Accelerated Pure Shift NMR Spectroscopy with Deep Learning,Analytical Chemistry

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Accelerated Pure Shift NMR Spectroscopy with Deep Learning
Analytical Chemistry ( IF 6.7 ) Pub Date : 2024-01-17 , DOI: 10.1021/acs.analchem.3c04007
Haolin Zhan _{1,

2} , Jiawei Liu ₁ , Qiyuan Fang ₁ , Xinyu Chen ₁ , Liangliang Hu ₁

Affiliation

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.

中文翻译：

通过深度学习加速纯位移核磁共振波谱分析

纯位移核磁共振（NMR）光谱提供了一种有前途的解决方案，可以提供足够的光谱分辨率，并且已越来越多地应用于化学的各个分支，但最佳分辨率通常伴随着较长的实验时间。我们提出了深度学习的概念证明，可实现快速、高质量和可靠的纯位移 NMR 重建。深度学习 (DL) 协议可以消除采样不足的伪影，区分化学位移相近的峰，并在加速采集的同时重建高分辨率纯位移 NMR 光谱。更有意义的是，轻量级神经网络通过数百个模拟数据学习在个人计算机上提供了令人满意的重建性能，这在一定程度上缓解了深度学习项目通常需要的大量真实训练样本和先进计算硬件的需求。此外，进一步利用适用于常见深度学习案例的 M-to-S 策略来提高网络泛化能力。因此，这项研究朝着广泛化学应用的深度学习协议迈出了有意义的一步。

更新日期：2024-01-17

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