Hello,
I have samples that contain analytes with relatively low concentration (in the order of tens of ppm) in various solvents (aqueous and organic solvents). As the 1H NMR solvents signals are very strong they hinder the observation of the analytes signals (which are present in relatively low quantities), hence, I would like to perform 1H multiple solvent suppression experiments. How should I proceed ? Which pulse program to choose ? (I work on a BRUKER Avance neo spectrometer).
Thank you in advance.
- how close are the signals of interest to the interfering solvent peaks?
- do you need accurate signal amplitude estimates?
A Bayesian is available that eliminates the need for solvent suppression. While the application below is for in-vivo MRS, it is a general solution for any situation where one for more very high amplitude signals interfere with very low amplitude signals.
Hutton, W. C.; Bretthorst, G. L.; Garbow, J. R.; Ackerman, J. J.; 2009; "High dynamic-range magnetic resonance spectroscopy (MRS) time-domain signal analysis";Journal: Magn Reson Med; 62:4; 1026-35
Abstract: In the absence of water signal suppression, the proton magnetic resonance spectroscopy ((1)H MRS) in vivo water resonance signal-to-noise ratio (SNR) is orders of magnitude larger than the SNR of all the other resonances. In this case, because the high-SNR water resonance dominates the data, it is difficult to obtain reliable parameter estimates for the low SNR resonances. Herein, a new model is described that offers a solution to this problem. In this model, the time-domain signal for the low SNR resonances is represented as the conventional sum of exponentially decaying complex sinusoids. However, the time-domain signal for the high SNR water resonance is assumed to be a complex sinusoid whose amplitude is slowly varying from pure exponential decay and whose phase is slowly varying from a constant frequency. Thus, the water resonance has only an instantaneous amplitude and frequency. The water signal is neither filtered nor subtracted from the data. Instead, Bayesian probability theory is used to simultaneously estimate the frequencies, decay-rate constants, and amplitudes for all the low SNR resonances, along with the water resonance's time-dependent amplitude and phase. While computationally intensive, this approach models all of the resonances, including the water and the metabolites of interest, to within the noise level.
I am analyzing samples of diverse nature (organic or aqueous) that contain different background chemicals and solvents so the interfering peaks can be anywhere in the conventional SW of 1H.
Thank you for the article, will definitely read it !
I am not sure about this video, but it may be helpful,
https://www.youtube.com/watch?v=8rtpCgMm4cE
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