A new general-purpose fully automatic baseline-correction procedure for 1D and 2D NMR data

A new procedure for automatic baseline correction of NMR data sets is presented. It is based on an improved automatic recognition of signal-free regions that uses a Continuous Wavelet transform derivative calculation, followed by a baseline modelling procedure based on the Whittaker smoother algorithm. The method has been proven to automatically flatten 1D and 2D NMR spectra with large baseline distortions arising from different sources, is tolerant to low signal-to-noise ratio spectra, and to signals of varying widths in a single spectrum. Even though this procedure has so far only been applied to NMR spectra, we believe it to also be applicable to other spectroscopies having relatively narrow peaks (e.g., mass spectrometry), and potentially to those with broad peaks (e.g., near infrared or ultraviolet).     

The equilibrium unfolding of MerP characterized by multivariate analysis of 2D NMR data

A general problem when analysing NMR spectra that reflect variations in the environment of target molecules is that different resonances are affected to various extents. Often a few resonances that display the largest frequency changes are selected as probes to reflect the examined variation, especially in the case, where the NMR spectra contain numerous resonances. Such a selection is dependent on more or less intuitive judgements and relying on the observed spectral variation being primarily caused by changes in the NMR sample. Second, recording changes observed for a few (albeit significant) resonances is inevitably accompanied by not using all available information in the analysis. Likewise, the commonly used chemical shift mapping (CSM) [Biochemistry 39 (2000) 26, Biochemistry 39 (2000) 12595] constitutes a loss of information since the total variation in the data is not retained in the projection into this single variable. Here, we describe a method for subjecting 2D NMR time-domain data to multivariate analysis and illustrate it with an analysis of multiple NMR experiments recorded at various folding conditions for the protein MerP. The calculated principal components provide an unbiased model of variations in the NMR spectra and they can consequently be processed as NMR data, and all the changes as reflected in the principal components are thereby made available for visual inspection in one single NMR spectrum. This approach is much less laborious than consideration of large numbers of individual spectra, and it greatly increases the interpretative power of the analysis.     

The fumarate sensor DcuS: progress in rapid protein fold elucidation by combining protein structure prediction methods with NMR spectroscopy

We illustrate how moderate resolution protein structures can be rapidly obtained by interlinking computational prediction methodologies with un- or partially assigned NMR data. To facilitate the application of our recently described method of ranking and subsequent refining alternative structural models using unassigned NMR data [Proc. Natl. Acad. Sci. USA 100 (2003) 15404] for such "structural genomics"-type experiments it is combined with protein models from several prediction techniques, enhanced to utilize partial assignments, and applied on a protein with an unknown structure and fold. From the original NMR spectra obtained for the 140 residue fumarate sensor DcuS, 1100 1H, 13C, and 15N chemical shift signals, 3000 1H-1H NOESY cross peak intensities, and 209 backbone residual dipolar couplings were extracted and used to rank models produced by de novo structure prediction and comparative modeling methods. The ranking proceeds in two steps: first, an optimal assignment of the NMR peaks to atoms is found for each model independently, and second, the models are ranked based on the consistency between the NMR data and the model assuming these optimal assignments. The low-resolution model selected using this ranking procedure had the correct overall fold and a global backbone RMSD of 6.0 angstrom, and was subsequently refined to 3.7 angstrom RMSD. With the incorporation of a small number of NOE and residual dipolar coupling constraints available very early in the traditional spectral assignment process, a model with an RMSD of 2.8 angstrom could rapidly be built. The ability to generate moderate resolution models within days of NMR data collection should facilitate large scale NMR structure determination efforts.     

Negative impact of noise on the principal component analysis of NMR data

Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.     

An efficient peak assignment algorithm for two-dimensional NMR correlation spectra of framework structures

An algorithm is described for efficiently assigning the resonances in NMR spectra to the inequivalent atoms in the structure under study based on the information in two-dimensional NMR correlation experiments and the 'connectivities' known from the structure. The algorithm, which is based on basic graph theory concepts, finds all possible assignments sets which are consistent with the experimentally observed correlations and known connectivities in a very efficient manner. It is designed to deal with less than ideal experimental data in which there may be overlapping peaks and uncertainty about the presence or absence of correlation peaks. The algorithm was primarily developed for assigning the peaks in the high-resolution solid-state 29Si MAS NMR spectra of highly siliceous zeolites based on two-dimensional 29Si INADEQUATE spectra and is described using the zeolites ZSM-12 and ZSM-5 as working examples. Peak assignment for zeolite frameworks is particularly challenging since there is often little or no information to distinguish peaks from one another such as characteristic chemical shifts, relative intensities, or different relaxation times. The algorithm may be a useful tool for easily, reliably, and efficiently working out peak assignments from other types of correlation experiments on other types of systems and further examples are provided in the Supplementary material.     

