Nuclear magnetic resonance data processing methods
This thesis describes the application of a wide variety of data processing methods, in particular the Maximum Entropy Method (MEM), to data from Nuclear Magnetic Resonance (NMR) experiments. Chapter 1 provides a brief introduction to NMR and to data processing, which is developed in chapter 2. NMR is described in terms of the classical model due to Bloch, and the principles of conventional (Fourier transform) data processing developed. This is followed by a description of less conventional techniques. The MEM is derived on several grounds, and related to both Bayesian reasoning and Shannon information theory. Chapter 3 describes several methods of evaluating the quality of NMR spectra obtained by a variety of data processing techniques; the simple criterion of spectral appearance is shown to be completely unsatisfactory. A Monte Carlo method is described which allows several different techniques to be compared, and the relative advantages of Fourier transformation and the MEM are assessed. Chapter 4 describes in vivo NMR, particularly the application of the MEM to data from Phase Modulated Rotating Frame Imaging (PMRFI) experiments. In this case the conventional data processing is highly unsatisfactory, and MEM processing results in much clearer spectra. Chapter 5 describes the application of a range of techniques to the estimation and removal of splittings from NMR spectra. The various techniques are discussed using simple examples, and then applied to data from the amino acid iso-leucine. The thesis ends with five appendices which contain historical and philosophical notes, detailed calculations pertaining to PMRFI spectra, and a listing of the MEM computer program.