Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Nagoya 464-8601, Chikusa-ku, Japan.
RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Yokohama 230-0045, Tsurumi-ku, Japan.
Int J Mol Sci. 2020 Apr 23;21(8):2978. doi: 10.3390/ijms21082978.
Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time-frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the * relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.
核磁共振（NMR）波谱通常用于表征分子复杂性，因为它可以通过各种采集参数和脉冲程序，无创地提供有关样品化学结构和分子迁移率的信息丰富的原子分辨率数据。然而，由于噪声和信号重叠，分析混合物的累积 NMR 数据具有挑战性。因此，在进行光谱分析之前，数据清理步骤（如质量检查、降噪和信号解卷积）是重要的过程。在这里，我们开发了一种用于数据清理的 NMR 测量信息学工具，它将短时傅里叶变换（STFT；一种时频分析方法）和概率稀疏矩阵分解（PSMF）结合用于信号解卷积和噪声因子分析。我们的工具可应用于一维 NMR 光谱的原始自由感应衰减（FID）信号。我们表明，信号解卷积方法降低了 FID 信号的噪声，将信噪比（SNR）提高了约十倍，并且将其应用于扩散编辑光谱允许通过*弛豫时间来分离大分子和未被抑制的小分子的信号。对 NMR 数据集的噪声因子分析确定了 SNR 与采集参数之间的相关性，确定了降低 SNR 的主要实验因素。
