Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Development of Biosensors for Determination of the Total Antioxidant Capacity(Izmir Institute of Technology, 2008) Çoban, Seçil; Bayraktar, OğuzIn this study, an amperometric laccase biosensor was developed for determination of the oleuropein concentration that is the biological active component of olive leaf and contributes dominantly to the total antioxidant capacity. The biosensor was prepared by immobilization of laccase from Trametes versicolor by addition of cross-linking agent, glutaraldehyde, into the carbon paste electrode. Different biosensors were prepared by changing the amount of crosslinking agent and concentration of the enzyme solution. So, effect of these parameters on biosensor performance was investigated. The best biosensor performance was determined for the biosensor having glutaraldehyde amount of 12.03 % vol. of the biosensor bottom part and 5 mg/ml of laccase enzyme. The effect of scan rate and temperature on the biosensor performance was also investigated in this study. The scan rate of 10 mV/s was decided to be the optimum for the amperometric detection of oleuropein considering the fastest response and maximum reduction current. 250C was chosen as an optimum temperature value due to the maximum laccase activity and capability of oleuropein acting as an antioxidant. Extraction of phenolics from olive leaf was also an important part of this study. The extract was divided into fractions varying in their oleuropein amounts such as polar fractions and relatively less polar fractions. Therefore, biosensor performance was investigated for fractions containing different type of phenolics. HPLC analyses of the fractions were also performed in this study. In addition total phenol content and antioxidant capacity of the fractions were determined by conventional methods.Master Thesis Prediction of Extractives and Lignin Contents of Anatolian Black Pine (pinus Nigra Arnold. Var Pallasiana) and Turkish Pine (pnus Brutia Ten.) Trees Using Infrared Spectroscopy and Multivariate Calibration(Izmir Institute of Technology, 2008) Karaman, İbrahim; Özdemir, Durmuş; Özdemir, DurmuşDetermination of quality parameters such as extractives and lignin contents of wood by wet chemistry analyses takes long time. Near-infrared (NIR) and mid-infrared (MIR) spectroscopy coupled with multivariate calibration offer fast and nondestructive alternative to obtain reliable results. However, due to complexity of multi-wavelength spectra, wavelength selection is generally required. Turkish pine and Anatolian black pine are the most growing pine species in Turkey. Forest products industry has widely accepted use of these trees because of their ability to grow on a wide range of sites and their suitability to produce desirable products. Determination of extractives and lignin contents of wood provides information to tree breeders when to cut and on how much chemical is needed in pulping and bleaching process. In this study, 58 samples of Turkish pine and 51 samples of Anatolian black pine were collected to investigate the correlation between NIR and MIR spectra of these samples and their extractives and lignin contents which were determined with reference methods. Genetic inverse least squares (GILS) was used for multivariate calibration. Standard error of calibration (SEC) values were less than 1.86% (w/w) for lignin and 1.19% (w/w) for extractives whereas standard error of prediction (SEP) values were less than 3.81% (w/w) for lignin and 2.04% (w/w) for extractives. Resulting R2 values for calibrations were larger than 0.8. Classification for Turkish pine and Anatolian black pine samples was performed by genetic algorithm based principal component analysis (GAPCA) and these two pine species were classified by using NIR and MIR spectra.Master Thesis Spectroscopic Determination of Industrial Oil Blends Using Multivariate Calibraton(Izmir Institute of Technology, 2009) Yalçın, Ayşegül; Özdemir, DurmuşThis study focuses on the development of multivariate calibration models for the aluminum rolling oil additives and contaminants using Fourier Transform Infrared (FTIR) spectroscopy and a genetic algorithm based inverse least squares (GILS) method. Multivariate calibration models were generated for both synthetic mixtures and real process samples taken from an industrial aluminum production plant. Two different additives and six different suspected contaminants were investigated in the base oil lubricant. Gas chromatography (GC) was used for the analysis of real process samples in order to establish reference values of additives and contaminants in the base rolling oil. FTIR spectra of real samples together with the reference values established with GC analysis were used to generate multivariate calibration models. GC analysis revealed that most of the contaminants gave overlapped chromatograms and therefore only the total contamination was determined with reference GC analysis. On the other hand, FTIR spectroscopy coupled with multivariate calibration was able to resolve overlapping components with synthetic samples. The reference values for both additives and contaminants obtained by GC were compared with the results of the spectroscopic analysis. The multivariate calibration models based on spectroscopic data validated with the real process samples in a period of twelve months, however only a set of 3-month data is given in this thesis. The R2 values between GC and multivariate spectroscopic determinations were around 0.99 indicating a good correlation between the two methods.