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TitleApplication of multivariate regression techniques to paint: for the quantitive FTIR spectroscopic analysis of polymeric components
AuthorPhala, Adeela Colyne
SubjectProtective coatings
SubjectFourier transform infrared spectroscopy
SubjectFourier transform spectroscopy
SubjectInfrared spectroscopy
SubjectMultivariate analysis
SubjectRegression analysis
SubjectDissertations, Academic
SubjectPolymers and polymerization -- Analysis
SubjectPartial least squares (PLS)
SubjectPrinciple component regression (PCR)
SubjectTheses, dissertations, etc.
SubjectCape Peninsula University of Technology. Department of Chemistry
AbstractThesis submitted in fulfilment of the requirements for the degree Master of Technology Chemistry in the Faculty of (Science) Supervisor: Professor T.N. van der Walt Bellville campus Date submitted: October 2011
AbstractIt is important to quantify polymeric components in a coating because they greatly influence the performance of a coating. The difficulty associated with analysis of polymers by Fourier transform infrared (FTIR) analysis’s is that colinearities arise from similar or overlapping spectral features. A quantitative FTIR method with attenuated total reflectance coupled to multivariate/ chemometric analysis is presented. It allows for simultaneous quantification of 3 polymeric components; a rheology modifier, organic opacifier and styrene acrylic binder, with no prior extraction or separation from the paint. The factor based methods partial least squares (PLS) and principle component regression (PCR) permit colinearities by decomposing the spectral data into smaller matrices with principle scores and loading vectors. For model building spectral information from calibrators and validation samples at different analysis regions were incorporated. PCR and PLS were used to inspect the variation within the sample set. The PLS algorithms were found to predict the polymeric components the best. The concentrations of the polymeric components in a coating were predicted with the calibration model. Three PLS models each with different analysis regions yielded a coefficient of correlation R2 close to 1 for each of the components. The root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) was less than 5%. The best out-put was obtained where spectral features of water was included (Trial 3). The prediction residual values for the three models ranged from 2 to -2 and 10 to -10. The method allows paint samples to be analysed in pure form and opens many opportunities for other coating components to be analysed in the same way.
PublisherCape Peninsula University of Technology