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 Harnessing Pulse Proteins as Soy Protein Substitutes in Spreadable Cheese Analogues: Exploring Correlations Among Protein Techno-Functionality, and Cheese Textural, Rheological and Sensory Properties(Elsevier Sci Ltd, 2026) Cavdaroglu, Elif; Topcuoglu, Meryem; Acar, Eda; Polat, Nil Yaren; Berk, Berkay; Cavdaroglu, Cagri; Yemenicioglu, AhmetThis study aimed at comparing techno-functional properties of faba bean (FBP), pinto bean (PBP), cowpea (CPP) and mung bean (MBP) proteins with commercial soy protein isolate (SPI), and exploring their correlations with textural, rheological and sensory properties of plant protein-based spreadable cheese analogues. FBP and MBP showed the best solubility between pH 3.0 and 11.0. The highest and the lowest water and oil (OAC) absorption capacities were observed for SPI and MBP (7.78 and 0.79 g H2O/g), and PBP and SPI (7.79 and 3.55 g oil/g). Protein's least gelling concentrations (LGC) ranged from 10 % (SPI) to 18 % (FBP). CPP, MBP, and PBP formed harder, gummier gels at >= LGC than SPI and FBP. Pre-gels of PBP and CPP at <= LGCs showed the highest consistency and viscosity indexes. Proteins showed similar emulsification. Cheese analogue from SPI (SPIC) showed the highest firmness (37.5 N) and work to shear for spreadability (57.5 N s), followed by cheese analogues of other proteins such as MBPC, CPPC, FBPC, and PBPC in descending order. The highest and lowest elastic (G ') and viscous (G '') moduli were obtained for MBPC (G' = 4353 and G"= 1277) and PBPC (G' = 377 Pa and G"= 98 Pa). OAC of proteins correlated with cheese analogues' firmness (r =- 0.918), work to shear for spreadability (r =- 0.910), and stickiness (r =- 0.894). Tan delta (G"/G ') of cheese analogues correlated with work to shear for spreadability (r = 0.986). SPIC and FBPC received the highest overall liking scores correlated mainly with appearance, color and taste. Correlating protein techno-functionality in cheese analogue opens new horizons for effective utilization of pulse proteins as soy protein substitutes.Article Citation - WoS: 8Citation - Scopus: 8Comparative Performance of Artificial Neural Networks and Support Vector Machines in Detecting Adulteration of Apple Juice Concentrate Using Spectroscopy and Time Domain Nmr☆(Elsevier, 2025) Cavdaroglu, Cagri; Altug, Nur; Serpen, Arda; Oztop, Mecit Halil; Ozen, BanuThe detection of adulteration in apple juice concentrate is critical for ensuring product authenticity and consumer safety. This study evaluates the effectiveness of artificial neural networks (ANN) and support vector machines (SVM) in analyzing spectroscopic data to detect adulteration in apple juice concentrate. Four techniques-UV-visible, fluorescence, near-infrared (NIR) spectroscopy, and time domain 1H nuclear magnetic resonance relaxometry (1H NMR)-were used to generate data from both authentic and adulterated apple juice samples. Adulterants included glucose syrup, fructose syrup, grape concentrate, and date concentrate. The spectroscopic data were pre-processed and analyzed using ANN and SVM models, with performance metrics such as sensitivity, specificity, and correct classification rates (CCR) evaluated for both calibration and validation sets. Results indicated that NIR spectroscopy combined with SVM provided the highest overall accuracy, with nearperfect specificity and high CCR values, making it the most robust method for adulteration detection. UV-visible and fluorescence spectroscopy also demonstrated strong performance but were slightly less consistent across different adulterants. 1H NMR relaxometry, while providing detailed molecular insights, showed variable sensitivity depending on the adulterant type. The findings showed the importance of selecting appropriate analytical techniques and machine learning models for food authentication. This study contributes to the development of non-destructive, rapid, and accurate methods for detecting food adulteration, which can help support industry efforts to enhance product integrity and maintain consumer trust.