All-atom Molecular Dynamic Simulations and NMR Spectra Study on Intermolecular Interactionsof N,N-dimethylacetamide-Water System

N,N-dimethylacetamide (DMA) has been investigated extensively in studying models of peptide bonds. An all-atom MD simulation and the NMR spectra were performed to investigate the interactions in the DMA-water system. The radial distribution functions (RDFs) and the hydrogen-bonding network were used in MD simulations. There are strong hydrogen bonds and weak C-H￠ ￠ ￠O contacts in the mixtures, as shown by the analysis of the RDFs. The insight structures in the DMA-water mixtures can be classified into different regions by the analysis of the hydrogen-bonding network. Chemical shifts of the hydrogen atom of water molecule with concentration and temperatures are adopted to study the interactions in the mixtures. The results of NMR spectra show good agreement with the statistical results of hydrogen bonds in MD simulations.     

A new democratic phase coherent data-scatter technique for calibration, measurement, fingerprinting and rapid archival identification of ultraviolet-visible multi-component food spectra

The new theory of democratic phase coherent data-scatter (DPCD-S) is introduced. Basics of UV-visible spectrometry theory and error propagation have been presented. The qualitative spectral analysis provided is point-by-point over the complete data set and not just limited to Lambda-maxima. Equal weightings of the ‘voting’ data scattering algorithm are employed in the analysis of both the calibration and food colour data and this is consequently the democratic algorithm. The paper shows how the technique can be used with UV-visible standards to analyse the wavelength and photometric calibration of a spectrophotometer. The main results relate to the analysis of a series of spectra taken on complex mixtures of three important food dyes and their quantitative analysis using the phase coherent data-scatter technique. This method is shown to offer new possibilities for identifying and archiving UV-visible spectra from a single point in a transform space. Complex spectra can therefore be represented by a single point in this transform space, which is weighted by the ‘votes’ of all the data points in the complex data set. The software allows the user to interrogate the scatter results and locate the scatter point to the specific spectral positions. A new mathematical operator has been introduced to resolve any possible coincidence of two spectral projection points. Analysis of two close spectra from very-different admixtures of food colours shows powerfully the utility of this operator. Error propagation severely limits the accuracy of the usual UV-method of simultaneous equation secondary mixtures analysis.     

Local covariance order diffusion-ordered spectroscopy: a powerful tool for mixture analysis

Diffusion-ordered spectroscopy (DOSY) is an important tool in NMR mixture analysis that has found use in most areas of chemistry, including organic synthesis, drug discovery, and supramolecular chemistry. Typically the aim is to disentangle the overlaid, and often overlapped, NMR spectra of individual mixture components and/or to obtain size and interaction information from their respective diffusion coefficients. The most common processing method, high-resolution DOSY, breaks down where component spectra overlap; here multivariate methods can be very effective, but only for small numbers (2-5) of components. In this study, we present a hybrid method, local covariance order DOSY (LOCODOSY), that breaks a spectral data set into suitable windows and analyzes each individually before combining the results. This approach uses a multivariate algorithm (e.g., SCORE or DECRA) to resolve only a small number of components in any given window. Because a small spectral region should contain signals from only a few components, even when the spectrum as a whole contains many more, the total number of resolvable chemical components rises dramatically. It is demonstrated here that complete resolution of component spectra can be achieved for mixtures that are much more complex than could previously be analyzed with DOSY. Thus, LOCODOSY is a powerful, flexible tool for processing NMR diffusion data of complex mixtures.     

Application of a new method for simultaneous phase and baseline correction of NMR signals (SINC)

