A Bayesian method to quantifying chemical composition using NMR: Application to porous media systems

This paper describes a Bayesian approach for inferring the chemical composition of liquids in porous media obtained using nuclear magnetic resonance (NMR). The model analyzes NMR data automatically in the time domain, eliminating the operator dependence of a conventional spectroscopy approach. The technique is demonstrated and validated experimentally on both pure liquids and liquids imbibed in porous media systems, which are of significant interest in heterogeneous catalysis research. We discuss the challenges and practical solutions of parameter estimation in both systems. The proposed Bayesian NMR approach is shown to be more accurate and robust than a conventional spectroscopy approach, particularly for signals with a low signal-to-noise ratio (SNR) and a short life time.

[1]  W. Marsden.  I and J , 2012 .

[2]  J Higinbotham,et al.  Use of voigt lineshape for quantification of in vivo 1H spectra , 1997, Magnetic resonance in medicine.

[3]  S. Huffel,et al.  MR spectroscopy quantitation: a review of time‐domain methods , 2001, NMR in biomedicine.

[4]  F. Malz,et al.  Validation of quantitative NMR. , 2005, Journal of pharmaceutical and biomedical analysis.

[5]  Marvin H. J. Guber.  Bayesian Spectrum Analysis and Parameter Estimation , 1988 .

[6]  L. Dou,et al.  Bayesian inference and Gibbs sampling in spectral analysis and parameter estimation: II , 1995 .

[7]  Kevin R Minard,et al.  NMR methods for in situ biofilm metabolism studies. , 2005, Journal of microbiological methods.

[8]  William J. Astle,et al.  A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures , 2011, 1105.2204.

[9]  Carl E. Rasmussen,et al.  Occam's Razor , 2000, NIPS.

[10]  Sebastian Nowozin,et al.  Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification , 2014, 1402.3580.

[11]  G Larry Bretthorst,et al.  High dynamic‐range magnetic resonance spectroscopy (MRS) time‐domain signal analysis , 2009, Magnetic resonance in medicine.

[12]  Lynn F. Gladden,et al.  Comparing Strengths of Surface Interactions for Reactants and Solvents in Porous Catalysts Using Two-Dimensional NMR Relaxation Correlations , 2009 .

[13]  Lynn F. Gladden,et al.  Measuring adsorption, diffusion and flow in chemical engineering: applications of magnetic resonance to porous media , 2011 .

[14]  Bruce J Balcom,et al.  The internal magnetic field distribution, and single exponential magnetic resonance free induction decay, in rocks. , 2005, Journal of magnetic resonance.

[15]  M. F. Cardoso,et al.  The simplex-simulated annealing approach to continuous non-linear optimization , 1996 .

[16]  Lalitha Venkataramanan,et al.  The diffusion-spin relaxation time distribution function as an experimental probe to characterize fluid mixtures in porous media , 2002 .

[17]  J. Griffin,et al.  Time-domain Bayesian detection and estimation of noisy damped sinusoidal signals applied to NMR spectroscopy. , 2007, Journal of magnetic resonance.