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Separation of water artifacts in 2D NOESY protein spectra using congruent matrix pencils

机译:使用一致的矩阵铅笔在二维NOESY蛋白光谱中分离水假象

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摘要

Multidimensional proton nuclear magnetic resonance spectra of biomolecules dissolved in aqueous solutions are usually contaminated by an intense water artifact. We discuss the application of a generalized eigenvalue decomposition (GEVD) method using a matrix pencil to solve the blind source separation (BSS) problem of removing the intense solvent peak and related artifacts. The method explores correlation matrices of the signals and their filtered versions in the frequency domain and implements a two-step algebraic procedure to solve the GEVD. Two-dimensional nuclear Overhauser enhancement spectroscopy (2D NOESY) of dissolved proteins is studied. Results are compared to those obtained with the SOBI [Belouchrani et al., IEEE Trans. Signal Process. 45(2) (1997) 434-444] algorithm which jointly diagonalizes several time-delayed correlation matrices and to those of the fastICA [Hyvaerinen and Oja, Neural Comput. 9 (1996) 1483-1492] algorithm which exploits higher order statistical dependencies of random variables.
机译:溶解在水溶液中的生物分子的多维质子核磁共振谱通常被强烈的水伪影污染。我们讨论使用矩阵铅笔的广义特征值分解(GEVD)方法的应用,以解决去除强溶剂峰和相关伪影的盲源分离(BSS)问题。该方法在频域中探索信号的相关矩阵及其滤波版本,并实现了两步代数过程来求解GEVD。研究了溶解蛋白的二维核Overhauser增强光谱(2D NOESY)。将结果与用SOBI获得的结果进行比较[Belouchrani等,IEEE Trans。信号处理。 45(2)(1997)434-444]算法共同对角化了几个时间相关矩阵和fastICA的相关矩阵[Hyvaerinen和Oja,神经计算。 9(1996)1483-1492]算法利用了随机变量的高阶统计相关性。

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