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: 2Fatty Acid Alkyl Ester and Wax Compositions of Olive Oils as Varietal Authentication Indicators(Springer, 2022) Uncu, O.; Ozen, B.Minor components of olive oils can be good markers for their authenticity, which is a significant quality issue for this product. It was aimed to determine individual and total fatty acid alkyl esters and waxes as minor constituents of olive oil and to investigate their novel varietal authentication capability separately and in combination for three main olive cultivars grown in three distinct locations of Aegean Region of Turkey. In addition, basic quality and purity parameters as free fatty acid, K values and fatty acid profiles were also determined for the characterization of the samples. Olive oil samples from different cultivars had different fatty acid profiles and two of these varieties had similar quality parameters. Statistical analyses were conducted with orthogonal partial least squares discriminant analysis (OPLS-DA) to differentiate varieties with respect to their individual and combined parameters of fatty acid alkyl esters and waxes. For calibration sets, use of individual fatty acid alkyl esters profile resulted in 80% correct classification rate while waxes alone was 67% successful in classifying the olive oils according to variety. It was found that alkyl esters in combination with waxes were more effective in discrimination of olive oils with respect to cultivar compared to their individual forms and the correct classification rate for the generated model is 92% for calibration set. Since fatty acid alkyl esters along with waxes have effect on cultivar differentiation, they could have a potential as authentication tools for olive oil besides their known quality characteristics. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Article Citation - WoS: 9Citation - Scopus: 8Quantitative Determination of Phenolic Compounds in Propolis Samples From the Black Sea Region (türkiye) Based on Hptlc Images Using Partial Least Squares and Genetic Inverse Least Squares Methods(Elsevier, 2023) Güzelmeriç, Etil; Özdemir, Durmuş; Şen, Nisa Beril; Çelik, Cansel; Yeşilada, ErdemThe complex chemical composition of propolis is related to the plant source to be used by honeybees. Propolis type is defined based on the plant source with the highest proportion in its composition, which is determined by chromatographic techniques as high-performance thin-layer chromatography (HPTLC). In addition to marker component identification to specify the propolis type, quantification of its proportion is also significant for prediction and reproducible pharmacological activity. One drawback for propolis marker component quantita-tion is that during the chromatographical analysis, not the main but the other plant sources with less proportion may cause interferences during the chemical analysis. In this study, the amounts of marker components were compared with the reference analysis data obtained by high-performance liquid chromatography (HPLC) and from HPTLC images using Partial Least Squares (PLS) and Genetic Inverse Least Squares (GILS) regression methods. Firstly, HPTLC images of propolis samples were processed by an image algorithm (developed in MATLAB) where the bands of each standard and the samples were cut same dimensional pieces as 351 x 26 pixels in height and width, respectively. Simultaneously, reference analysis of the marker components in propolis samples was performed with a validated HPLC method. Consequently, the reference values obtained from HPLC versus PLS, and GILS predicted values of the eight compounds based on the digitized HPTLC images of the chromatograms were found to be matched successfully. The results of the multivariate calibration models demonstrated that HPTLC images could be used quantitatively for quality control of propolis used as a food supplement.Article Citation - WoS: 5Citation - Scopus: 5Developing Predictive Equations for Water Capturing Performance and Sediment Release Efficiency for Coanda Intakes Using Artificial Intelligence Methods(MDPI, 2022) Hazar, Oğuz; Tayfur, Gökmen; Elçi, Şebnem; Singh, Vijay P.Estimation of withdrawal water and filtered sediment amounts are important to obtain maximum efficiency from an intake structure. The purpose of this study is to develop empirical equations to predict Water Capturing Performance (WCP) and Sediment Release Efficiency (SRE) for Coanda type intakes. These equations were developed using 216 sets of experimental data. Intakes were tested under six different slopes, six screens, and three water discharges. In SRE experiments, sediment concentration was kept constant. Dimensionless parameters were first developed and then subjected to multicollinearity analysis. Then, nonlinear equations were proposed whose exponents and coefficients were obtained using the Genetic Algorithm method. The equations were calibrated and validated with 70 and 30% of the data, respectively. The validation results revealed that the empirical equations produced low MAE and RMSE and high R2 values for both the WCP and the SRE. Results showed outperformance of the empirical equations against those of MNLR. Sensitivity analysis carried out by the ANNs revealed that the geometric parameters of the intake were comparably more sensitive than the flow characteristics.