Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Biophysical Assessment of Protein Stability in Ethanol-Stressed Environments via UV Absorption and Fluorescence Spectroscopies
    (Elsevier, 2026) Akyuz, Ersed; Vorob'ev, Mikhail M.; Guler, Gunnur
    Maintaining the structure and functionality of proteins is crucial in applications ranging from food preservation to pharmaceutical formulation. Ethanol, while commonly used as a solvent and preservative, can induce structural changes in proteins depending on its concentration and the specific structure of the protein itself. This study investigates the structural effects of ethanol on three types of model proteins, namely bovine serum albumin (BSA), beta-Lactoglobulin (beta-Lg), and beta-Casein (beta-Cn), by using UV-Vis spectroscopy and fluorescence spectroscopy. The conformational responses of proteins in water-EtOH solutions of various ethanol concentrations (0-10 %, v/v) were analyzed through absorbance and emission spectral changes. At increasing ethanol concentration, UV absorption data showed distinct protein-dependent spectral changes. beta-Lg and beta-Cn exhibited strong hypochromism (an absorbance decrease of similar to 25 %) and red-shifting at 222 nm and 220 nm, respectively, indicating partial unfolding and solvent exposure of aromatic residues. BSA demonstrated subtle changes, and consistent quenching in fluorescence with a continuous blue-shifting to 330 nm, suggesting a moderate overall stability and local rearrangements in its structure. beta-Cn exhibited red-shifted fluorescence and quenching, reflecting its flexible, disordered structure and heterogeneous response to solvent conditions. Statistical analysis revealed that while fluorescence spectroscopy was highly sensitive to the intrinsic differences between proteins (p < 0.001), the ethanol-induced conformational changes were too subtle to be detected as a statistically significant treatment effect. The consistency of these trends indicates a rational underlying mechanism of interaction. This reflects the subtle nature of the effect at the tested concentrations rather than the absence of an effect. Moreover, these results unveil the protein-specific effects of ethanol and strongly emphasize the importance of solvent composition in maintaining protein integrity. Ethanol concentrations up to 5 % may offer protein stability whereas high ethanol levels (>= 5-10 %) promote structural perturbations. These results will be useful for both basic scientific research, such as biophysical studies and the advancement of optical techniques, and various industrial uses.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 8
    Comparative 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, Banu
    The 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.