Efficient peak extraction of proton NMR spectroscopy using lineshape adaptation

Nuclear magnetic resonance (NMR) spectroscopy signals are ideally modelled as a superimposition of damped exponentials in additive Gaussian noise. In order to extract the information from these signals, methods are needed to decompose the signal into its components and estimate their parameters. This task can become quite difficult due to factors such as large number of samples, unknown and possibly large number of components, and lineshape distortion. In this paper, we propose a computationally efficient method for peak extraction in proton NMR spectroscopy without any a priori information. This method combines a simple damped complex exponential parameter estimation strategy with lineshape adaptation in the frequency domain. We apply the proposed technique on real NMR data and show that it outperforms competing state of the art methods. It is shown that the new method is capable of extracting very small lines such as satellites.