A novel R-package graphic user interface for the analysis of metabonomic profiles

Background Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries. /Results The package offers the following options: Raw 1-dimensional spectra processing: phase, baseline correction and normalization. Importing processed spectra. Including/excluding spectral ranges, optional binning and bucketing, detection and alignment of peaks. Sorting of metabolites based on their ability to discriminate, metabolite selection, and outlier identification. Multivariate unsupervised analysis: principal components analysis (PCA). Multivariate supervised analysis: partial least squares (PLS), linear discriminant analysis (LDA), k-nearest neighbor classification. Neural networks. Visualization and overlapping of spectra. Plot values of the chemical shift position for different samples. Furthermore, the "Metabonomic" GUI includes a console to enable other kinds of analyses and to take advantage of all R statistical tools. /Conclusion We made complex multivariate analysis user-friendly for both experienced and novice users, which could help to expand the use of NMR-based metabonomics

Process Applications of NMR

This thesis describes applications of NMR techniques to flowing liquid streams to obtain quantitative information about the contents of the streams. The quantitative accuracy of NMR spectroscopy for composition measurement of liquid mixtures is measured as ±0.34 mol% and ±1 mol% for static and flowing mixtures respectively. The effects of flow on NMR spectroscopy are analysed using the residence time distributions of the streams in the magnet and the detection coil. Algorithms are developed for automated analysis of the NMR spectra of the mixtures, in which automatic phase and baseline correction are performed together. A peak-assignment algorithm is written that identifies components in a mixture based on the patterns observed in the pure-component spectra. Automated composition analysis of mixture spectra is performed using these algorithms in less than 4 minutes with an accuracy of ±0.66 mol%. A mathematical model is derived for the NMR spectrum of a mixture that considers the spectrum a weighted sum of pure-component spectra shifted in frequency. The experimental lineshape observed in an inhomogeneous magnetic field is poorly fitted by a Lorentzian lineshape, so a new model lineshape is developed based on the distribution of resonance frequencies across the sample. Volume selective NMR spectroscopy using the STEAM and PROJSAT pulse sequences is optimised to give quantitative results from well-defined volumes with minimal signal contamination. The STEAM pulse sequence is modified to include flow-compensated slice selection gradients. The accuracy of the compositions measured from volume selective spectra is measured as ±1 mol% and ±2 mol% for static and flowing mixtures respectively. Pulsed field gradient NMR sequences using double echoes for flow compensation are tested on flowing water, then used to determine the droplet size distributions of flowing emulsions. Flow images are acquired of a vertical liquid jets showing the narrowing and acceleration of the jet and the entrainment of the surrounding water

Standardless, automated determination of Chlorine-35 by 35Cl nuclear magnetic resonance

A robust, fully automated, walk-up method is reported to quantify chloride in samples using 35Cl nuclear magnetic resonance. Minimal user input is required, no standards are acquired at the time of analysis; and the submission, acquisition, processing, and production of results are seamlessly integrated within existing software. The method demonstrated good linearity with R2 = 0.999 over three orders of magnitude of analyte concentration. The results were highly independent of analyte functionality, and the stability of instrument response was sufficient that analyses of additional standards were not required for a period of several months. At a nominal sample concentration of 10 mg/ml in D2O at 400 MHz, detection and quantitation limits of 0.1 and 0.5% (w/w) were achieved in a 1-h analysis time. Robust methodology was achieved by applying a rigorous approach to method development and validation to determine and evaluate fully the time- and sample-dependent factors that affect quantitation in these measurements

Beyond the noise : high fidelity MR signal processing

This thesis describes a variety of methods developed to increase the sensitivity and resolution of liquid state nuclear magnetic resonance (NMR) experiments. NMR is known as one of the most versatile non-invasive analytical techniques yet often suffers from low sensitivity. The main contribution to this low sensitivity issue is a presence of noise and level of noise in the spectrum is expressed numerically as “signal-to-noise ratio”. NMR signal processing involves sensitivity and resolution enhancement achieved by noise reduction using mathematical algorithms. A singular value decomposition based reduced rank matrix method, composite property mapping, in particular is studied extensively in this thesis to present its advantages, limitations, and applications. In theory, when the sum of k noiseless sinusoidal decays is formatted into a specific matrix form (i.e., Toeplitz), the matrix is known to possess k linearly independent columns. This information becomes apparent only after a singular value decomposition of the matrix. Singular value decomposition factorises the large matrix into three smaller submatrices: right and left singular vector matrices, and one diagonal matrix containing singular values. Were k noiseless sinusoidal decays involved, there would be only k nonzero singular values appearing in the diagonal matrix in descending order providing the information of the amplitude of each sinusoidal decay. The number of non-zero singular values or the number of linearly independent columns is known as the rank of the matrix. With real NMR data none of the singular values equals zero and the matrix has full rank. The reduction of the rank of the matrix and thus the noise in the reconstructed NMR data can be achieved by replacing all the singular values except the first k values with zeroes. This noise reduction process becomes difficult when biomolecular NMR data is to be processed due to the number of resonances being unknown and the presence of a large solvent peak