Nuclear Magnetic Resonance Data Processing Research Articles

An Investigation into the Applicability of Wideline NMR for the Determination of Oil in Waxes

AbstractThe applicability of solid‐state NMR (Nuclear Magnetic Resonance) for determination of oil content in waxes is probed by NMR experimental and data processing approaches. NMR is found to underestimate the value of the oil content in Fischer‐Tropsch (FT) waxes due to the ability of the wax to trap small domains of oil within the matrix during crystallisation. No clear relationship could be obtained between T2 relaxation time and oil content, even after applying corrections due to disparities between mass and proton densities. Near linear calibration curves could be obtained by isolating a true oil signal at −32 °C. Model studies using heptadecane as simple oil analogue proved that removing oil/wax interactions and only using the oil as a diluent, near linear calibration curves could be attained. Insight into the mechanism of oil retention in both FT and paraffin waxes is obtained, showing differences due to both molecular and morphological variation. Results showed that, due to underlying molecular interactions, linear calibration curves cannot necessarily be expected even when melting behaviour varies linearly. In applications where oil content plays a crucial role in wax properties, this knowledge enables the industry to make more informed decisions when discrepancies in wax performance arise.

A Generally Regularized Inversion for NMR Applications and Beyond

NMR (Nuclear Magnetic Resonance) technique presents a powerful analytical tool in the fields of chemistry, biology, and material science. The performance of the NMR technique highly relies on post-data processing methods, which are diverse according to different applications. In this paper, we propose a general processing method that can cover a wide range of applications from molecular dynamics study to biomedical engineering. We analyze the computation performance of the method by comparing it to other well-known algorithms and demonstrate its application in various NMR data processing problems, i.e., diffusion-ordered spectroscopy processing, under-sampled MRI (Magnetic Resonance Imaging) reconstruction, Laplace NMR inversion, and spectrum denoising. The results corroborate that the method performs effectively and efficiently compared to state-of-the-art methods. Moreover, we construct the method as a light toolbox, with which the users can perform various kinds of data processing tasks conveniently via graphic interfaces.

Two-dimensional NMR inversion based on fast norm smoothing method

Two-dimensional (2D) nuclear magnetic resonance (NMR) inversion operates with massive echo train data and is an ill-posed problem. It is very important to select a suitable inversion method for the 2D NMR data processing. In this study, we propose a fast, robust, and effective method for 2D NMR inversion that improves the computational efficiency of the inversion process by avoiding estimation of some unneeded regularization parameters. Firstly, a method that combines window averaging (WA) and singular value decomposition (SVD) is used to compress the echo train data and obtain the singular values of the kernel matrix. Subsequently, an optimum regularization parameter in a fast manner using the signal-to-noise ratio (SNR) of the echo train data and the maximum singular value of the kernel matrix are determined. Finally, we use the Butler-Reeds-Dawson (BRD) method and the selected optimum regularization parameter to invert the compressed data to achieve a fast 2D NMR inversion. The numerical simulation results indicate that the proposed method not only achieves satisfactory 2D NMR spectra rapidly from the echo train data of different SNRs but also is insensitive to the number of the final compressed data points.

NMRium browser-based nuclear magnetic resonance data processing

Tony Davies and Luc Patiny introduce us to free online NMR data processing tool in “NMRium browser-based nuclear magnetic resonance data processing”. They run through the background to the project, how it works and how you can try it yourself. There is a video introduction and an online demo page where you can play with different scenarios.

A proposed NMR solution for multi-phase flow fluid detection

In the petroleum industry, detection of multi-phase fluid flow is very important in both surface and down-hole measurements. Accurate measurement of high rate of water or gas multi-phase flow has always been an academic and industrial focus. NMR is an efficient and accurate technique for the detection of fluids; it is widely used in the determination of fluid compositions and properties. This paper is aimed to quantitatively detect multi-phase flow in oil and gas wells and pipelines and to propose an innovative method for online nuclear magnetic resonance (NMR) detection. The online NMR data acquisition, processing and interpretation methods are proposed to fill the blank of traditional methods. A full-bore straight tube design without pressure drop, a Halbach magnet structure design with zero magnetic leakage outside the probe, a separate antenna structure design without flowing effects on NMR measurement and automatic control technology will achieve unattended operation. Through the innovation of this work, the application of NMR for the real-time and quantitative detection of multi-phase flow in oil and gas wells and pipelines can be implemented.