NMR spectral quantitation by principal component analysis. III. A generalized procedure for determination of lineshape variations

We present a general procedure for automatic quantitation of a series of spectral peaks based on principal component analysis (PCA). PCA has been previously used for spectral quantitation of a single resonant peak of constant shape but variable amplitude. Here we extend this procedure to estimate all of the peak parameters: amplitude, position (frequency), phase and linewidth. The procedure consists of a series of iterative steps in which the estimates of position and phase from one stage of iteration are used to correct the spectra prior to the next stage. The process is convergent to a stable result, typically in less than 5 iterations. If desired, remaining linewidth variations can then be corrected. Correction of (typically) unwanted variations of these types is important not only for direct peak quantitation, but also as a preprocessing step for spectral data prior to application of pattern recognition/classification techniques. The procedure is demonstrated on simulated data and on a set of 992 (31)P NMR in vivo spectra taken from a kinetic study of rat muscle energetics. The proposed procedure is robust, makes very limited assumptions about the lineshape, and performs well with data of low signal-to-noise ratio.     

Automated Signal Detection as Tool to Evaluate Magnetic Field Homogeneity from Fourier Transformed Proton NMR Spectra

Off line analysis of proton nuclear magnetic resonance (NMR) spectra by computerized peak detection is used to determine the shim quality from Fourier transformed spectra. The method is designed to analyze spectra independently from any further information about the measurement. For this purpose, the number of maxima in a spectral region is determined and compared to the number of peaks identified by an automatic routine utilizing a curvature-based method. The resulting “shim quality quotient” represents a good indicator for the magnetic field homogeneity present during the measurement. This method can be applied to separated single signals as well as to entire spectral regions. Hence, its brought use as tool in automatic interpretation of NMR spectra is possible. An example is shown for an in situ proton NMR measurement of glucose mutarotation.     

Entropy minimization and spectral dissimilarity curve resolution technique applied to nuclear magnetic resonance data sets

The use of entropy minimization and spectral dissimilarity is applied to three nuclear magnetic resonance (NMR) data sets. The data sets contain 2, 2, and 3 observables each. It was found that without any a priori information the sets of pure component spectra underlying the NMR spectroscopic observations could be extracted. These successful spectral resolutions suggest that a combined entropy minimization and spectral dissimilarity approach can be further developed for even larger NMR data sets containing a larger number of observables. Brief comparison to DECRA and PMF curve resolution results is also presented.     

光谱分析在煤结构研究中的进展

煤结构是各类煤相关研究的微观基础,光谱分析作为煤结构研究的重要方法被广泛应用,其在煤结构研究中的进展对光谱分析方法的普及、应用和发展有重要意义.光谱分析方法研究煤结构已成为煤化工领域使用的常规方法,能够快速无损检测,对煤分子结构的破坏小,可为不同环境条件下煤物理化学性质的变化提供有效的检测手段.从光谱分析在煤质、大分子...     

On reasons of Si NMR chemical shift/structure relations for silicon oxides, nitrides, and carbides: An individual-gauge-for-localized-orbitals study

For -quartz, monoclinic ZSM-5, -and β-Si₃N₄ and SiC---6H polytype, the silicon chemical shifts have been calculated using the IGLO (individual gauge for localized orbitals) method and models of different size in real crystal geometry. The result is a theoretical chemical shift scale, which is very similar to the corresponding experimental scale from ²⁹Si MAS NMR experiments. It is shown that the assignment of isotropic silicon chemical shifts of crystallized solids based on theory is a method of practical applicability, also in cases where experimental methods or empirical relations fail. The two NMR spectral lines of -Si₃N₄ are for the first time assigned to the crystallographic positions. The partition of the silicon chemical shifts into localized contributions from different parts of the model allows insight into the interactions around the resonance nucleus due to substituent and geometry variations leading to silicon chemical shifts.     

