回顾与展望:指数信号与机器学习在核磁共振中的应用

IF 0.8 4区化学 Q4 SPECTROSCOPY

Spectroscopy Pub Date : 2023-09-01 DOI:10.56530/spectroscopy.yx1073b8

Di Guo, Xianjing Chen, Mengli Lu, Wangfeng He, Sihui Luo, Yanqin Lin, Yuqing Huang, Lizhi Xiao, Xiaobo Qu

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引用次数: 0

摘要

核磁共振波谱在化学、生物医学、食品科学、能源等领域的成分分析、分子结构解析和动态研究中具有重要的应用价值。作为一种基本函数，指数函数可以用来模拟自由感应衰减、弛豫和扩散的核磁共振信号。在本文中，我们将分别回顾傅里叶和拉普拉斯核磁共振指数信号，以及最先进的机器学习在核磁共振应用中的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Review and Prospect: Applications of Exponential Signals with Machine Learning in Nuclear Magnetic Resonance

Nuclear magnetic resonance (NMR) spectroscopy presents an important analytical tool for composition analysis, molecular structure elucidation, and dynamic study in the fields of chemistry, biomedicine, food science, energy and more. As a basic function, exponential functions can be applied to model NMR signals of free induction decay, relaxation, and diffusion. In this paper, we will review Fourier and Laplace NMR exponential signals separately, as well as the performance of state-of-the-art machine learning on NMR applications.

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来源期刊

Spectroscopy 物理-光谱学

CiteScore

1.10

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： Spectroscopy welcomes manuscripts that describe techniques and applications of all forms of spectroscopy and that are of immediate interest to users in industry, academia, and government.