Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - Scopus: 1Adulteration of Pomegranate Molasses With Sugar Syrups: Application of FTIR-ATR Spectroscopy and Chemometrics(Elsevier, 2025) Kilinc, Gizem Simge; Uncu, Oguz; Eren, Ismail; Bagdatlioglu, NerimanIn 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: 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.Review Citation - WoS: 10Citation - Scopus: 10Authentication of Vinegars With Targeted and Non-Targeted Methods(Taylor & Francis, 2023) Çavdaroğlu, Çağrı; Çavdaroğlu, Çağrı; Özen, Banu; Özen, Fatma BanuThere has been a growing interest in vinegar, especially after the increasing reports about its beneficial health effects. Bioactive compounds of vinegar are associated with its antimicrobial, antioxidant, antidiabetic, antitumor, and anti-obesity types of activities. Quality of vinegar is related with the authenticity of the product besides the amounts of bioactive compounds in its composition. Addition of cheaper substitutes to higher quality vinegars and false labeling are some common authentication problems for this product. There are various examples of the use of targeted and untargeted methods in authentication studies for vinegars. Specific constituents and properties of vinegars such as molecular isotope ratios and individual volatile compounds were used to detect adulteration with targeted methods. On the other hand, untargeted methods, mostly in the form of the application of spectroscopic techniques, such as infrared and fluorescence spectroscopy in combination with chemometrics, provide an overall measurement. This review mainly focuses on adulteration types and elaborates on different targeted and non-targeted methods used to authenticate vinegars.Article Citation - WoS: 35Citation - Scopus: 43Potential of Fourier-Transform Infrared Spectroscopy in Adulteration Detection and Quality Assessment in Buffalo and Goat Milks(Elsevier, 2021) Şen, Sevval; Dündar, Zahide; Uncu, Oğuz; Özen, BanuAdulteration of higher priced milks with cheaper ones to obtain extra profit can be the cause of adverse health effects as well as economic loss. In this study, it was aimed to differentiate goat-cow and buffalo-cow milk mixtures and also to estimate the critical quality parameters of these milks by the evaluation of Fourier-transform infrared (FTIR) spectroscopic data with chemometric methods. Raw goat and buffalo milks were mixed with cow milk at 1-50% (v/v) concentrations and FTIR spectra of the pure and mixed samples were obtained at 4000-650 cm-1. Orthogonal partial least square discriminant analysis (OPLS-DA) resulted in differentiation of goat-cow and buffalo-cow milk mixtures with 93% and 91% correct classification rates, respectively. Detection level for mixing is determined as higher than 5% for both milks. Total fat, protein, lactose and non-fat solid contents were predicted from FTIR spectral data of the combination of three types of milks by partial least square models with R2 values of 0.99. As a result, FTIR spectroscopy provides rapid and simultaneous detection of adulteration and prediction of quality parameters regardless of the milk type.Article Citation - WoS: 25Citation - Scopus: 30Prediction of Chemical Parameters and Authentication of Various Cold Pressed Oils With Fluorescence and Mid-Infrared Spectroscopic Methods(Elsevier Ltd., 2021) Doğruer, Ilgın; Uyar, H. Hilal; Uncu, Oğuz; Özen, BanuIt was aimed to compare the performances of two spectroscopic methods, fluorescence and mid-infrared spectroscopy, in terms of their adulteration detection and estimation of several chemical properties for various cold pressed seed oils. Spectroscopic profiles, fatty acid, free fatty acid and total phenol contents of pumpkin seed, grape seed, black cumin oil, and sesame seed oils were determined and these oils were mixed with sunflower oil at 1–50% (v/v). Both spectroscopic techniques provided comparable results for determination of adulteration of each oil type and the most successful prediction was obtained for pumpkin seed oil at levels >%1. Combined data set of oils resulted in successful quantification of their free fatty acid value, total phenol and major fatty acids contents with both spectroscopic methods regardless of oil type. Both techniques could be used as reliable, fast and environmentally friendly alternatives in the analyses of different types of seed oils. © 2020 Elsevier LtdArticle Citation - WoS: 82Citation - Scopus: 103A Comparative Study of Mid-Infrared, Uv-Visible and Fluorescence Spectroscopy in Combination With Chemometrics for the Detection of Adulteration of Fresh Olive Oils With Old Olive Oils(Elsevier Ltd., 2019) Uncu, Oğuz; Uncu, Oğuz; Özen, Banu; Özen, Fatma BanuThe work aimed to detect and quantify adulteration of fresh olive oils with old olive oils from the previous harvest year by using different spectroscopic approaches in combination with chemometrics. Adulterated samples prepared in varying concentrations (10.50%(v/v)) were analyzed with fluorescence, Fourier transform-infrared (FT-IR), and ultraviolet-visible (UV-vis) spectroscopic methods. Orthogonal partial least square-discriminant analysis (OPLS-DA) and partial least squares (PLS) regression techniques were used for the differentiation of adulterated oils from the pure oils and prediction of adulteration levels, respectively. After the application of various pre-treatment methods, all of the OPLS-DA classification models generated for every spectroscopic technique successfully differentiated adulterated and non-adulterated oils with over 90% correct classification rate. FT-IR + UV-vis and fluorescence spectral data were also successfully used to predict adulteration levels with high coefficient of determinations for both calibration (0.94 and 0.98) and prediction (0.91 and 0.97) models and low error values for calibration (4.22% and 2.68%), and prediction (5.20% and 2.82%), compared to individual FT-IR and UV-vis spectroscopy were obtained. Therefore, FT-IR + UV-vis and fluorescence spectroscopy as being fast and environmentally friendly tools have great potential for both classification and quantification of adulteration practices involving old olive oil.Article Citation - WoS: 14Citation - Scopus: 18Evaluation of Three Spectroscopic Techniques in Determination of Adulteration of Cold Pressed Pomegranate Seed Oils(Elsevier Ltd., 2020) Uncu, Oğuz; Napiórkowska, Alicja; Szajna, Tomasz K.; Özen, BanuIt was aimed to compare three spectroscopic methods in determination of adulteration of cold pressed pomegranate seed oils (PSOs) with sunflower oil in this research. UV–visible, mid-infrared and fluorescence spectra of pure and adulterated pomegranate oils (1–50%, v/v) were collected and data were analyzed with multivariate statistical analysis techniques. According to orthogonal partial least square discriminant analysis, best differentiation between pure and mixed samples was obtained with mid-infrared spectroscopy having 100% success rate. Fluorescence and UV–visible spectroscopy also provided good discrimination between samples with 96 and 88% successful classification rates, respectively. As a result of partial least square regression analysis, detection limits for mid-infrared, UV–visible and fluorescence spectroscopies are determined as >1, 5 and 10% in order. Since all spectroscopic methods provided detection of mixtures of cold pressed PSOs with sunflower oil at low concentrations they could serve as easy to use and rapid techniques in control laboratories. © 2020 Elsevier B.V.