Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article FTIR Spectroscopy Coupled With Chemometrics for Evaluating Functional Food Efficacy in an in Vitro Model of Iron Deficiency Anemia(Elsevier Science Ltd, 2026) Dalyan, Eda; Cavdaroglu, Cagri; Ozen, Banu; Gulec, SukruVibrational spectroscopy offers a rapid, cost-effective approach for studying biological systems. This study employs Fourier Transform Infrared (FTIR) spectroscopy, combined with Soft Independent Modeling of Class Analogy (SIMCA), to evaluate treatment outcomes for iron deficiency anemia (IDA). The model was built using spectra from healthy and anemic cells, then validated with cells treated with commonly used iron supplements. In calibration, 9 of 10 control and all IDA samples were correctly classified; 14 of 15 validation samples were identified as healthy. The model was applied to cells treated with protein-iron complexes. All samples treated with a 60:1 protein-iron ratio matched the healthy group, while 3 of 4 treated with a 10:1 ratio matched the IDA group. These results were further supported by iron-regulated gene expression of transferrin receptor (TFR) and (Ankyrin Repeat Domain 37) ANKRD37. FTIR coupled with chemometrics enables rapid assessment of functional effects and shows potential for screening functional ingredients in anemia-targeted food products.Article A Knowledge-Driven Computer Vision Framework for Automated Atomic Force Microscopy Surface Characterization(Elsevier Science Ltd, 2026) Deveci, D. Gemici; Barandir, T. Karakoyun; Unverdi, O.; Celebi, C.This study presents an innovative analytical framework developed to automate Atomic Force Microscopy (AFM)-based surface characterization. The proposed methodology integrates computer vision (CV) algorithms and machine learning (ML) techniques to overcome the limitations of conventional observer-dependent approaches and visual inspection methods. In the first stage of the two-step data processing pipeline, raw AFM signals were converted into structured datasets, correspondences between images acquired under different loading conditions were identified, and drift effects in both direction and magnitude were predicted using a LightGBM-based machine learning (ML) model to guide subsequent analytical processes. This process establishes a unified coordinate reference across varying force levels, enabling pixel-level comparability of surface maps. In the second stage, the aligned datasets are systematically analyzed through block-based local maxima detection, edge-based contour extraction, morphological filtering, and skeletonization algorithms. In this way, ridge-like surface features are reliably identified and quantitatively evaluated along their axes under varying force conditions. The framework ensures data integrity while enabling high-resolution and reproducible analyzes. Beyond its automation capability, it is distinguished by its integrated, modular architecture, where each component operates sequentially along a unified processing pipeline. The methodology was validated using epitaxial monolayer graphene grown on the C-face of SiC, successfully demonstrating its ability to resolve both geometric and force-dependent mechanical responses. In this regard, the proposed system extends beyond conventional cross-sectional analysis by providing a drift-aware, knowledge-guided compensation mechanism and directionally resolved evaluation, offering a robust, automation-ready infrastructure for nanoscale surface characterization.Article Citation - Scopus: 105Moisture Sorption Isotherm Characteristics of Peppers(Elsevier Science Ltd, 2001) Kaymak-Ertekin,F.; Sultanoglu,M.Moisture sorption isotherms of green and red peppers were determined at three different temperatures (30 °C, 45 °C and 60 °C) and relative humidities (10-90%), using the standard static, gravimetric method. The GAB, Halsey, Oswin and BET sorption models were tested to fit the experimental data. A nonlinear regression analysis method was used to evaluate the constants of four sorption equations. The Halsey equation gave the best fit to the experimental sorption data for a wide range of water activity while BET gave the best fit for a water activity range of 0.1-0.5. The agreement between experimental and calculated values was found to be satisfactory. The isosteric heats of desorption and adsorption of water were determined from the equilibrium data at different temperatures using the Clasius-Clapeyron equation.