Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China.
Anal Chem. 2021 Jan 26;93(3):1377-1382. doi: 10.1021/acs.analchem.0c03087. Epub 2020 Dec 30.
Nuclear magnetic resonance (NMR) is one of the most powerful analytical tools and is extensively applied in many fields. However, compared to other spectroscopic techniques, NMR has lower sensitivity, impeding its wider applications. Using data postprocessing techniques to increase the NMR spectral signal-to-noise ratio (SNR) is a relatively simple and cost-effective method. In this work, a deep neural network, termed as DN-Unet, is devised to suppress noise in liquid-state NMR spectra to enhance SNR. It combines structures of encoder-decoder and convolutional neural network. Different from traditional deep learning training strategy, M-to-S strategy is developed to enhance DN-Unet capability that multiple noisy spectra (inputs) correspond to a same single noiseless spectrum (label) in the training stage. The trained 1D model can be used for denoising not only 1D but also high dimension spectra, further improving DN-Unet's performance. 1D, 2D, and 3D NMR spectra were utilized to evaluate DN-Unet performance. The results suggest that DN-Unet provides larger than 200-fold increase in SNR with weak peaks hidden in noise perfectly recovered and spurious peaks suppressed well. Since DN-Unet developed here to increase SNR is based on data postprocessing, it is universal for a variety of samples and NMR platforms. The great SNR enhancement and extreme excellence in differentiating signal and noise would greatly promote various liquid-state NMR applications.
核磁共振（NMR）是最强大的分析工具之一，广泛应用于许多领域。然而，与其他光谱技术相比，NMR 的灵敏度较低，限制了其更广泛的应用。使用数据后处理技术来提高核磁共振谱的信噪比（SNR）是一种相对简单且经济有效的方法。在这项工作中，设计了一种称为 DN-Unet 的深度神经网络，用于抑制液体核磁共振谱中的噪声以提高 SNR。它结合了编码器-解码器和卷积神经网络的结构。与传统的深度学习训练策略不同，开发了 M-to-S 策略来增强 DN-Unet 的能力，即在训练阶段，多个噪声谱（输入）对应于相同的单个无噪声谱（标签）。在训练阶段，多个噪声谱（输入）对应于相同的单个无噪声谱（标签）。在训练阶段，多个噪声谱（输入）对应于相同的单个无噪声谱（标签）。在训练阶段，多个噪声谱（输入）对应于相同的单个无噪声谱（标签）。在训练阶段，多个噪声谱（输入）对应于相同的单个无噪声谱（标签）。在训练阶段，多个噪声谱（输入）对应于相同的单个无噪声谱（标签）。在训练阶段，多个噪声谱（输入）对应于相同的单个无噪声谱（标签）。在训练阶段，多个噪声谱（输入）对应于相同的单个无噪声谱（标签）。在训练阶段，多个噪声谱（输入）对应于相同的单个无噪声谱（标签）。训练好的一维模型不仅可以用于去噪一维谱，还可以用于高维谱，进一步提高了 DN-Unet 的性能。使用一维、二维和三维 NMR 谱评估了 DN-Unet 的性能。结果表明，DN-Unet 提供了超过 200 倍的 SNR 增益，能够完美恢复隐藏在噪声中的弱峰，并很好地抑制了伪峰。由于这里开发的用于提高 SNR 的 DN-Unet 是基于数据后处理的，因此它适用于各种样品和 NMR 平台。极大的 SNR 增强和在区分信号和噪声方面的卓越表现，将极大地促进各种液体核磁共振应用。