Phase-sensitive spectral estimation by the hybrid filter diagonalization method

A more robust way to obtain a high-resolution multidimensional NMR spectrum from limited data sets is described. The Filter Diagonalization Method (FDM) is used to analyze phase-modulated data and cast the spectrum in terms of phase-sensitive Lorentzian "phase-twist" peaks. These spectra are then used to obtain absorption-mode phase-sensitive spectra. In contrast to earlier implementations of multidimensional FDM, the absolute phase of the data need not be known beforehand, and linear phase corrections in each frequency dimension are possible, if they are required. Regularization is employed to improve the conditioning of the linear algebra problems that must be solved to obtain the spectral estimate. While regularization smoothes away noise and small peaks, a hybrid method allows the true noise floor to be correctly represented in the final result. Line shape transformation to a Gaussian-like shape improves the clarity of the spectra, and is achieved by a conventional Lorentzian-to-Gaussian transformation in the time-domain, after inverse Fourier transformation of the FDM spectra. The results obtained highlight the danger of not using proper phase-sensitive line shapes in the spectral estimate. The advantages of the new method for the spectral estimate are the following: (i) the spectrum can be phased by conventional means after it is obtained; (ii) there is a true and accurate noise floor; and (iii) there is some indication of the quality of fit in each local region of the spectrum. The method is illustrated with 2D NMR data for the first time, but is applicable to n-dimensional data without any restriction on the number of time/frequency dimensions.     

基于选择编码的超快速磁共振波谱方法

核磁共振(NMR)波谱技术是当今最有力的谱学工具之一,在化学、生物和医药等众多领域获得重要而广泛的应用．基于时空编码的快速采样方法自2002年Frydman小组提出后,大大增强了高维磁共振波谱的采样效率．在某一些应用体系中,存在若干个强度远超于其他谱峰的情况,很容易由于动态增益不足而检测不到某些较弱的谱峰,而往往这些较弱的谱峰包含着感兴趣的信息．且在实际的化学生物应用中,存在选择性感兴趣检测的情况,即只需要选择性地观察若干个具有标记作用的谱峰．由于时空编码技术借助于高速切换的双极性梯度来完成解码,因而无法选择性地检测若干个非连续的频点．为解决以上两个问题,该文提出一种选择编码的时空编码方法,即在序列中施加选择性脉冲,选择性破坏某些谱峰的编码过程,使之不能在解码期解码,从而简化谱图,实现选择性压制或者非连续频点的感兴趣检测．如果把选择性反转脉冲换为硬反转脉冲加选择性反转脉冲,则最终的谱图中只出现被选择性脉冲选中的谱峰．理论分析及相关的实验验证了这种方法的可行性和有效性.     

直肠组织的高分辨魔角旋转核磁共振波谱研究

文章对6例直肠癌变及正常组织进行高分辨魔角旋转核磁共振波谱研究,结果显示直肠癌变和正常组织的核磁共振氢谱存在明显差异。这可以通过特征峰面积与0.88处峰积分面积的比值上的差异看出：（1）在化学位移0.75～1.55之间,癌组织各种氨基酸[缬氨酸,异亮氨酸,亮氨酸]与脂肪酸甲基的比值（I₂/I₁）,癌组织乳酸盐与脂肪酸甲基的比值(I₄/I₁)都明显增大。（2）在化学位移1.55～2.90之间,癌变组织中亮氨酸、赖氨酸、异亮氨酸与脂肪酸甲基的比值（I₇/I₁）, 谷氨酸、谷氨酰胺、缬氨酸、琥珀酸与脂肪酸甲基的比值((I₉+I₁₁)/I₁)、天冬氨酸与脂肪酸甲基的比值((I₁₂+I₁₄)/I₁)都较正常组织明显增大。（3）在化学位移2.90～3.49之间,癌变组织氨基酸与脂肪酸甲基的比值（I₁₅/I₁）、胆碱类与脂肪酸甲基的比值((I₁₆+I₁₇)/I₁)、牛磺酸与脂肪酸甲基的比值((I₁₈+I₁₉)/I₁)都较正常组织明显增大。（4）在化学位移3.49～4.50之间,其他代谢物与脂肪酸甲基的比值（I₂₀/I₁）,以及甘油基与脂肪酸甲基的比值（I₂₂/I₁）在癌变组织中都有增大的趋势。（5）化学位移4.5～10之间,癌变组织的核苷酸发生了变化,癌变组织的不饱和脂肪酸与脂肪酸甲基的比值（I₂₄/I₁）明显减小。（6）在化学位移-8～0.75之间,癌变组织的谱峰有减少的趋势。通过上述分析可知,通过癌变与正常组织代谢物NMR谱峰的差异,可以区分癌变和正常组织。说明核磁共振波谱技术可能发展成为一种诊断直肠癌的新方法。     

