1 research outputs found
Deep Learning Methodology for Obtaining Ultraclean Pure Shift Proton Nuclear Magnetic Resonance Spectra
Nuclear magnetic resonance (NMR) is one of the most powerful
analytical
techniques. In order to obtain high-quality NMR spectra, a real-time
Zangger–Sterk (ZS) pulse sequence is employed to collect low-quality
pure shift NMR data with high efficiency. Then, a neural network named
AC-ResNet and a loss function named SM-CDMANE are developed to train
a network model. The model with excellent abilities of suppressing
noise, reducing line widths, discerning peaks, and removing artifacts
is utilized to process the acquired NMR data. The processed spectra
with noise and artifact suppression and small line widths are ultraclean
and high-resolution. Peaks overlapped heavily can be resolved. Weak
peaks, even hidden in the noise, can be discerned from noise. Artifacts,
even as high as spectral peaks, can be removed completely while not
suppressing peaks. Eliminating perfectly noise and artifacts and smoothing
baseline make spectra ultraclean. The proposed methodology would greatly
promote various NMR applications