Anjum MAR, Dmochowski PA, Teal PD. Two-dimensional subband Steiglitz-McBride algorithm for automatic analysis of two-dimensional nuclear magnetic resonance data.
MAGNETIC RESONANCE IN CHEMISTRY : MRC 2020;
58:106-115. [PMID:
31663635 DOI:
10.1002/mrc.4960]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 10/01/2019] [Accepted: 10/18/2019] [Indexed: 06/10/2023]
Abstract
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.
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