A generalized approach to automated NMR peak list editing: application to reduced dimensionality triple resonance spectra

We present an algorithm and program called Pattern Picker that performs editing of raw peak lists derived from multidimensional NMR experiments with characteristic peak patterns. Pattern Picker detects groups of correlated peaks within peak lists from reduced dimensionality triple resonance (RD-TR) NMR spectra, with high fidelity and high yield. With typical quality RD-TR NMR data sets, Pattern Picker performs almost as well as human analysis, and is very robust in discriminating real peak sets from noise and other artifacts in unedited peak lists. The program uses a depth-first search algorithm with short-circuiting to efficiently explore a search tree representing every possible combination of peaks forming a group. The Pattern Picker program is particularly valuable for creating an automated peak picking/editing process. The Pattern Picker algorithm can be applied to a broad range of experiments with distinct peak patterns including RD, G-matrix Fourier transformation (GFT) NMR spectra, and experiments to measure scalar and residual dipolar coupling, thus promoting the use of experiments that are typically harder for a human to analyze. Since the complexity of peak patterns becomes a benefit rather than a drawback, Pattern Picker opens new opportunities in NMR experiment design.     

Random Initialisation of the Spectral Variables: an Alternate Approach for Initiating Multivariate Curve Resolution Alternating Least Square (MCR-ALS) Analysis

Multivariate curve resolution alternating least square (MCR-ALS) analysis is the most commonly used curve resolution technique. The MCR-ALS model is fitted using the alternate least square (ALS) algorithm that needs initialisation of either contribution profiles or spectral profiles of each of the factor. The contribution profiles can be initialised using the evolve factor analysis; however, in principle, this approach requires that data must belong to the sequential process. The initialisation of the spectral profiles are usually carried out using the pure variable approach such as SIMPLISMA algorithm, this approach demands that each factor must have the pure variables in the data sets. Despite these limitations, the existing approaches have been quite a successful for initiating the MCR-ALS analysis. However, the present work proposes an alternate approach for the initialisation of the spectral variables by generating the random variables in the limits spanned by the maxima and minima of each spectral variable of the data set. The proposed approach does not require that there must be pure variables for each component of the multicomponent system or the concentration direction must follow the sequential process. The proposed approach is successfully validated using the excitation-emission matrix fluorescence data sets acquired for certain fluorophores with significant spectral overlap. The calculated contribution and spectral profiles of these fluorophores are found to correlate well with the experimental results. In summary, the present work proposes an alternate way to initiate the MCR-ALS analysis.     

A new method for spectral decomposition using a bilinear Bayesian approach

A frequent problem in analysis is the need to find two matrices, closely related to the underlying measurement process, which when multiplied together reproduce the matrix of data points. Such problems arise throughout science, for example, in imaging where both the calibration of the sensor and the true scene may be unknown and in localized spectroscopy where multiple components may be present in varying amounts in any spectrum. Since both matrices are unknown, such a decomposition is a bilinear problem. We report here a solution to this problem for the case in which the decomposition results in matrices with elements drawn from positive additive distributions. We demonstrate the power of the methodology on chemical shift images (CSI). The new method, Bayesian spectral decomposition (BSD), reduces the CSI data to a small number of basis spectra together with their localized amplitudes. We apply this new algorithm to a 19F nonlocalized study of the catabolism of 5-fluorouracil in human liver, 31P CSI studies of a human head and calf muscle, and simulations which show its strengths and limitations. In all cases, the dataset, viewed as a matrix with rows containing the individual NMR spectra, results from the multiplication of a matrix of generally nonorthogonal basis spectra (the spectral matrix) by a matrix of the amplitudes of each basis spectrum in the the individual voxels (the amplitude matrix). The results show that BSD can simultaneously determine both the basis spectra and their distribution. In principle, BSD should solve this bilinear problem for any dataset which results from multiplication of matrices representing positive additive distributions if the data overdetermine the solutions.     

