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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1002/mrc.4960
|2 doi
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|a eng
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|a Anjum, Muhammad Ali Raza
|e verfasserin
|4 aut
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|a Two-dimensional subband Steiglitz-McBride algorithm for automatic analysis of two-dimensional nuclear magnetic resonance data
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 27.01.2020
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|a Date Revised 27.01.2020
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a © 2019 John Wiley & Sons, Ltd.
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|a 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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|a Journal Article
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|a 2D Steiglitz-McBride
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|a 2D-BIC
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|a 2D-IPF
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|a 2D-NMR
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|a algorithm
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|a automatic
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| 650 |
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|a subband
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1 |
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|a Dmochowski, Pawel A
|e verfasserin
|4 aut
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1 |
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|a Teal, Paul D
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Magnetic resonance in chemistry : MRC
|d 1985
|g 58(2020), 1 vom: 07. Jan., Seite 106-115
|w (DE-627)NLM098179667
|x 1097-458X
|7 nnas
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| 773 |
1 |
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|g volume:58
|g year:2020
|g number:1
|g day:07
|g month:01
|g pages:106-115
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|u http://dx.doi.org/10.1002/mrc.4960
|3 Volltext
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