Alternative data processing techniques for serial NMR experiments

NMR measurements are often performed in a serial manner, that is, the acquisition of an FID signal is repeated under various conditions, either controlled (as temperature or pH changes) or uncontrolled (as reaction progress). The traditional approach to process “serial” data is to perform the Fourier transform of each FID in a series. However, it suffers from several problems, in particular, from the need to sample full Nyquist grid and reach a sufficient signal-to-noise ratio in each separate spectrum. The problems become particularly cumbersome in the case of multidimensional signals, where sampling is costly and sensitivity is an issue. Over the years, several methods of alternative, “joint” processing of FID series have been proposed. In this paper, we discuss the principles of some of them: Accordion Spectroscopy, Multidimensional Decomposition, Radon transform, a combination of Compressed Sensing and the Laplace transform. According to our knowledge, this is the first review on serial NMR data processing approaches. The reader is provided with MATLAB scripts allowing to perform simulations and processing using these algorithms.