A Practical Method to Compensate for the Effect of Echo Spacing on the Shale NMRT2Spectrum

AbstractThe low field nuclear magnetic resonance (NMR) technique is considered as one of the most effective methods to characterize the pore size and fluid distribution in geophysical prospecting. The signal of NMR is noninvasive and lithology independent, while the effect of NMR responses influenced by the echo spacing (TE) is significant when applied to unconventional reservoirs which are featured with ultralow porosity and pore radius.TEis expected to be as low as possible to ensure most of the hydrogen signals are accounted for, but it is hard to achieve in downhole NMR measurement. We develop a practical method to compensate for the influence ofTEon the transversal relaxation time (T2) spectrum of shale. Based on the experiments with differentTE, the relationship between the NMR calibrated porosity and theTEis established to recover the signal amplitude. The effect ofTEon the peak position is investigated, and then an empirical equation is constructed to move the peak position to its original state. By the above two‐step correction algorithm, theT2spectrums of shale at largeTEare compensated precisely. The proposed method provides an effective way to obtain the realT2spectrum and achieves good results in the downhole NMR data processing of shale reservoir.

SpinStudioJ: A cross-platform NMR data acquisition and processing workbench based on a plug-in architecture.

Flexibility and extensibility are important issues in the design of nuclear magnetic resonance (NMR) software, as these determine the ability to integrate a variety of continuously evolving data acquisition and processing methods. Here, SpinStudioJ is introduced. It is an NMR data acquisition and processing workbench with a plug-in-based architecture. The workbench is based on Eclipse Rich Client Platform, which provides a plug-and-play runtime mechanism and rich graphical user interface functionality. New data acquisition methods and processing algorithms can be easily integrated into the SpinStudioJ workbench by defining extension points, without the need to redistribute existing modules. The software is independent of operating systems, as it leverages the cross-platform feature of the Java virtual machine.

Correction to: Analysis of the Regularization Parameters of Primal–Dual Interior Method for Convex Objectives Applied to 1H Low Field Nuclear Magnetic Resonance Data Processing

Two-dimensional low-field hydrogen nuclear magnetic resonance (2D 1H LF-NMR) analysis of chemical compounds measures T1 and T2 relaxation times observed as exponential decay curves. Once relaxation curves are measured and stored in the format of discrete digital signals, they must be transformed, mathematically, into spectra that can be read and interpreted. We used primal–dual interior method for convex objectives (PDCO) that provided more accurate reconstructions than the standard algorithms. It is the objective of this paper to estimate the most suitable PDCO parameterization that provide accurate and robust reconstructions of relaxation curves into 2D spectra for LF-NMR under different signal-to-noise ratios. Finding optimal regularization parameters is an active field of mathematical research. PDCO, however, by making use of two regularization parameters instead of a single one, presents a much harder task, where the consolidated search criteria of a single parameter cannot be extended to two parameters. We featured a method based on numerical experiments and simulations that identified optimal and unique model coefficients that maximize PDCO reconstruction accuracy under different signal-to-noise conditions. The coefficients, as determined for artificial signals, increase the confidence of accurate reconstruction for 2D LF-NMR analysis on real lab samples.

Analysis of the Regularization Parameters of Primal–Dual Interior Method for Convex Objectives Applied to 1H Low Field Nuclear Magnetic Resonance Data Processing

Analysis of the Regularization Parameters of Primal–Dual Interior Method for Convex Objectives Applied to 1H Low Field Nuclear Magnetic Resonance Data Processing

A Hybrid Method for NMR Data Compression Based on Window Averaging (WA) and Principal Component Analysis (PCA)

Prior to the advent of nuclear magnetic resonance (NMR) data inversion, a common approach for handling the large amount of raw echo data collected by NMR logging was data compression for improving the inversion speed. A fast compression method with a high compression ratio is required for processing NMR logging data. In this paper, we proposed a hybrid method to compress NMR data based on the window averaging (WA) and principal component analysis (PCA) methods. The proposed method was compared with the WA method and the PCA method in terms of the compression times of simulated one-, two-, and three-dimensional NMR data, the inversion times of compressed echo data, and the accuracy of NMR maps created with and without compression. We processed NMR log data and compared the inversion results with different compression methods. The results indicated that the proposed method with a high compression speed and a high compression ratio can be used for NMR data compression, and its accuracy depended on the precompressed echo number, and it is obvious that the method have practical applications for NMR data processing, especially for multi-dimensional NMR.

