Spectral processing methods for the removal of t1 noise and solvent artifacts from NMR spectra

Abstract

A data processing method is described which reduces the effects of t1 noise artifacts and improves the presentation of 2D NMR spectral data. A t1 noise profile is produced by measuring the average noise in each column. This profile is then used to determine weighting coefficients for a sliding weighted smoothing filter that is applied to each row, such that the amount of smoothing each point receives is proportional to both its estimated t1 noise level and the level of t1 noise of neighbouring points. Thus, points in the worst t1 noise bands receive the greatest smoothing, whereas points in low-noise regions remain relatively unaffected. In addition, weighted smoothing allows points in low-noise regions to influence neighbouring points in noisy regions. This method is also effective in reducing the noise artifacts associated with the solvent resonance in spectra of biopolymers in aqueous solution. Although developed primarily to improve the quality of 2D NMR spectra of biopolymers prior to automated analysis, this approach should enhance processing of spectra of a wide range of compounds and can be used whenever noise occurs in discrete bands in one dimension of a multi-dimensional spectrum.