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Multivariate data analysis using spectroscopic data of fluorocarbon alcohol mixtures / Nothnagel, C.

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dc.contributor.author Nothnagel, Carien en_US
dc.date.accessioned 2012-08-27T15:58:31Z
dc.date.available 2012-08-27T15:58:31Z
dc.date.issued 2012 en_US
dc.identifier.uri http://hdl.handle.net/10394/7064
dc.description Thesis (M.Sc. (Chemistry))--North-West University, Potchefstroom Campus, 2012.
dc.description.abstract Pelchem, a commercial subsidiary of Necsa (South African Nuclear Energy Corporation), produces a range of commercial fluorocarbon products while driving research and development initiatives to support the fluorine product portfolio. One such initiative is to develop improved analytical techniques to analyse product composition during development and to quality assure produce. Generally the C–F type products produced by Necsa are in a solution of anhydrous HF, and cannot be directly analyzed with traditional techniques without derivatisation. A technique such as vibrational spectroscopy, that can analyze these products directly without further preparation, will have a distinct advantage. However, spectra of mixtures of similar compounds are complex and not suitable for traditional quantitative regression analysis. Multivariate data analysis (MVA) can be used in such instances to exploit the complex nature of spectra to extract quantitative information on the composition of mixtures. A selection of fluorocarbon alcohols was made to act as representatives for fluorocarbon compounds. Experimental design theory was used to create a calibration range of mixtures of these compounds. Raman and infrared (NIR and ATR–IR) spectroscopy were used to generate spectral data of the mixtures and this data was analyzed with MVA techniques by the construction of regression and prediction models. Selected samples from the mixture range were chosen to test the predictive ability of the models. Analysis and regression models (PCR, PLS2 and PLS1) gave good model fits (R2 values larger than 0.9). Raman spectroscopy was the most efficient technique and gave a high prediction accuracy (at 10% accepted standard deviation), provided the minimum mass of a component exceeded 16% of the total sample. The infrared techniques also performed well in terms of fit and prediction. The NIR spectra were subjected to signal saturation as a result of using long path length sample cells. This was shown to be the main reason for the loss in efficiency of this technique compared to Raman and ATR–IR spectroscopy. It was shown that multivariate data analysis of spectroscopic data of the selected fluorocarbon compounds could be used to quantitatively analyse mixtures with the possibility of further optimization of the method. The study was a representative study indicating that the combination of MVA and spectroscopy can be used successfully in the quantitative analysis of other fluorocarbon compound mixtures. en_US
dc.publisher North-West University
dc.subject Chemometrics en_US
dc.subject Multivariate data analysis en_US
dc.subject Partial least squares regression en_US
dc.subject Principal component regression en_US
dc.subject Raman spectroscopy en_US
dc.subject Near infrared spectroscopy (NIR) en_US
dc.subject Attenuated total reflectance infrared spectroscopy (ATR-IR) en_US
dc.subject Fourier transform spectroscopy en_US
dc.subject Fluorocarbon alcohols en_US
dc.title Multivariate data analysis using spectroscopic data of fluorocarbon alcohol mixtures / Nothnagel, C. en_US
dc.type Thesis en_US
dc.description.thesistype Masters en_US


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    This collection contains the original digitized versions of research conducted at the North-West University (Potchefstroom Campus)

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