Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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  • Master Thesis
    In-Depth Investigation of the Effects of Different Preprocessing Strategies on Infrared Spectroscopic Data
    (01. Izmir Institute of Technology, 2021) Deniz, Elin İlayda; Özdemir, Durmuş
    Whenever collecting experimental data, they may contain several spurious sources of variability which can hinder the extraction of the desired relevant information so that it is rarely the case that they can be processed as such by chemometrics approaches. In recent years, pre-processing techniques has become an integral part of chemometrics modeling with the purpose of providing better endmodels through fundamental knowledge for Near-Infrared (NIR) users. The aim of pre-processing techniques is to improve success of multivariate regression by reducing undesired physical phenomena in the spectra. This thesis describes the theory of present pre-processing techniques and compares the qualitative and quantitative results of their application. Mean centering, scatter-correction methods and spectral derivatives are the instances of those pre-processing techniques used in this thesis. Those techniques and combinations of them have been applied in order to find the best pre-processing strategy. To be able to observe the results and compare the effects of the applied methods, Partial Least Squares and Genetic Inverse Laast Squares are carried out as multivariate calibration methods. When comparing the calibration results of raw data with pre-processing techniques applied data, decreasement standard eror of prediction (SEP) values observed after appliying those techniques, which is good manner. However, better in comparison, t-Test: Paired Two Sample for Means applied. The results demontrates that there is no significant difference within 95% confidence level.
  • Master Thesis
    Development of Chemometrics Method Based on Infrared Spectroscopy for the Determination of Cement Composition and Process Optimization [master Thesis]
    (01. Izmir Institute of Technology, 2021) Tepeli, Dilek; Özdemir, Durmuş
    Calcium, silicon, aluminum, iron oxide-containing raw materials are used in controlled portions to manufacture cement. (How cement Is Made, n.d.) This mixture is first converted to clinker, obtained by heating the mixture to 1500oC; some additives are added and ground to obtain cement. Depending on the purpose, various types are produced, and therefore, the determination of cement composition is an essential task for the quality consideration and the sustainability of the production processes. The quantitative analysis of cement is performed with X-ray fluorescence spectroscopy. However, XRF generally requires tedious and lengthy analysis times. In this study, quantitative determination of the raw materials, intermediate products, and types of cement by using Fourier transform infrared spectroscopy coupled with chemometrics multivariate calibration method is aimed, which could be an alternative for the current XRF technique. Samples were collected from a local cement factory that has been in the sector for several years. Reference analyses of the samples were performed at the quality control laboratory of the same factory. The same samples were analyzed by the FTIR-ATR spectrometer. The resulting FTIR spectra combined with XRF reference composition data were used to construct calibration models using the partial least squares method (PLS). Based on the obtained results, the proposed method could generate quite successful results for the quantitative determination of all types of products used to produce cement. The regression coefficients (R2) of the PLS models vary from 0.95 to 0.99. The standard errors of cross-validations were found as from 0.21 to 1.42 (w/w%).
  • Master Thesis
    Determination of Crucial Parameters in Gasoline Blends by Using Infrared Spectroscopy Coupled With Multivariate Calibration Methods
    (01. Izmir Institute of Technology, 2021) Sakallı, Fatma Nur; Özdemir, Durmuş
    In petroleum refineries, converting the manual gasoline blending system to an automatic inline blending system provides the most economical blending in gasoline production, increasing efficiency, and reliability. The most important requirement for an automatic inline blending system is the determination of gasoline parameters in a short time with high reliability. For this purpose, fast and simple analytical methods have been developed to determine crucial parameters of gasoline blends by using Fourier Transform Infrared Spectroscopy (FTIR) coupled with multivariate calibration methods which are Partial Least Squares Regression (PLSR) and Genetic Inverse Least Squares Regression (GILS) for this study. Turkey Petroleum Refinery Incorporated Company (TUPRAS) Izmir Refinery collected all gasoline samples and tested them using reference test methods at Quality Control Laboratory. Since commercial product samples were used in this study, the data ranges of the parameters were quite narrow. The Standard Error of Cross-Validation (SECV) and Standard Error of Prediction (SEP) values were acceptable, although the determintion coefficient (R2) value of some parameters was below the expectation. It has been observed that the prediction results of GILS are better in these parameters, whose R2 value is low because the data range is very narrow. In the comparison made with the reproducibility values specified in the reference measurement methods, it was determined that the calibration model results of most parameters were acceptable. Collecting more samples in a longer time interval to expand the data range of the parameters, or preparing a data set with experimental design can improve the prediction performance.
