Digitisation and data processing in Fourier transform NMR

Lindon, John C.; Ferrige, Anthony G.

doi:10.1016/0079-6565(80)80002-1

Cited by 248 publications

(24 citation statements)

References 22 publications

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“…It is believed that preprocessing by increasing SI is beneficial for digital NMR resolution . This process is called zero filling and has no effect on the peak positions, intensities, or linewidths of the spectrum, but it does increase the digital resolution (fewer hertz per data point) of signals.…”

Section: Resultsmentioning

confidence: 99%

Practical guide for selection of ¹H qNMR acquisition and processing parameters confirmed by automated spectra evaluation

Monakhova

Diehl²

2017

Magnetic Reson in Chemistry

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In our recent paper, a new technique for automated spectra integration and quality control of the acquired results in qNMR was developed and validated (Monakhova & Diehl, Magn. Res. Chem. 2017, doi: 10.1002/mrc.4591). The present study is focused on the influence of acquisition and postacquisition parameters on the developed automated routine in particular, and on the quantitative NMR (qNMR) results in general, which has not been undertaken previously in a systematic and automated manner. Results are presented for a number of model mixtures and authentic pharmaceutical products measured on 500- and 600-MHz NMR spectrometers. The influence of the most important acquisition (spectral width, transmitter [frequency] offset, number of scans, and time domain) and processing (size of real spectrum, deconvolution, Gaussian window multiplication, and line broadening) parameters for qNMR was automatically investigated. Moderate modification of the majority of the investigated parameters from default instrument settings within evaluated ranges does not significantly affect the trueness and precision of the qNMR. Lite Gaussian window multiplication resulted in accuracy improvement of the qNMR output and is recommended for routine measurements. In general, given that the acquisition and processing parameters were selected based on the presented guidelines, automated qNMR analysis can be employed for reproducible high-precision concentration measurements in practice.

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Section: Resultsmentioning

confidence: 99%

Practical guide for selection of ¹H qNMR acquisition and processing parameters confirmed by automated spectra evaluation

Monakhova

Diehl²

2017

Magnetic Reson in Chemistry

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“…In addition, the SNR may be decreased as a result of the water reference deconvolution. For broad resonances the deconvolution procedure results in a narrowing of the resultant resonance line widths, which also decreases SNR in the same manner as resolution enhancement (25). This effect may be somewhat alleviated by development of an improved algorithm that imposes a Gaussian line shape with a width that is related to initial line width.…”

Section: Resultsmentioning

confidence: 99%

Automated processing for proton spectroscopic imaging using water reference deconvolution

Maudsley

Meyerhoff

et al. 1994

Magnetic Resonance in Med

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Automated formation of MR spectroscopic images (MRSI) is necessary before routine application of these methods is possible for in vivo studies; however, this task is complicated by the presence of spatially dependent instrumental distortions and the complex nature of the MR spectrum. A data processing method is presented for completely automated formation of in vivo proton spectroscopic images, and applied for analysis of human brain metabolites. This procedure uses the water reference deconvolution method (G. A. Morris, J. Magn. Reson. 80, 547(1988)) to correct for line shape distortions caused by instrumental and sample characteristics, followed by parametric spectral analysis. Results for automated image formation were found to compare favorably with operator dependent spectral integration methods. While the water reference deconvolution processing was found to provide good correction of spatially dependent resonance frequency shifts, it was found to be susceptible to errors for correction of line shape distortions. These occur due to differences between the water reference and the metabolite distributions.

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“…Following Lindon and Ferrige (3), the spectral line resulting from acquisition of the FID over a time T is:

S_{T} (ν) = 2 M_{0} \int_{0}^{T} e^{- t ∕ T_{2}} e^{- i 2 π ν t} d t

It can be shown either through direct calculation or using the convolution theorem that the spectral line maximum is attained at n = 0, as for Eq. (38).…”

Section: Derivation Of the Nmr-mfmentioning

confidence: 99%

“…More commonly, the time constant of the multiplying exponential, τ, is estimated from the overall decay time constant of the FID envelope. This leads to a substantial increase in SNR for all spectral lines including those for which T 2, i ≠ τ (3). …”

Section: Derivation Of the Nmr-mfmentioning

confidence: 99%

“…The NMR-MF can be understood intuitively by noting that SNR is greatest at the beginning of an FID and decreases over the acquisition period, so that preferential weighting of the initial part of the signal leads to an increase in spectral SNR after Fourier transformation. Multiplication by many functional forms would result in such preferential weighting; the fact that among exponentials, maximum SNR results from multiplication as described above has been demonstrated in many places (1–3). It is important to note that the SNR maximized in this way is defined according to maximum spectral line amplitude, rather than area.…”

Section: Introductionmentioning

confidence: 97%

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Equivalence of the time‐domain matched filter and the spectral‐domain matched filter in one‐dimensional NMR spectroscopy

Spencer¹

2010

Concepts Magnetic Resonance

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A type of "matched filter" (MF), used extensively in the processing of one-dimensional spectra, is defined by multiplication of a free-induction decay (FID) by a decaying exponential with the same time constant as that of the FID. This maximizes, in a sense to be defined, the signal-to-noise ratio (SNR) in the spectrum obtained after Fourier transformation. However, a different entity known also as the matched filter was introduced by van Vleck in the context of pulse detection in the 1940's and has become widely integrated into signal processing practice. These two types of matched filters appear to be quite distinct. In the NMR case, the "filter", that is, the exponential multiplication, is defined by the characteristics of, and applied to, a time domain signal in order to achieve improved SNR in the spectral domain. In signal processing, the filter is defined by the characteristics of a signal in the spectral domain, and applied in order to improve the SNR in the temporal (pulse) domain. We reconcile these two distinct implementations of the matched filter, demonstrating that the NMR "matched filter" is a special case of the matched filter more rigorously defined in the signal processing literature. In addition, two limitations in the use of the MF are highlighted. First, application of the MF distorts resonance ratios as defined by amplitudes, although not as defined by areas. Second, the MF maximizes SNR with respect to resonance amplitude, while intensities are often more appropriately defined by areas. Maximizing the SNR with respect to area requires a somewhat different approach to matched filtering.

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Digitisation and data processing in Fourier transform NMR

Cited by 248 publications

References 22 publications

Practical guide for selection of ¹H qNMR acquisition and processing parameters confirmed by automated spectra evaluation

Practical guide for selection of ¹H qNMR acquisition and processing parameters confirmed by automated spectra evaluation

Automated processing for proton spectroscopic imaging using water reference deconvolution

Equivalence of the time‐domain matched filter and the spectral‐domain matched filter in one‐dimensional NMR spectroscopy

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Digitisation and data processing in Fourier transform NMR

Cited by 248 publications

References 22 publications

Practical guide for selection of 1H qNMR acquisition and processing parameters confirmed by automated spectra evaluation

Practical guide for selection of 1H qNMR acquisition and processing parameters confirmed by automated spectra evaluation

Automated processing for proton spectroscopic imaging using water reference deconvolution

Equivalence of the time‐domain matched filter and the spectral‐domain matched filter in one‐dimensional NMR spectroscopy

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Practical guide for selection of ¹H qNMR acquisition and processing parameters confirmed by automated spectra evaluation

Practical guide for selection of ¹H qNMR acquisition and processing parameters confirmed by automated spectra evaluation