Speaq 2.0: A complete workflow for high-throughput 1D NMR spectra processing and quantification.

Nuclear Magnetic Resonance (NMR) spectroscopy is, together with liquid chromatography-mass spectrometry (LC-MS), the most established platform to perform metabolomics. In contrast to LC-MS however, NMR data is predominantly being processed with commercial software. Meanwhile its data processing remains tedious and dependent on user interventions. As a follow-up to speaq, a previously released workflow for NMR spectral alignment and quantitation, we present speaq 2.0. This completely revised framework to automatically analyze 1D NMR spectra uses wavelets to efficiently summarize the raw spectra with minimal information loss or user interaction. The tool offers a fast and easy workflow that starts with the common approach of peak-picking, followed by grouping, thus avoiding the binning step. This yields a matrix consisting of features, samples and peak values that can be conveniently processed either by using included multivariate statistical functions or by using many other recently developed methods for NMR data analysis. speaq 2.0 facilitates robust and high-throughput metabolomics based on 1D NMR but is also compatible with other NMR frameworks or complementary LC-MS workflows. The methods are benchmarked using a simulated dataset and two publicly available datasets. speaq 2.0 is distributed through the existing speaq R package to provide a complete solution for NMR data processing. The package and the code for the presented case studies are freely available on CRAN (https://cran.r-project.org/package=speaq) and GitHub (https://github.com/beirnaert/speaq).

SpecPad: device-independent NMR data visualization and processing based on the novel DART programming language and Html5 Web technology.

SpecPad is a new device-independent software program for the visualization and processing of one-dimensional and two-dimensional nuclear magnetic resonance (NMR) time domain (FID) and frequency domain (spectrum) data. It is the result of a project to investigate whether the novel programming language DART, in combination with Html5 Web technology, forms a suitable base to write an NMR data evaluation software which runs on modern computing devices such as Android, iOS, and Windows tablets as well as on Windows, Linux, and Mac OS X desktop PCs and notebooks. Another topic of interest is whether this technique also effectively supports the required sophisticated graphical and computational algorithms. SpecPad is device-independent because DART's compiled executable code is JavaScript and can, therefore, be run by the browsers of PCs and tablets. Because of Html5 browser cache technology, SpecPad may be operated off-line. Network access is only required during data import or export, e.g. via a Cloud service, or for software updates. A professional and easy to use graphical user interface consistent across all hardware platforms supports touch screen features on mobile devices for zooming and panning and for NMR-related interactive operations such as phasing, integration, peak picking, or atom assignment. Copyright © 2017 John Wiley & Sons, Ltd.

Bridging experiment and theory: a template for unifying NMR data and electronic structure calculations.

BackgroundThe testing of theoretical models with experimental data is an integral part of the scientific method, and a logical place to search for new ways of stimulating scientific productivity. Often experiment/theory comparisons may be viewed as a workflow comprised of well-defined, rote operations distributed over several distinct computers, as exemplified by the way in which predictions from electronic structure theories are evaluated with results from spectroscopic experiments. For workflows such as this, which may be laborious and time consuming to perform manually, software that could orchestrate the operations and transfer results between computers in a seamless and automated fashion would offer major efficiency gains. Such tools also promise to alter how researchers interact with data outside their field of specialization by, e.g., making raw experimental results more accessible to theorists, and the outputs of theoretical calculations more readily comprehended by experimentalists.ResultsAn implementation of an automated workflow has been developed for the integrated analysis of data from nuclear magnetic resonance (NMR) experiments and electronic structure calculations. Kepler (Altintas et al. 2004) open source software was used to coordinate the processing and transfer of data at each step of the workflow. This workflow incorporated several open source software components, including electronic structure code to compute NMR parameters, a program to simulate NMR signals, NMR data processing programs, and others. The Kepler software was found to be sufficiently flexible to address several minor implementation challenges without recourse to other software solutions. The automated workflow was demonstrated with data from a ^{17}hbox {O} NMR study of uranyl salts described previously (Cho et al. in J Chem Phys 132:084501, 2010).ConclusionsThe functional implementation of an automated process linking NMR data with electronic structure predictions demonstrates that modern software tools such as Kepler can be used to construct programs that comprehensively manage complex, multi-step scientific workflows spanning several different computers. Automation of the workflow can greatly accelerate the pace of discovery, and allows researchers to focus on the fundamental scientific questions rather than mastery of specialized software and data processing techniques. Future developments that would expand the scope and power of this approach include tools to standardize data and associated metadata formats, and the creation of interactive user interfaces to allow real-time exploration of the effects of program inputs on calculated outputs.

