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.Article Citation - WoS: 9Citation - Scopus: 11Authentication of Pomegranate Juice in Binary and Ternary Mixtures With Spectroscopic Methods(Elsevier, 2023) Aykaç, Başak; Çavdaroğlu, Çağrı; Özen, BanuFruit juices are among the most commonly adulterated food products and especially pomegranate juice as a high value product is mixed with different adulterants for unfair economic profit. It was aimed to investigate the performances of UV–visible and Fourier-transform infrared (FTIR) spectroscopies combined with chemometric methods to determine adulteration of pomegranate juice with dark colored sour cherry and black carrot juices. Binary and ternary mixtures of pomegranate juice with 2 adulterants were prepared at 5–25% levels. After various data transformations, both spectroscopic data of authentic and adulterated samples were evaluated with different chemometric classification tools. Classification models with 97% correct classification rate for validation set were obtained both for UV–visible and FTIR spectral data. Accurate predictions of adulterant concentration were also achieved with chemometric models using both spectroscopic data. These spectroscopic techniques provide rapid and accurate prediction of pomegranate juice adulteration in binary and ternary mixtures with dark colored juices.Article Citation - WoS: 28Citation - Scopus: 29Detection of Vinegar Adulteration With Spirit Vinegar and Acetic Acid Using Uv–visible and Fourier Transform Infrared Spectroscopy(Elsevier, 2022) Çavdaroğlu, Çağrı; Özen, BanuVinegar is one of the commonly adulterated food products, and variations in product and adulterant spectrum make the detection of adulteration a challenging task. This study aims to determine adulteration of grape vinegars with spirit vinegar and synthetic acetic acid using different spectroscopic methods. For this purpose, grape vinegars were mixed separately with spirit vinegar and diluted synthetic acetic acid (4%) at 1–50% (v/v) ratios. Spectra of vinegars and mixtures were obtained with UV–visible and Fourier-transform infrared (FTIR) spectrometers. Data were evaluated with various chemometric methods and artificial neural networks (ANN). Correct classification rates of at least 94.3% and higher values were obtained by the evaluation of both spectroscopic data along with their combination with chemometric methods and ANN for discrimination of non-adulterated and adulterated vinegars. UV–vis and FTIR spectroscopy can be rapid and accurate ways of detecting adulteration in vinegars regardless of adulterant type.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.