  • Master Thesis
    The Development of Chemometric Methods Based on Molecular Spectroscopy for the Standardization of Production Processes and Product Traceability of Personal Care and Cleaning Products
    (Izmir Institute of Technology, 2019) Çiftçi İlmek, Berfu; Özdemir, Durmuş
    Personal care and cleaning products are the main consumer goods. Changes in our heath caused by all of the chemicals that we exposed to everyday if these products are not produced according to the regulations and determined formulations. Because of this reason, quality control of the product formulation quantitatively is very important. There are some analytical methods for the determination of anion active matter, nonionic matter and total active matter in the product mixture. However, these techniques are expensive and do not give accurate results. The purpose of this thesis principally based on development of rapid, accurate and practical infrared spectroscopic technique based on multivariate chemometrics data analysis methods for the standardization of production processes and product traceability of personal care and cleaning products. In this thesis, two different products are studied which are namely liquid soap and shower gel. Fourier Transform Infrared spectroscopy coupled with Attenuated Total Reflectance accessory based chemometrics multivariate calibration models were developed for the quantitative determination of liquid soap and shower gel compounds. Genetic Inverse Least Squares was used as the chemometrics method for the development of multivariate calibration models in the quantitative determination of liquid soap and shower gel compositions. Standard error of cross validation and standard error of prediction values for content of the liquid soap samples were found 0.26% and 0.21 % (w/w %), respectively. Standard error of cross validation and standard error of prediction values for content of the shower gel samples were found 0.27 % and 0.30 % (w/w %), respectively.
  • Master Thesis
    Development of a New Infrared Spectroscopic Method Based on Multivariate Calibration for the Determination of Aluminum and Magnesium Oxid Thickness on Aluminum Foil and Sheets Surfaces
    (İzmir Institute of Technology, 2016) Meşe, Ayten Ekin; Özdemir, Durmuş
    Surface oxidation is a general problem for certain industrial applications such as coating and painting of the finished rolled products. A detailed understanding for the oxide growth mechanism as well as the development of a simple analytical method to measure this oxide thickness is very important in aluminum rolling industry and this study aims to develop a spectroscopic method to determine the oxide thicknesses on the surface of the aluminum by using multivariate calibration and infrared spectroscopy. Two main series of different aluminum alloys (3005 and 3003BZ) were selected in this study to develop a proposed methodology which is based on the combination of Fourier Transform Infrared Spectroscopy (FTIR) with Grazing Angle ATR accessory and chemometrics multivariate calibration techniques. In order to obtain oxide thickness values, X-ray Photoelectron Spectroscopy (XPS) was used and aluminum oxide (Al2O3) and magnesium oxide (MgO) thicknesses determinations were carried out by two different multivariate calibration models which are Genetic Inverse Least Squares (GILS) and Genetic Partial Least Squares (GPLS). These models were able to predict Al2O3 and MgO thicknesses using FTIR that is faster, easier and cheaper to operate as well as from XPS. The correlation coefficients of XPS reference oxide thickness values versus FTIR-GATR based GILS and GPLS predicted values were better than 0.919 in range of 0 to 25 nanometers for Al2O3 and 0 to 35 nm for MgO. These results suggest that grazing angle FTIR-ATR spectroscopy may offer a simple and nondestructive alternative for quick determination of oxide layer thickness.