Authentication of the origin of sucrose-based sugar products using quantitative natural abundance (13) C NMR.

Due to possible falsification of sugar cane products with cheaper alternative (sugar beet) on the market, a simple analytical methodology needs to be developed to control the authenticity of sugar products. A direct (13) C nuclear magnetic resonance (NMR) method has been validated to differentiate between sucrose-based sugar products produced from sugar beet (C3 plant) and sugar cane (C4 plant). The method is based on calculating relative (13) C content of the C1, C2, C5, and the C1, C4, C5, C6 positions of the glycosyl and fructosyl moieties of the sucrose molecule, respectively. NMR acquisition parameters and data processing have been optimized to reach a high level of intraday and interday precision (<0.2%). Good linearity (R(2) = 0.93) was obtained for the beet sugar-cane sugar blends containing from 0 to 100 wt% of beet sugar. The method was applied to ten commercial sucrose-based sugar products of different botanical origin. Principal component analysis (PCA) was applied to the relative peak areas for replicate measurements to visualize the difference between studied products. The (13) C NMR method is a good alternative to complex isotope ratio mass spectrometry measurements for routine detection and semi-quantification of adulteration of commercial cane sugar (C4 plant) with less expensive beet sugar (C3 plant). © 2015 Society of Chemical Industry.

Touch NMR: An NMR Data Processing Application for the iPad

Nuclear magnetic resonance (NMR) spectroscopy has become one of the most powerful technologies to aid research in numerous scientific disciplines. With the development of consumer electronics, mobile devices have played increasingly important roles in our daily life. However, there is currently no application available for mobile devices able to perform basic NMR data processing. In this article, we introduce Touch NMR, a free iPad application for basic NMR data processing. It is capable of handling free induction decay data in the Agilent, Bruker, and JEOL Delta formats. It can also accomplish basic NMR data operations, such as fast Fourier transform, phase correction, baseline correction, referencing, peak picking, integration, and others. Furthermore, in Touch NMR, distortionless enhancement by polarization transfer spectral editing, relaxation-time fitting, and 2D spectrum displaying with projections in two dimensions are also available. For the purpose of offering users a better experience, multitouch functionality and cloud storage are additionally provided. Finally, it is believed that Touch NMR can save considerable time for both instructors and students in tackling NMR data processing, during in-class education as well as during exams.

MetaboLab - advanced NMR data processing and analysis for metabolomics

BackgroundDespite wide-spread use of Nuclear Magnetic Resonance (NMR) in metabolomics for the analysis of biological samples there is a lack of graphically driven, publicly available software to process large one and two-dimensional NMR data sets for statistical analysis.ResultsHere we present MetaboLab, a MATLAB based software package that facilitates NMR data processing by providing automated algorithms for processing series of spectra in a reproducible fashion. A graphical user interface provides easy access to all steps of data processing via a script builder to generate MATLAB scripts, providing an option to alter code manually. The analysis of two-dimensional spectra (1H,13C-HSQC spectra) is facilitated by the use of a spectral library derived from publicly available databases which can be extended readily. The software allows to display specific metabolites in small regions of interest where signals can be picked. To facilitate the analysis of series of two-dimensional spectra, different spectra can be overlaid and assignments can be transferred between spectra. The software includes mechanisms to account for overlapping signals by highlighting neighboring and ambiguous assignments.ConclusionsThe MetaboLab software is an integrated software package for NMR data processing and analysis, closely linked to the previously developed NMRLab software. It includes tools for batch processing and gives access to a wealth of algorithms available in the MATLAB framework. Algorithms within MetaboLab help to optimize the flow of metabolomics data preparation for statistical analysis. The combination of an intuitive graphical user interface along with advanced data processing algorithms facilitates the use of MetaboLab in a broader metabolomics context.

