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    A Chemometric Approach to Assess the Frying Stability of Cottonseed Oil Blends During Deep-Frying Process: I. Polar and Polymeric Compound Analyses
    (Wiley, 2013) Arslan, Fatma Nur; Kara, Huseyin; Ayyildiz, Hamide Filiz; Topkafa, Mustafa; Tarhan, Ismail; Kenar, Adnan
    The main goal of the present study was (i) to determine the formation of degradation products in cottonseed oil (CSO) blends during deep frying process by adsorption and high performance size exclusion chromatography techniques and (ii) to evaluate the impacts of food additives on total polar (TPC) and polymeric compound (PTAG) formation using a chemometric approach. In order to prepare the frying CSO blends; ascorbic palmitate, mixed tocopherols, dimethylpolysiloxane, lecithin and sesame oils were used as additives. To determine the real impacts of additives, a quarter-fraction factorial experimental design with two levels and five factors was used. The changes in TPC and PTAG data were carefully evaluated during 10 h of frying at 170 +/- A 5 A degrees C with normal distribution (ND) graphs and analyzed using a one-way analysis of variance (ANOVA), followed by Tukey's Post-hoc test (alpha = 0.05). The results indicated that the increasing values for TPC and PTAG during the frying processes for all blends, TPC and PTAG contents reached maximum levels of 16.37 and 6.01 % respectively, which are below the limit values stated by official authorities for the quality assessment of frying oils. The ANOVA test results were in good agreement with ND graphs and data indicated that the impact of mixed tocopherols was significant for TPC formation, meanwhile the impact of lecithin and ascorbic palmitate x dimethylpolysiloxane were significant for PTAG formation. Thus, the present study should be considered to be a very useful guide for developing new frying oil formulations based on CSO by using food additives.
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    A chemometric study: Automated flow injection analysis method for the quantitative determination of humic acid in Ilgm lignite
    (Elsevier Science Bv, 2016) Tarhan, Ismail; Kara, Huseyin
    A rapid, sensitive and provident flow injection analysis (FIA) method was developed within the framework of a chemometric approach for the quantification of humic acid (HA) in the lignite obtained from Ilgm, Konya, Turkey. The proposed method allows automatic determination of 60 samples per hour over a wide calibration range (0-2000 mg L-1, R-2: 0.9988) and needs only 10 mu L of sample at a flow rate of mobile phase (X-1), 2 mL min(-1); pH of mobile phase (X-2), 8, and system temperature (X-3), 20 degrees C. The limits of detection (LOD) and quantification (LOQ) were calculated as 9.18 mg L-1 and 30.60 mg L-1, respectively, and the relative standard deviation (RSD) for 500 mg L-1 HA was calculated as 3.44 (n: 9). It was revealed that the standard deviation (SD) values of the proposed FIA method are lower than those of the spectrophotometric method. (C) 2014 King Saud University. Production and hosting by Elsevier B.V.
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    Rapid determination of adulteration of clove essential oil with benzyl alcohol and ethyl acetate: Towards quality control analysis by FTIR with chemometrics
    (Elsevier, 2022) Tarhan, Ismail; Bakir, Muhammed Rasit; Kalkan, Oktay; Yontem, Mustafa; Kara, Huseyin
    In this study, Fourier transform infrared (FTIR) spectroscopy in tandem with chemometrics was used for the discrimination and quantification of the adulterants, such as benzyl alcohol (B) and ethyl acetate (E) in clove essential oil (CO). Different multivariate models with various spectral derivatization methods were developed and their analysis abilities the adulterants were compared using statistical quality parameters. To discriminate the adulterations thanks to the FTIR data, 130 chemometric models were built by principal component analysis (PCA) algorithm. The statistical performances of the PCA models developed were evaluated by the number of samples outside of explained variance (95 %) and eigen value. To quantify the adulterant concentrations in CO samples, 117 partial least squares (PLS) regression models employing the FTIR data were developed. To find out the best PLS model, the root mean square error of prediction (RMSEP), and root mean square error of cross-validation (RMSECV) were mainly used. Root mean square error of calibration (RMSEC) and R-square were also evaluated. The best discrimination results were achieved using 1st derivative spectra in the region 3500-3100 cm(-1) (SRB1) and 2nd derivative spectra in the region 1077-1008 cm(-1) (SRE3) for the adulterants of B and E, respectively. The best PLS calibration results were obtained from the combinations of the normal spectra in the regions 3500-3100 cm(-1), 1027-993 cm(-1), and 756-569 cm(-1) (SRBC) and SRE3 for the quantification of B and E, respectively. The results of the study indicated that FTIR with chemometrics could be used for simultaneously discrimination and quantification of the adulterants of B and E in COs without using any toxic chemicals or pretreatments.