Maximum-Likelihood Deconvolution of NMR Spectra in Multidimensional Space

Maximum-likelihood spectral deconvolution can be applied to three- and four-dimensional NMR spectra, improving resolution by effectively sharpening lines in air of the dimensions of the data set. Spectral noise is simultaneously suppressed and cross-talk between (hyper)planes is minimized. This method is demonstrated for two three-dimensional data sets; the method can be combined with linear prediction time extension of multidimensional free-induction decays and other preparatory data processing. Multidimensional MLM reconstruction offers promise for future fully automated data processing and analysis of large NMR data arrays used far determination of complex molecular structures from NMR. The algorithm is readily adapted to parallel computers, which will extend its practicality for the largest data arrays, including four- and five-dimensional data.