Leak detection of complex pipelines based on the filter diagonalization method: robust technique for eigenvalue assessment

The FDM (filter diagonalization method), an interesting technique used in nuclear magnetic resonance data processing for tackling FFT (fast Fourier transform) limitations, can be used by considering pipelines, especially complex configurations, as a vascular apparatus with arteries, veins, capillaries, etc. Thrombosis, which might occur in humans, can be considered as a leakage for the complex pipeline, the human vascular apparatus. The choice of eigenvalues in FDM or in spectra-based techniques is a key issue in recovering the solution of the main equation (for FDM) or frequency domain transformation (for FFT) in order to determine the accuracy in detecting leaks in pipelines. This paper deals with the possibility of improving the leak detection accuracy of the FDM technique thanks to a robust algorithm by assessing the problem of eigenvalues, making it less experimental and more analytical using Tikhonov-based regularization techniques. The paper starts from the results of previous experimental procedures carried out by the authors.

Robust Spectral Leak Detection of Complex Pipelines Using Filter Diagonalization Method

The control and managing of pipelines have been assuming a major importance for all kinds of fluids to be conveyed through. When the fluid is like oil, harmful liquid and/or water for human beings necessity, the monitoring of pipelines becomes extremely fundamental. Based on the reflexion according to fast detecting systems, spectral analysis response is a topic of interest. Among spectral analysis response techniques, fast Fourier transform (FFT) is rated. Different other techniques are utilized, but they are costly and difficult to be used. An interesting technique, used in nuclear magnetic resonance data processing, filter diagonalization method (FDM), for tackling FFT limitations, can be used, by considering the pipeline, especially complex configurations, as a vascular apparatus with arteries, veins, capillaries, etc. The thrombosis, for human vascular apparatus, that might be occur, can be considered as a leakage for the complex pipeline. The research proposes the use of FDM according to two sub techniques called algorithm I and algorithm II. The first algorithm is a direct transformation of FDM application, while the second includes robustness and a regularization technique to solve ill-posed problems that may emerge in processing data. The results are encouraging.

Z-matrix formalism for quantitative noise assessment of covariance nuclear magnetic resonance spectra.

Due to the limited sensitivity of many nuclear magnetic resonance (NMR) applications, careful consideration must be given to the effect of NMR data processing on spectral noise. This work presents analytical relationships as well as simulated and experimental results characterizing the propagation of noise by unsymmetric covariance NMR processing, which concatenates two NMR spectra along a common dimension, resulting in a new spectrum showing spin correlations as cross peaks that are not directly measured in either of the two input spectra. It is shown how the unsymmetric covariance spectrum possesses an inhomogeneous noise distribution across the spectrum with the least amount of noise in regions whose rows and columns do not contain any cross or diagonal peaks and with the largest amount of noise on top of signal peaks. Therefore, methods of noise estimation commonly used in Fourier transform spectroscopy underestimate the amount of uncertainty in unsymmetric covariance spectra. Different data processing procedures, including the Z-matrix formalism, thresholding, and maxima ratio scaling, are described to assess noise contributions and to reduce noise inhomogeneity. In particular, determination of a Z score, which measures the difference in standard deviations of a statistic from its mean, for each spectral point yields a Z matrix, which indicates whether a given peak intensity above a threshold arises from the covariance of signals in the input spectra or whether it is likely to be caused by noise. Application to an unsymmetric covariance spectrum, obtained by concatenating two 2D (13)C-(1)H heteronuclear, single quantum coherence (HSQC) and (13)C-(1)H heteronuclear, multiple bond correlation (HMBC) spectra of a metabolite mixture along their common proton dimension, reveals that for sufficiently sensitive input spectra the reduction in sensitivity due to covariance processing is modest.

Strategy for Automated Analysis of Dynamic Metabolic Mixtures by NMR. Application to an Insect Venom

Elucidation of the composition of chemical-biological samples is a main focus of systems biology and metabolomics. Due to the inherent complexity of these mixtures, reliable, efficient, and potentially automatable methods are needed to identify the underlying metabolites and natural products. Because of its rich chemical information content, nuclear magnetic resonance (NMR) spectroscopy has a unique potential for this task. Here we present a generalization and application of a recently introduced NMR data collection, processing, and analysis strategy that circumvents the need for extensive purification and hyphenation prior to analysis. It uses covariance TOCSY NMR spectra measured on a 1-mm high-temperature cryogenic probe that are analyzed by a spectral trace clustering algorithm yielding 1D NMR spectra of the individual components for their unambiguous identification. The method is demonstrated on a metabolic model mixture and is then applied to the unpurified venom mixture of an individual walking stick insect that contains several slowly interconverting and closely related metabolites.