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Two‐dimensional subband Steiglitz–McBride algorithm for automatic analysis of two‐dimensional nuclear magnetic resonance data
Author(s) -
Anjum Muhammad Ali Raza,
Dmochowski Pawel A.,
Teal Paul D.
Publication year - 2020
Publication title -
magnetic resonance in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 72
eISSN - 1097-458X
pISSN - 0749-1581
DOI - 10.1002/mrc.4960
Subject(s) - free induction decay , chemistry , nuclear magnetic resonance , bayesian probability , algorithm , nuclear magnetic resonance spectroscopy , automation , magnetic resonance imaging , biological system , artificial intelligence , computer science , physics , spin echo , radiology , medicine , mechanical engineering , biology , engineering
Rapid, accurate, and automatic quantitation of two‐dimensional nuclear magnetic resonance(2D‐NMR) data is a challenging problem. Recently, a Bayesian information criterion based subband Steiglitz–McBride algorithm has been shown to exhibit superior performance on all three fronts when applied to the quantitation of one‐dimensional NMR free induction decay data. In this paper, we demonstrate that the 2D Steiglitz–McBride algorithm, in conjunction with 2D subband decomposition and the 2D Bayesian information criterion, also achieves excellent results for 2D‐NMR data in terms of speed, accuracy, and automation—especially when compared in these respects to the previously published analysis techniques for 2D‐NMR data.