despite strict regulations prohibiting adulteration, honey remains one of the most frequently adulterated foods, often with low-cost commercial syrups. Conventional detection methods require expensive instruments, expert operators, and lengthy analysis times, limiting their practical use. This study introduces a rapid and automated method for detecting and quantifying honey adulteration using thermal image analysis combined with a tailored Convolutional Neural Network (CNN) architecture. Thirty-six pure honey samples (blossom and honeydew) from different regions of Türkiye were adulterated with inverted sugar, maltose, and glucose syrups at varying levels (3 %-60 % weight/weight (w/w)). Samples were heated to 60 degrees C and thermal images were captured during cooling using a custom image-capturing unit. The CNN model employed a multi-layer structure, starting with a shallow network for binary classification (pure vs adulterated honey) achieving 100 % accuracy, followed by specialized deeper CNN regressors to quantify adulterant levels with mean squared errors of 0.0003, 0.001, and 0.0002 for glucose, maltose, and inverted sugar, respectively. This layered CNN approach leverages thermal patterns linked to adulteration, enabling sensitive, rapid, and non-destructive quality control. Furthermore, the method is integrated into a user-friendly hardware-software system called Compact Adulteration Testing Cabinet on Honey (CATCH), requiring no specialized expertise, demonstrating strong potential for automated honey authenticity verification in practical settings.
Citation - WoS: 5
Citation - Scopus: 5
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)
(Elsevier Ltd, 2023) Yadigar Seyfi Cankal; Mehmet Suleyman Ünlütürk; Sevcan Mehmet Unluturk; Cankal, Yadigar Seyfi; Unluturk, Mehmet S.; Unluturk, Sevcan
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 ∼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.