Master Degree / Yüksek Lisans Tezleri

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

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2008 - 200912010 - 20194

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Publisher: search.filters.publisher.Izmir Institute of TechnologySubject: search.filters.subject.Infrared spectroscopy

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Now showing 1 - 5 of 5
  • Master Thesis
    Estimation of Low Sucrose Concentrations and Classification of Bacteria Concentrations With Machine Learning on Spectroscopic Data
    (Izmir Institute of Technology, 2019) Mezgil, Bahadır; Baştanlar, Yalın; Baştanlar, Yalın
    Spectroscopy can be used to identify elements. In a similar way, there are recent studies that use optical spectroscopy to measure the material concentrations in chemical solutions. In this study, we employ machine learning techniques on collected ultraviolet-visible spectra to estimate the level of sucrose concentrations in solutions and to classify bacteria concentrations. Some metal nanoparticles are very sensitive to refraction index changes in the environment and this helps to detect small refraction index changes in the solution. In our study, gold nanoparticles are used and we benefited from this property to estimate sucrose concentrations. The samples in different low sucrose concentration solutions are obtained by mixing the sucrose measured with precision scales with pure water and then the UV-Vis spectrum of each sample is measured. For the bacteria concentration solutions, spectra for six different bacteria concentrations are captured. Spectra of the same solutions are also captured before adding the bacteria. For each of these solutions, four sets are prepared where gold nanoparticles are not grown (minute 0) and grown for 4 minutes, 10 minutes and 12 minutes. After the dataset preparation, these spectrum measurements are transferred into MATLAB environment as sucrose concentration dataset and bacteria solution dataset. Then the necessary preprocessing steps are performed in order to get the most informative and distinguishing information from these datasets. The raw measurement values and processed spectrum measurements are trained with shallow Artificial Neural Networks (ANN) on MATLAB Deep Learning Toolbox and Support Vector Machine (SVM) on MATLAB Statistics and Machine Learning Toolbox. When the results of the conducted machine learning experiments are examined, success rate is promising for the estimation of sucrose concentrations and very high for classification of bacteria concentrations in pure water solution.
  • 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.
  • 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, Durmuş
    Determination 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
    Varietal Classification and Prediction of Chemical Parameters of Turkish Wines by in Frared Spectroscopy
    (Izmir Institute of Technology, 2010) Öztürk, Burcu; Özen, Fatma Banu; Özen, Fatma Banu
    This study was performed with the aim of varietal classification of mono-varietal Turkish wines and development of models to predict basic enological parameters from mid-IR spectra with the use of chemometric methods. Mid-infrared (MIR) spectroscopy combined with multivariate data analysis was employed to make a varietal classification of commercial Turkish wines (Boğazkere, Cabarnet Sauvignon, Çalkarası, Kalecik Karası, Merlot, Öküzgözü, Papazkarası, Shiraz, Emir, Misket, Narince, Sultaniye and Chardonnay) from 2006 and 2007 vintages. Wine samples (n.79) including red, rose and white wines were scanned in the mid-IR region (4000-650 cm-1) and three spectral regions (965-1565 cm-1, 1700-1900 cm-1 and 2800-3040 cm-1) were used to classify wines on the basis of grape variety. The principal component analysis (PCA) was applied to the spectral data of the wine samples. Although a clear classification could not be achieved according to varieties, almost complete classification of red and white wines was observed. For the quantification analysis, a total of eleven enological parameters, including total phenol and anthocyanin content, pH, brix, titratable acidity, colour intensity (CI), tint, yellow%, red%, blue% and the proportion of red colour produced by anthocyanins (dA%) were determined with analytical reference methods. Correlation between the results of the reference methods and MIR spectral data was tested with partial least square (PLS) regression analysis and prediction models were developed with the use of these correlations. The calibration and validation sets were established to evaluate the predictive ability of the models. As a result of PLS analysis, the best models were developed for total phenols and CI with excellent predictions (R2.0.93 and 0.89, respectively and residual predictive deviation RPD.3.68 and 3.83, respectively). The model of pH determination and yellow% gave a good prediction (R2.0.85 and 0.85, respectively and RPD.2.7 and 2.04, respectively).