Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA.
J Phys Chem A. 2013 Apr 25;117(16):3319-31. doi: 10.1021/jp310725k. Epub 2013 Apr 15.
An algorithm is presented for one-dimensional NMR systems that employs nonlinear, non-Fourier methods to convert noisy time-dependent free induction decay (FID) data to a denoised frequency spectrum that gives reliable chemical shifts and coupling constants when the spectrum is Lorentzian. It is formulated in a way that increases frequency sensitivity and resolution and, for nuclei of low natural abundance, potentially avoids enrichment totally or in part. The algorithm should also be of use in analytical chemistry where enrichment is not possible. In effect, the useful limit of detection is significantly lowered. The algorithm uses new "phasing" and "feature stability upon accumulation" methods to reliably separate signal from noise at low signal-to-noise ratios where the Fourier spectrum requires many more transients to be definitive as to what is signal and what is noise. The long-standing problem of "false features" that plagued many prior attempts to employ nonlinear methods is thereby resolved for Lorentzian spectra. Examples are reported, and the limitations of the algorithm are discussed.
本文提出了一种用于一维 NMR 系统的算法,该算法采用非线性、非傅里叶方法,将噪声时域自由感应衰减(FID)数据转换为去噪的频率谱,当谱线为洛伦兹线时,该频率谱给出可靠的化学位移和耦合常数。该算法以一种能够提高频率灵敏度和分辨率的方式进行了构建,对于自然丰度低的核,它可以完全或部分避免富集。该算法还应该在无法进行富集的分析化学中有用。实际上,有用的检测极限大大降低。该算法使用新的“相位”和“积累时特征稳定性”方法,在低信噪比下可靠地区分信号和噪声,而傅里叶谱在确定信号和噪声方面需要更多的瞬态。由此解决了困扰许多先前尝试采用非线性方法的洛伦兹谱中“虚假特征”的长期问题。报告了一些示例,并讨论了该算法的局限性。