Removal of t1 noise from metabolomic 2D 1H–13C HSQC NMR spectra by Correlated Trace Denoising

Poulding, Simon; Charlton, Adrian J.; Donarski, James; Wilson, Julie

doi:10.1016/j.jmr.2007.09.004

Cited by 15 publications

(11 citation statements)

References 30 publications

(38 reference statements)

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“…A second method, relying on the singular value decomposition of matrices and less specific to NMR, was developed by Cadzow et al and used successfully to reduce t 1 noise. [29,30] A third method, developed specifically for metabolomics and called correlated trace de-noising, was developed by Poulding et al [31] Of these three noise reduction methods, the Cadzow algorithm is the most general: it is based on Singular Value Decomposition, does not take into account any assumption on the nature of the data, except that it can be decomposed as a sum of exponentially damped sinusoids, which means it can be used on any data that can be Fourier transformed. There is no correlation hypothesis, and the calculation only relies on the internal coherence of the signal.…”

Section: Two-dimensional Fourier Transform Ion Cyclotron Resonance Mamentioning

confidence: 99%

Two‐dimensional Fourier transform ion cyclotron resonance mass spectrometry: reduction of scintillation noise using Cadzow data processing

Agthoven

Coutouly²,

Rolando

et al. 2011

Rapid Comm Mass Spectrometry

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In two-dimensional Fourier transform ion cyclotron resonance mass spectrometry (2D FTICR-MS), scintillation noise, caused mostly by fluctuations in the number of ions in the ICR cell, is the leading cause for errors in spectrum interpretation. In this study, we adapted an algorithm based on singular value decomposition and first introduced by Cadzow et al. (IEE Proceedings Pt. F 1987, 134, 69) to 2D FTICR-MS and we measured its performance in terms of noise reduction without losing signal information in the 2D mass spectrum.

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Section: Two-dimensional Fourier Transform Ion Cyclotron Resonance Mamentioning

confidence: 99%

Two‐dimensional Fourier transform ion cyclotron resonance mass spectrometry: reduction of scintillation noise using Cadzow data processing

Agthoven

Coutouly²,

Rolando

et al. 2011

Rapid Comm Mass Spectrometry

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“…The largest t1 noise ridges can be higher in intensity than genuine peaks associated with low concentration compounds, causing problems for automated peak identification. However, the fact that t1 noise ridges are highly correlated for different F2 values, allows these artifacts to be removed and Correlated Trace Denoising (Poulding et al 2007) was applied to all spectra to reduce the effects of t1 noise before analysis.…”

Section: Rat Brain Tissue Samplesmentioning

confidence: 99%

“…However, prohibitive data acquisition times have meant that such methods have not been fully exploited in non-targeted approaches. Recent advances have significantly helped to reduce the data collection time needed to obtain 2D spectra (Tiziani et al 2006;Giraudeau et al 2009) and to minimise the effect of spectral noise (Poulding et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Peak fitting in 2D 1H–13C HSQC NMR spectra for metabolomic studies

et al. 2010

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A modified Lorentzian distribution function is used to model peaks in two-dimensional (2D) 1 H-13 C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra. The model fit is used to determine accurate chemical shifts from genuine signals in complex metabolite mixtures such as blood. The algorithm can be used to extract features from a set of spectra from different samples for exploratory metabolomics. First a reference spectrum is created in which the peak intensities are given by the median value over all samples at each point in the 2D spectra so that 1 H-13 C correlations in any spectra are accounted for. The mathematical model provides a footprint for each peak in the reference spectrum, which can be used to bin the 1 H-13 C correlations in each HSQC spectrum. The binned intensities are then used as variables in multivariate analyses and those found to be discriminatory are rapidly identified by cross referencing the chemical shifts of the bins with a database of 13 C and 1 H chemical shift correlations from known metabolites.

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“…In one particular case when t 1 noise from a strong signal prevents observation of adjacent weaker cross-peaks, selective saturation of that strong signal has been employed in MAS NMR [9]. On the other hand, various post-acquisition methods have been proposed [10-12]. Nevertheless, these methods do not address the root causes of t 1 noise, and hence should only be used as a last resort.…”

Section: Introductionmentioning

confidence: 99%

A simple method for NMR t1 noise suppression

Harwood

Yang

et al. 2017

Journal of Magnetic Resonance

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t1 noise appears as random or semi-random spurious streaks along the indirect t1 (F1) dimension of a 2D or nD NMR spectrum. It can significantly downgrade spectral quality, especially for spectra with strong diagonal signals such as NOESY, because useful and weak cross-peaks can be easily buried under t1 noise. One of the significant contributing factors to t1 noise is unwanted and semi-random F2 signal modulation during t1 acquisition. As such, t1 noise from different acquisitions is unlikely to correlate with each other strongly. In the case of NOESY, co-addition of multiple spectra significantly reduces t1 noise compared with conventional acquisition with the same amount of total acquisition time and resolution.

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Removal of t1 noise from metabolomic 2D 1H–13C HSQC NMR spectra by Correlated Trace Denoising

Cited by 15 publications

References 30 publications

Two‐dimensional Fourier transform ion cyclotron resonance mass spectrometry: reduction of scintillation noise using Cadzow data processing

Two‐dimensional Fourier transform ion cyclotron resonance mass spectrometry: reduction of scintillation noise using Cadzow data processing

Peak fitting in 2D 1H–13C HSQC NMR spectra for metabolomic studies

A simple method for NMR t1 noise suppression

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