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dc.contributor.advisorBusch, Kenneth W.
dc.contributor.advisorBusch, Marianna A.
dc.contributor.authorIngle, Jemima Rose.
dc.contributor.otherBaylor University. Dept. of Chemistry and Biochemistry.en
dc.date.accessioned2006-10-13T16:32:15Z
dc.date.available2006-10-13T16:32:15Z
dc.date.copyright2006-08
dc.date.issued2006-10-13T16:32:15Z
dc.identifier.urihttp://hdl.handle.net/2104/4820
dc.descriptionIncludes bibliographical references (p. 199-204).en
dc.description.abstractThe enantiomeric composition of samples was determined using spectroscopy and multivariate regression modeling. Partial least-squares (PLS-1) regression models were developed from the spectral data of solutions containing both enantiomers in varying ratios. The developed regression models were used to predict the enantiomeric composition of unknown validation samples. The predictive ability of the models was evaluated in terms of the root mean square absolute error and the root mean square percent relative error. To address the issue of enantiomeric compositions higher than 0.9, a study was conducted using a large number of samples of phenylalanine and [beta]-cyclodextrin in the upper percentile range, varying from 90-100%. Validation studies with these samples gave absolute errors of 0.0217. In order to study the effects of varying the analyte concentration, two compounds were studied at five concentration levels. Three analyses were performed for each compound. One analysis used only the raw spectral data, one analysis included the concentration as a variable, and one analysis utilized the normalized spectra. Solutions of phenylalanine and [beta]-cyclodextrin resulted in a best absolute error of 0.0316 for the normalized spectral data. Solutions of norephedrine and [beta]-cyclodextrin resulted in a best absolute error of 0.0367 for the raw data. Finally, the spectral data can be used to predict the concentration, the predicted concentration used to normalize the data, and the new normalized data used to predict the enantiomeric composition with an absolute error of less than 0.06 for both compounds. Two simple sugars were tested for their use as chiral auxiliaries. Validation studies with fructose gave absolute errors of 0.0211 (2-octanol) and 0.0308 (phenylalanine); validation studies with glucose gave an absolute error of 0.0184. A comparison study between NIR and UV-visible spectral ranges yielded much poorer results in the NIR (absolute error 0.298) than in the UV-visible (absolute error 0.0308). Finally, a comparison study of 2-octanol and [alpha]-methylbenzylamine with and without a chiral auxiliary was completed. These results varied widely based on solvent and concentration. Modeling studies with impurities did not resemble the spectral behavior of real samples.en
dc.description.statementofresponsibilityby Jemima Rose Ingle.en
dc.format.extentxvii, 204 p. : ill.en
dc.format.extent2674309 bytes
dc.format.extent3340790 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en
dc.subjectChirality.en
dc.subjectEnantiameters -- Analysis.en
dc.subjectChemistry -- Statistical methods.en
dc.subjectRegression analysis.en
dc.titleStudies on regression modeling of spectral data as a means of chiral analysis.en
dc.typeThesisen
dc.description.degreePh.D.en
dc.rights.accessrightsWorldwide access.en
dc.rights.accessrightsAccess changed 5/25/11.
dc.contributor.departmentChemistry and Biochemistry.en


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