Developments in Multivariate DOSY processing and Pure Shift NMR, authored by Adam Colbourne and submitted for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical Sciences at the University of Manchester, 26th February 2014. The theme of this thesis is resolution; the separation of overlapping, entangled information in NMR spectroscopy data. The ability to resolve the features of a dataset is important because it greatly simplifies, or even makes possible, the interpretation of those features to yield information. Here, methods developed to increase resolving power in two different areas of NMR spectroscopy are described; these areas are so-called 'pure shift' or δ-resolved NMR and diffusion-ordered spectroscopy (DOSY). Pure shift NMR aims to reduce the overlap of the signals present in an NMR spectrum by collapsing the multiplet structure caused by spin-spin coupling. There are a variety of methods for achieving this, each of which has its pros and cons. A homo-nuclear decoupling scheme originated by K. Zangger and H. Sterk is implemented in its most recent form to decouple the F1 and F2 dimensions of the 2D NOESY experiment individually. The application of covariance processing to allow the removal of all the multiplet structure from data produced by these singly decoupled experiments is demonstrated and the results discussed. Full experimental homo-nuclear decoupling of 2D NMR is discussed and demonstrated with the TOCSY experiment using a combination of Bax's constant time decoupling scheme in F1 and Zangger-Sterk decoupling in F2. DOSY is strictly a catch-all term for the data processing applied to pulsed field gradient NMR data to extract information on the diffusion of chemical species, but is widely accepted as referring to the combination of the two. Applied to mixtures, DOSY is a powerful tool that can allow the separation of the spectra of the mixture components; this greatly simplifies the process of interpreting mixture NMR data. However, DOSY processing struggles where signals from different, but similarly diffusing chemical species overlap; one is faced with the problem of separating similar, overlapping exponentials in noisy data. Standard DOSY processing schemes can be described as univariate or multivariate with respect to the way in which they handle DOSY data; the former analyses the data a single frequency at a time, the latter tries to untangle the whole dataset at once. Multivariate processing schemes are better suited to resolving overlap in DOSY data, because they use all of the information available, the counter point being that too much information causes them to break down. SCORE is one such algorithm. Research into constraining and augmenting SCORE is presented, leading into a discussion of the potential application of prior knowledge of the DOSY dataset. While exploring the application of prior knowledge, it was realised that the differences between the spectra extracted by SCORE could be used to separate mixture components in a general manner. The presented OUTSCORE algorithm uses information from both the spectra and diffusion dimensions of DOSY data to separate components almost an order of magnitude more similarly diffusing than was previously possible. Finally, a hybrid processing scheme termed LOCODOSY is reported, that breaks a dataset down into smaller sections for individual multivariate analysis before recombination of the results; circumventing the problem of having too much or too little data in any one analysis. The LOCODOSY processing scheme is demonstrated on both the SCORE and OUTSCORE algorithms.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:618011 |
| Date | January 2014 |
| Creators | Colbourne, Adam |
| Contributors | Morris, Gareth; Nilsson, Mathias |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/developments-in-multivariate-dosy-processing-and-pure-shift-nmr(12eaae86-174a-4bf9-92ca-3c9ddfa748ec).html |
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