In this thesis, we applied both numerical methods and artificial intelligence techniques to NMR data processing and analysis. First, a comprehensive study of the Iterative Quadratic Maximum Likelihood (IQML) method applied to NMR spectral parameter estimation is reported. The IQML is compared to other conventional time domain data analysis methods. Extensive simulations demonstrate the superior performance of the IQML method. We also develop a new technique, which uses genetic algorithm with a priori knowledge, to improve the quantification of NMR spectral parameters. The new proposed method outperforms the other conventional methods, especially in the situations that there are signals close in frequencies and the signal-to-noise ratio of the FID is low. / The usefulness of Singular Value Decomposition (SVD) method in NMR data processing is further exploited. A new two dimensional spectral processing scheme based on SVD is proposed for suppressing strong diagonal peaks. The superior performance of this method is demonstrated on an experimental phase-sensitive COSY spectrum. / Finally, we studied the feasibility of using neural network predicted secondary structure information in the NMR data analysis. Protein chemical shift databases are compiled and are used with the neural network predicted protein secondary structure information to improve the accuracy of protein chemical shift prediction. The potential of this strategy for amino acid classification in NMR resonance assignment is explored.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.35864 |
| Date | January 1998 |
| Creators | Choy, Wing Yiu, 1969- |
| Contributors | Sanctuary, Bryan C. (advisor) |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Department of Chemistry.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 001657180, proquestno: NQ50131, Theses scanned by UMI/ProQuest. |
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