Literature DB >> 23534870

Increasing sensitivity in determining chemical shifts in one dimensional Lorentzian NMR spectra.

H S Taylor1, Ralf Haiges, Allan Kershaw.   

Abstract

An algorithm is presented for one-dimensional NMR systems that employs nonlinear, non-Fourier methods to convert noisy time-dependent free induction decay (FID) data to a denoised frequency spectrum that gives reliable chemical shifts and coupling constants when the spectrum is Lorentzian. It is formulated in a way that increases frequency sensitivity and resolution and, for nuclei of low natural abundance, potentially avoids enrichment totally or in part. The algorithm should also be of use in analytical chemistry where enrichment is not possible. In effect, the useful limit of detection is significantly lowered. The algorithm uses new "phasing" and "feature stability upon accumulation" methods to reliably separate signal from noise at low signal-to-noise ratios where the Fourier spectrum requires many more transients to be definitive as to what is signal and what is noise. The long-standing problem of "false features" that plagued many prior attempts to employ nonlinear methods is thereby resolved for Lorentzian spectra. Examples are reported, and the limitations of the algorithm are discussed.

Year:  2013        PMID: 23534870     DOI: 10.1021/jp310725k

Source DB:  PubMed          Journal:  J Phys Chem A        ISSN: 1089-5639            Impact factor:   2.781


  2 in total

1.  Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data.

Authors:  Agustin Barba; Santiago Dominguez; Carlos Cobas; David P Martinsen; Charles Romain; Henry S Rzepa; Felipe Seoane
Journal:  ACS Omega       Date:  2019-02-14

2.  Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic Sparse Matrix Factorization.

Authors:  Shunji Yamada; Atsushi Kurotani; Eisuke Chikayama; Jun Kikuchi
Journal:  Int J Mol Sci       Date:  2020-04-23       Impact factor: 5.923

  2 in total

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