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一种用于信号密集谱的鲁棒自动相位校正方法。

A robust automatic phase correction method for signal dense spectra.

机构信息

State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, People's Republic of China.

出版信息

J Magn Reson. 2013 Sep;234:82-9. doi: 10.1016/j.jmr.2013.06.012. Epub 2013 Jun 25.

Abstract

A robust automatic phase correction method for Nuclear Magnetic Resonance (NMR) spectra is presented. In this work, a new strategy combining 'coarse tuning' with 'fine tuning' is introduced to correct various spectra accurately. In the 'coarse tuning' procedure, a new robust baseline recognition method is proposed for determining the positions of the tail ends of the peaks, and then the preliminary phased spectra are obtained by minimizing the objective function based on the height difference of these tail ends. After the 'coarse tuning', the peaks in the preliminary corrected spectra can be categorized into three classes: positive, negative, and distorted. Based on the classification result, a new custom negative penalty function used in the step of 'fine tuning' is constructed to avoid the negative peak points in the spectra excluded in the negative peaks and distorted peaks. Finally, the fine phased spectra can be obtained by minimizing the custom negative penalty function. This method is proven to be very robust for it is tolerant to low signal-to-noise ratio, large baseline distortion and independent of the starting search points of phasing parameters. The experimental results on both 1D metabonomics spectra with over-crowded peaks and 2D spectra demonstrate the high efficiency of this automatic method.

摘要

提出了一种稳健的核磁共振(NMR)谱自动相位校正方法。在这项工作中,提出了一种新的“粗调”与“细调”相结合的策略,以准确校正各种谱。在“粗调”过程中,提出了一种新的稳健基线识别方法,用于确定峰尾的位置,然后通过最小化基于这些峰尾高度差的目标函数来获得初步的相位谱。“粗调”后,初步校正谱中的峰可分为三类:正峰、负峰和畸变峰。基于分类结果,在“细调”步骤中构建了一个新的自定义负惩罚函数,以避免在谱中排除负峰和畸变峰的负峰点。最后,通过最小化自定义负惩罚函数来获得精细的相位谱。该方法对低信噪比、大基线失真具有很强的鲁棒性,并且不依赖于相参数的起始搜索点。在具有过拥挤峰的 1D 代谢组学谱和 2D 谱上的实验结果证明了这种自动方法的高效性。

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