Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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Department: search.filters.department.İzmir Institute of TechnologySubject: search.filters.subject.Adulteration

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  • Article
    Citation - Scopus: 1
    Adulteration of Pomegranate Molasses With Sugar Syrups: Application of FTIR-ATR Spectroscopy and Chemometrics
    (Elsevier, 2025) Kilinc, Gizem Simge; Uncu, Oguz; Eren, Ismail; Bagdatlioglu, Neriman
    In this study, it was aimed to determine the adulteration ratio of pomegranate molasses (PM) with sugar syrups by using FTIR spectroscopy based upon chemometrics. With this intention, 34 pure PM samples were supplied from local manufacturers and adulterated with high fructose corn syrup (HFCS), glucose-fructose syrup (GFS) and beet sugar syrup (BSS) in varying ratios (5-50 %, w/w). Authentic and adulterated PM samples were analyzed in the range of 4000 and 400 cm(-1) wavenumber by FTIR spectroscopy. PCA was applied as a pretreatment for classification and regression analysis to select the spectral region and data reduction. Whereby the DD-SIMCA models were created using this information. The adulterated and authentic samples were classified correctly by the developed DD-SIMCA models. In the calibration and prediction model of DD-SIMCA, authentic and adulterated PM samples were correctly classified with high sensitivity (>= 0.91) and specificity (>= 0.94), and a clear distinction was observed with high efficiency (>= 0.94). Adulteration rates in PM samples were determined by PLS-R analysis. The correlation coefficients (R-2 >= 0.98) of models were also found quite high. As a consequence, FTIR spectroscopy in conjunction with chemometric approaches could be applied as a quick, dependable, non-destructive, and environmentally friendly tool for categorizing, distinguishing, and quantifying adulteration rates in PM samples.
  • 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.