Modern spectrum analysis in multidimensional NMR spectroscopy: Comparison of linear-prediction extrapolation and maximum-entropy reconstruction
Modern spectrum analysis in multidimensional NMR spectroscopy: Comparison of linear-prediction extrapolation and maximum-entropy reconstruction
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DOI：
10.1021/ja011669o
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发表时间：
2002-03-06
影响因子：
15
通讯作者：
Hoch, JC
中科院分区：
文献类型：
--
作者：
Stern, AS;Li, KB;Hoch, JC
核磁共振波谱是一种固有的不敏感技术，许多具有挑战性的应用，如生物分子研究，在灵敏度和分辨率的极限操作，在超导磁体，低温探针和脉冲序列技术的进步，导致显着提高灵敏度和分辨率在过去的十年。相反，在NMR光谱学中最广泛使用的信号处理方法，即通过线性预测（LP）然后通过离散傅立叶变换（DFT）对时域信号进行外推，是在20世纪80年代早期开发的，并且尚未对其灵敏度和分辨率的影响进行详细审查。在这里，我们报告了对通过LP外推和DFT获得的光谱的准确度和精确度的首次系统研究。我们比较的结果得到的光谱最大熵（MaxEnt）重建，这是同期开发的LP外推，但没有广泛应用于NMR光谱。虽然它减少了截断伪影，并增加了强峰的幅度，我们发现，LP外推产生假阳性峰，并引入频率误差。对于较长的数据记录和较高的信噪比，LP外推的这些缺陷变得不那么明显。与LIP外推法相比，MaxEnt通常对于给定数量的数据样本产生更多可检测的峰、更准确的峰频率和更少的假阳性峰。MaxEnt还允许使用非线性采样，这可以显著提高分辨率。这些结果表明，使用MaxEnt与非线性采样，而不是LP外推，可以减少足够的灵敏度和分辨率所需的仪器时间量的2倍或更多。
NMR spectroscopy is an inherently insensitive technique, and many challenging applications such as biomolecular studies operate at the very limits of sensitivity and resolution, Advances in superconducting magnet, cryogenic probe, and pulse sequence technologies have resulted in dramatic improvements in both sensitivity and resolution in the past decade. Conversely, the signal-processing method used most widely in NMR spectroscopy, extrapolation of the time domain signal by linear prediction (LP) followed by discrete Fourier transformation (DFT), was developed in the early 1980s and has not been subjected to detailed scrutiny for its impact on sensitivity and resolution. Here we report the first systematic investigation of the accuracy and precision of spectra obtained by LP extrapolation followed by DFT. We compare the results to spectra obtained by maximum-entropy (MaxEnt) reconstruction, which was developed contemporaneously to LP extrapolation but is not widely employed in NMR spectroscopy. Although it reduces truncation artifacts and increases the amplitudes of strong peaks, we find that LP extrapolation generates false-positive peaks and introduces frequency errors. These defects of LP extrapolation become less pronounced for longer data records and higher signal-to-noise ratio. MaxEnt generally yields more detectable peaks for a given number of data samples, more accurate peak frequencies, and fewer false-positive peaks than LIP extrapolation. MaxEnt also permits the use of nonlinear sampling, which can give dramatic improvements in resolution. These results show that the use of MaxEnt together with nonlinear sampling, rather than LP extrapolation, could reduce the amount of instrument time required for adequate sensitivity and resolution by a factor of 2 or more.