  Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering
tool to determine the metabolic concentrations, molecule structures and so on.
The data acquisition time, however, is very long in multi-dimensional NMR. To
accelerate data acquisition, non-uniformly sampling is an effective way but may
encounter severe spectral distortions and unfaithful quantitative measures when
the acceleration factor is high. By modelling the acquired signal as the
superimposed exponentials, we proposed a virtual peak (VIP) approach to
selfadapt the prior spectral information, such as the resonance frequency and
peak lineshape, and then feed these information into the reconstruction. The
proposed method is further implemented with cloud computing to facilitate
online, open, and easy access. Results on simulated and experimental data
demonstrate that, compared with the low-rank Hankel matrix method, the new
approach reconstructs high-fidelity NMR spectra from highly undersampled data
and achieves more accurate quantification. The maximum quantitative errors of
distances between nuclear pairs and concentrations of metabolites in mixtures
have been reduced by 61.1% and 57.7%, respectively.
