Food Engineering / Gıda Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/12
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Article Citation - WoS: 12Citation - Scopus: 12Mid-Infrared Spectroscopic Detection of Sunflower Oil Adulteration With Safflower Oil(CSIC Consejo Superior de Investigaciones Cientificas, 2019) Uncu, Oğuz; Özen, Banu; Tokatlı, FigenThe oil industry is in need of rapid analysis techniques to differentiate mixtures of safflower-sunflower oils from pure oils. The current adulteration detection methods are generally cumbersome and detection limits are questionable. The aim of this study was to test the capability of a mid-infrared spectroscopic method to detect the adulteration of sunflower oil with safflower oil compared to fatty acid analysis. Mid-infrared spectra of pure oils and their mixtures at the 10-60% range were obtained at 4000-650 cm(-1) wavenumber and fatty acid profiles were determined. Data were analyzed by multivariate statistical analysis techniques. The lowest level of detection was obtained with mid-infrared spectroscopy at 30% while the fatty acid profile could determine adulteration at around 60%. Adulteration levels were predicted successfully using PLS regression analysis of infrared data with R-2 (calibration) = 0.96 and R-2 (validation) = 0.93. As a rapid and minimum waste generating technique, mid-infrared spectroscopy could be a useful tool for the screening of raw material to detect safflower-sunflower oil mixtures.Article Citation - WoS: 30Citation - Scopus: 34Authentication of a Turkish Traditional Aniseed Flavoured Distilled Spirit, Raki(Elsevier Ltd., 2013) Yüceesoy, Dila; Özen, BanuConsumption of traditional aniseed alcoholic beverage, raki, adulterated with methanol results in deaths, therefore, its detection is an important issue. In this study, mid-infrared spectra of pure and methanol adulterated (0.5-10% (vol/vol)) raki samples were collected with an attenuated total reflectance attachment of a Fourier-transform infrared spectrometer. Principal component analysis was used to discriminate pure and adulterated raki samples, then, a partial least square model was constructed to determine the adulterant methanol content in raki using mid-IR spectral data. A minimum threshold level of 0.5% methanol in raki samples was successfully detected. A good prediction model for determination of methanol adulteration ratio in raki samples was also constructed (R2 = 0.98 and RPD = 8.35).Article Citation - WoS: 231Citation - Scopus: 264Detection of Adulteration of Extra-Virgin Olive Oil by Chemometric Analysis of Mid-Infrared Spectral Data(Elsevier Ltd., 2009) Gürdeniz, Gözde; Özen, Fatma BanuThis study focuses on the detection and quantification of extra-virgin olive oil adulteration with different edible oils using mid-infrared (IR) spectroscopy with chemometrics. Mid-IR spectra were manipulated with wavelet compression previous to principal component analysis (PCA). Detection limit of adulteration was determined as 5% for corn-sunflower binary mixture, cottonseed and rapeseed oils. For quantification of adulteration, mid-IR spectral data were manipulated with orthogonal signal correction (OSC) and wavelet compression before partial least square (PLS) analysis. The results revealed that models predict the adulterants, corn-sunflower binary mixture, cottonseed and rapeseed oils, in olive oil with error limits of 1.04, 1.4 and 1.32, respectively. Furthermore, the data were analysed with a general PCA model and PLS discriminant analysis (PLS-DA) to observe the efficiency of the model to detect adulteration regardless of the type of adulterant oil. In this case, detection limit for adulteration is determined as 10%.Article Citation - WoS: 120Citation - Scopus: 135Authentication of Pomegranate Juice Concentrate Using Ftir Spectroscopy and Chemometrics(Elsevier Ltd., 2008) Vardin, Hasan; Tay, Abdullatif; Özen, Fatma Banu; Mauer, LisaFourier transform infrared (FTIR) spectroscopy and chemometric techniques were used to detect the adulteration of pomegranate juice concentrate (PJC) with grape juice concentrate (GJC). The main differences between PJC and GJC infrared spectra occurred in the 1780-1685 cm-1 region, which corresponds to C{double bond, long}O stretching. Principal component analysis of the spectra was used to: (1) differentiate pure PJC and GJC samples and (2) classify adulterated (containing 2-14% vol/vol GJC) and pure PJC samples. Two principal components explained 99% of the variability in each of these applications. Partial least square analysis of the spectra resulted in prediction of the GJC adulterant concentration in PJC with a correlation coefficient, R2, of 0.9751. Partial least square analysis of spectra could also predict % titratable acidity and total solids in PJC with correlation coefficients of 0.9114 and 0.9916, respectively. Therefore, FTIR and chemometrics provide a useful approach for authenticating pomegranate juice concentrate.Article Citation - WoS: 68Citation - Scopus: 76Differentiation of Mixtures of Monovarietal Olive Oils by Mid-Infrared Spectroscopy and Chemometrics(John Wiley and Sons Inc., 2007) Gürdeniz, Gözde; Tokatlı, Figen; Özen, Fatma BanuFourier transform infrared (FT-IR) spectroscopy in combination with chemometric techniques has become a useful tool for authenticity determination of extra-virgin olive oils. Spectroscopic analysis of monovarietal extra-virgin olive oils obtained from three different olive cultivars (Erkence, Ayvalik and Nizip) and mixtures (Erkence-Nizip and Ayvalik-Nizip) of monovarietal olive oils was performed with an FT-IR spectrometer equipped with a ZnSe attenuated total reflection sample accessory and a deuterated tri-glycine sulfate detector. Using spectral data, principal component analysis successfully classified each cultivar and differentiated the mixtures from pure mono-varietal oils. Quantification of two different monovarietal oil mixtures (2-20%) is achieved using partial least square (PLS) regression models. Correlation coefficients (R2) of the proposed PLS regression models are 0.94 and 0.96 for the Erkence-Nizip and Ayvalik-Nizip mixtures, respectively. Cross-validation was applied to check the goodness of fit for the PLS regression models, and R 2 of the cross-validation was determined as 0.84 and 0.91, respectively, for the two mixtures.