Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

Browse

Filters

2025 - 20263

Settings

Access type: Metadata onlyAuthor: search.filters.author.Ozen, Banu

Search Results

Now showing 1 - 3 of 3
  • 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, Sukru
    Vibrational 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
    Citation - WoS: 1
    Legume and Nut Flours From the Mediterranean Area: Proximate Compositions, Techno-Functionalities, and Spectroscopy Patterns as a Function of Species, Origin, and Treatment
    (Elsevier, 2025) Cappa, Carola; Ozen, Banu; Tokatli, Figen; Imeneo, Valeria; Aguilo-Aguayo, Ingrid; Sahan, Yasemin; Alamprese, Cristina
    This study systematically evaluates proximate composition, color, techno-functionalities, and spectroscopy patterns of 19 legume and 16 nut commercial flours of the Mediterranean area. Effect of species, origin, and treatment were analyzed using a Generalized Linear Model. Except for legume proteins, species and origin significantly (p <= 0.001) affected flour composition, while heat treatment only had a significant effect (p <= 0.05) on nuts. A large variability was observed in fats (0.6-69 g/100g) and proteins (3.7-36 g/100g), and the FT-IR spectra reflected the 35-flours composition. Principal component model clearly distinguished flours based on their carbohydrate, fat, and protein contents. For legumes, color indices, bulk density, and emulsifying properties were significantly affected by species, origin, and treatment, while foaming properties were influenced only by species. For nuts, oil absorption capacity, emulsion properties, and foaming properties were significantly affected by species, origin, and treatment. The origin had a significant effect on water retention capacity (40-433 %) of nuts. The study findings contribute to a better knowledge of Mediterranean legume and nut flours, clarifying their distinct properties for a higher awareness in their use for the design of food products with tailored features.
  • 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.