核磁共振谱图自动相位校正新方法——等密度点连线法

文中提出一种新的基于DISPA圆的自动相位校正方法,该方法利用谱峰两侧在复平面上数据疏密程度相等的点的连线的斜率,求出每个峰的相位角, 继而利用多个峰的相角与频率的线性关系得出准确的全谱相位的零级和一级校正值. 该方法不需要使用全部峰点,而且对于存在基线畸变和有重叠峰的谱图都能较好的处理. 该算法不需要迭代计算,实现简单,计算快速准确,已将其成功运用于自行开发的核磁共振数据处理软件ECNMR中.     

Information‐theoretic chemometric analyses of Raman data for chemical reaction studies

A methodology of multivariate chemometric techniques based on the information‐theoretic approach was applied for elucidating chemical reaction information from a Raman data array R _m×ν that arises from in situ reaction monitoring. This reaction‐induced dynamic dataset R _m×ν can be contaminated by random cosmic ray spikes found in the midst of characteristic spectral variations associated with the disappearance or emergence of Raman active reactants, intermediates and products. Such spurious cosmic spikes were identified and removed using a novel and fast numerical approach based on maximum and minimum spectral entropy principles while preserving the genuine reaction‐induced spectral variations. Subsequently, the band‐target entropy minimization (BTEM) algorithm, a minimum spectral entropy based self‐modeling curve resolution technique, was applied to recover the pure component spectra of Raman active chemical species. Information gain through the chemometric analyses was calculated using information entropies with base 2 logarithm. This sequence of information‐theoretic chemometric analyses (or transinformations) was successfully tested on the reaction spectral data obtained from alcoholysis of acetic anhydride, which contains four Raman active chemical species. It is envisioned that this series of multivariate statistical analyses will be useful in chemical reaction studies and process analytical technology (PAT) applications that utilize in situ Raman spectroscopy to monitor transient dynamic changes in chemical concentrations, and also in Raman microscopy/imaging data containing spatial variations. Copyright © 2010 John Wiley & Sons, Ltd.     

Imaged deconvolution: a method for extracting high-resolution NMR spectra from inhomogeneous fields

We present a novel method for obtaining high resolution NMR spectra in the presence of grossly inhomogeneous magnetic fields, such as those encountered in one-sided access NMR. Our method combines the well-known principle of reference deconvolution with NMR imaging in order to resolve spectral features with frequency resolution orders of magnitude smaller than the prevailing line-broadening due to field inhomogeneity. We demonstrate that, in cases of inhomogeneous field line-broadening more than an order of magnitude larger than the spectral features to be resolved, rather than performing reference deconvolution on the sample as a whole, it is more favourable in terms of SNR to divide the target region of a sample into smaller sub-regions, by means of chemical shift imaging, and then to perform reference deconvolution on the individual sub-region spectra, finally summing the results In this way, significant resolution enhancements can be obtained in the presence of severe magnetic field inhomogeneity without an unacceptable loss in SNR.     

Universal correlations and power-law tails in financial covariance matrices

We investigate whether quantities such as the global spectral density or individual eigenvalues of financial covariance matrices can be best modelled by standard random matrix theory or rather by its generalisations displaying power-law tails. In order to generate individual eigenvalue distributions a chopping procedure is devised, which produces a statistical ensemble of asset-price covariances from a single instance of financial data sets. Local results for the smallest eigenvalue and individual spacings are very stable upon reshuffling the time windows and assets. They are in good agreement with the universal Tracy-Widom distribution and Wigner surmise, respectively. This suggests a strong degree of robustness especially in the low-lying sector of the spectra, most relevant for portfolio selections. Conversely, the global spectral density of a single covariance matrix as well as the average over all unfolded nearest-neighbour spacing distributions deviate from standard Gaussian random matrix predictions. The data are in fair agreement with a recently introduced generalised random matrix model, with correlations showing a power-law decay.