欠采样
化学
核磁共振波谱
深度学习
光谱学
人工神经网络
分辨率(逻辑)
一般化
人工智能
高分辨率
协议(科学)
分析化学(期刊)
计算机科学
有机化学
遥感
替代医学
地质学
病理
数学分析
物理
医学
量子力学
数学
作者
Haolin Zhan,Jiawei Liu,Feng‐Ling Qing,Xinyu Chen,Linlin Hu
标识
DOI:10.1021/acs.analchem.3c04007
摘要
Pure shift nuclear magnetic resonance (NMR) spectroscopy presents a promising solution to provide sufficient spectral resolution and has been increasingly applied in various branches of chemistry, but the optimal resolution is generally accompanied by long experimental times. We present a proof of concept of deep learning for fast, high-quality, and reliable pure shift NMR reconstruction. The deep learning (DL) protocol allows one to eliminate undersampling artifacts, distinguish peaks with close chemical shifts, and reconstruct high-resolution pure shift NMR spectroscopy along with accelerated acquisition. More meaningfully, the lightweight neural network delivers satisfactory reconstruction performance on personal computers by several hundred simulated data learning, which somewhat lifts the prohibiting demand for a large volume of real training samples and advanced computing hardware generally required in DL projects. Additionally, an M-to-S strategy applicable to common DL cases is further exploited to boost the network generalization capability. As a result, this study takes a meaningful step toward deep learning protocols for broad chemical applications.
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