Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Interaction of Hazelnut-Derived Polyphenols With Biodegradable Film Matrix: Structural, Barrier, and Functional Properties(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Hızır-Kadı, I.; Demircan, E.; Özçelik, B.The study presents a sustainable approach to valorizing hazelnut processing by-products, specifically skins and shells, through their conversion into bioactive polyphenol-rich extracts using pressurized hot water extraction (PHWE), an environmentally friendly green technology. PHWE yielded extracts with total phenolic contents of 25.4 mg GAE/g dw (shell) and 83.7 mg GAE/g dw (skin), which were incorporated into biodegradable poly(vinyl alcohol)/carboxymethyl cellulose (PVA/CMC) films at concentrations of 1–3% (w/v). The resulting composites were comprehensively characterized in terms of structural, mechanical, thermal, and barrier properties. FTIR, DSC, and XRD analyses demonstrated strong hydrogen bonding, increased thermal stability, and reduced crystallinity due to polyphenol–polymer interactions. Phenolic incorporation enhanced UV-blocking capability, increased antioxidant activity by up to five-fold, and reduced oxygen permeability from 0.048 to 0.015 (cm3·mm·m−2·day−1·atm−1) (69% reduction, p < 0.05), compared to neat PVA while maintaining desirable transparency (>70%). Optimal formulations (HSkE-II) exhibited a 39% increase in elongation at break and improved flexibility without compromising film integrity. Application tests using fresh-cut apples, watermelon, and chicken revealed significant reductions in microbial growth (up to ~1.2 log CFU/g), lipid oxidation, and weight loss during storage, confirming the films’ potential for active food packaging. This work highlights an efficient valorization strategy for nut industry by-products and demonstrates their functional integration into sustainable biodegradable packaging systems. © 2025 by the authors.Article Citation - Scopus: 20Estrus Detection and Dairy Cow Identification With Cascade Deep Learning for Augmented Reality-Ready Livestock Farming(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Arıkan, İ.; Ayav, T.; Seçkin, A.Ç.; Soygazi, F.Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming. © 2023 by the authors.