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Review and prospect: NMR spectroscopy denoising and reconstruction with low‐rank Hankel matrices and tensors
Author(s) -
Qiu Tianyu,
Wang Zi,
Liu Huiting,
Guo Di,
Qu Xiaobo
Publication year - 2021
Publication title -
magnetic resonance in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 72
eISSN - 1097-458X
pISSN - 0749-1581
DOI - 10.1002/mrc.5082
Subject(s) - hankel matrix , chemistry , nuclear magnetic resonance spectroscopy , tensor (intrinsic definition) , rank (graph theory) , spectroscopy , matrix (chemical analysis) , noise reduction , sensitivity (control systems) , nuclear magnetic resonance , statistical physics , analytical chemistry (journal) , physics , computer science , mathematics , artificial intelligence , organic chemistry , quantum mechanics , pure mathematics , electronic engineering , engineering , chromatography , combinatorics
Nuclear magnetic resonance (NMR) spectroscopy is an important analytical tool in chemistry, biology, and life science, but it suffers from relatively low sensitivity and long acquisition time. Thus, improving the apparent signal‐to‐noise ratio and accelerating data acquisition became indispensable. In this review, we summarize the recent progress on low‐rank Hankel matrix and tensor methods, which exploit the exponential property of free‐induction decay signals, to enable effective denoising and spectra reconstruction. We also outline future developments that are likely to make NMR spectroscopy a far more powerful technique.