Multidimensional NMR can provide unmatched spectral resolution, which is crucial when dealing with samples of biological macromolecules. The resolution, however, comes at the high price of long experimental time. Non-uniform sampling (NUS) of the evolution time domain allows to suppress this limitation by sampling only a small fraction of the data, but requires sophisticated algorithms to reconstruct omitted data points. A significant group of such algorithms known as compressed sensing (CS) is based on the assumption of sparsity of a reconstructed spectrum. Several papers on the application of CS in multidimensional NMR have been published in the last years, and the developed methods have been implemented in most spectral processing software. However, the publications rarely show the cases when NUS reconstruction does not work perfectly or explain how to solve the problem. On the other hand, every-day users of NUS develop their rules-of-thumb, which help to set up the processing in an optimal way, but often without a deeper insight. In this paper, we discuss several sources of problems faced in CS reconstructions: low sampling level, missassumption of spectral sparsity, wrong stopping criterion and attempts to extrapolate the signal too much. As an appendix, we provide MATLAB codes of several CS algorithms used in NMR. We hope that this work will explain the mechanism of NUS reconstructions and help readers to set up acquisition and processing parameters. Also, we believe that it might be helpful for algorithm developers.Electronic supplementary materialThe online version of this article (doi:10.1007/s10858-016-0068-3) contains supplementary material, which is available to authorized users.

Pure shift NMR with maximum performance by non-uniform sampling with prior knowledge.

NMR spectroscopy, used routinely for structure elucidation, has also become a widely applied tool for process and reaction monitoring. However, the most informative of NMR methods—correlation experiments—are often useless in this kind of applications. The traditional sampling of a multidimensional FID is usually time‐consuming, and thus, the reaction‐monitoring toolbox was practically limited to 1D experiments (with rare exceptions, e.g., single‐scan or fast‐sampling experiments). Recently, the technique of time‐resolved non‐uniform sampling (TR‐NUS) has been proposed, which allows to use standard multidimensional pulse sequences preserving the temporal resolution close to that achievable in 1D experiments. However, the method existed only as a prototype and did not allow on‐the‐fly processing during the reaction. In this paper, we introduce TReNDS: free, user‐friendly software kit for acquisition and processing of TR‐NUS data. The program works on Bruker, Agilent, and Magritek spectrometers, allowing to carry out up to four experiments with interleaved TR‐NUS. The performance of the program is demonstrated on the example of enzymatic hydrolysis of sucrose.

EXACT acquisition allows the fast and safer acquisition of ASAP 2D NMR experiments in just a few seconds.