Recently, we presented a new approach for simultaneous phase and baseline correction of nuclear magnetic resonance (NMR) signals (SINC) that is based on multiobjective optimization. The algorithm can automatically correct large sets of NMR spectra, which are commonly acquired when reactions and processes are monitored with NMR spectroscopy. The aim of the algorithm is to provide spectra that can be evaluated quantitatively, for example, to calculate the composition of a mixture or the extent of reaction. In this work, the SINC algorithm is tested in three different studies. In an in-house comparison study, spectra of different mixtures were corrected both with the SINC method and manually by different experienced users. The study shows that the results of the different users vary significantly and that their average uncertainty of the composition measurement is larger than the uncertainty obtained when the spectra are corrected with the SINC method. By means of a dilution study, we demonstrate that the SINC method is also applicable for the correction of spectra with low signal-to-noise ratio. Furthermore, a large set of NMR spectra that was acquired to follow a reaction was corrected with the SINC method. Even in this system, where the areas of the peaks and their chemical shifts changed during the course of reaction, the SINC method corrected the spectra robustly. The results show that this method is especially suited to correct large sets of NMR spectra and it is thus an important contribution for the automation of the evaluation of NMR spectra.     

icoshift: An effective tool for the alignment of chromatographic data

The Interval Correlation Optimised Shifting algorithm (icoshift) has recently been introduced for the alignment of nuclear magnetic resonance spectra. The method is based on an insertion/deletion model to shift intervals of spectra/chromatograms and relies on an efficient Fast Fourier Transform based computation core that allows the alignment of large data sets in a few seconds on a standard personal computer. The potential of this programme for the alignment of chromatographic data is outlined with focus on the model used for the correction function. The efficacy of the algorithm is demonstrated on a chromatographic data set with 45 chromatograms of 64,000 data points. Computation time is significantly reduced compared to the Correlation Optimised Warping (COW) algorithm, which is widely used for the alignment of chromatographic signals. Moreover, icoshift proved to perform better than COW in terms of quality of the alignment (viz. of simplicity and peak factor), but without the need for computationally expensive optimisations of the warping meta-parameters required by COW. Principal component analysis (PCA) is used to show how a significant reduction on data complexity was achieved, improving the ability to highlight chemical differences amongst the samples.     

Application of quantitative artificial neural network analysis to 2D NMR spectra of hydrocarbon mixtures

Understanding relationships between the structure and composition of molecular mixtures and their chemical properties is a main industrial aim. One central field of research is oil chemistry where the key question is how the molecular characteristics of composite hydrocarbon mixtures can be associated with the macroscopic properties of the oil products. Apparently these relationships are complex and often nonlinear and therefore call for advanced spectroscopic techniques. An informative and an increasingly used approach is two-dimensional nuclear magnetic resonance (2D NMR) spectroscopy. In the case of composite hydrocarbons the application of 2D NMR methodologies in a quantitative manner pose many technical difficulties, and, in any case, the resulting spectra contain many overlapping resonances that challenge the analytical work. Here, we present a general methodology, based on quantitative artificial neural network (ANN) analysis, to resolve overlapping information in 2D NMR spectra and to simultaneously assess the relative importance of multiple spectral variables on the sample properties. The results in a set of 2D NMR spectra of oil samples illustrate, first, that use of ANN analysis for quantitative purposes is feasible also in 2D and, second, that this methodology offers an intrinsic opportunity to assess the complex and nonlinear relationships between the molecular composition and sample properties. The presented ANN methodology is not limited to the analysis of NMR spectra but can also be applied in a manner similar to other (multidimensional) spectroscopic data.     

Gas chromatography/flame ionisation detection mass spectrometry for the detection of endogenous urine metabolites for metabonomic studies and its use as a complementary tool to nuclear magnetic resonance spectroscopy

Metabonomics is a relatively new field of research in which the total pool of metabolites in body fluids or tissues from different patient groups is subjected to comparative analysis. Nuclear magnetic resonance (NMR) spectroscopy is the technology that is currently most widely used for the analysis of these highly complex metabolite mixtures, and hundreds of metabolites can be detected without any upfront separation. We have investigated in this study whether gas chromatography (GC) separation in combination with flame ionisation detection (FID) and mass spectrometry (MS) detection can be used for metabolite profiling from urine. We show that although GC sample preparation is much more involved than for NMR, hundreds of metabolites can reproducibly be detected and analysed by GC. We show that the data quality is sufficiently high--particularly if appropriate baseline correction and time-warping methods are applied--to allow for data comparison by chemometrics methods. A sample set of urines from eleven healthy human volunteers was analysed independently by GC and NMR, and subsequent chemometrics analysis of the two datasets showed some similar features. As judged by NIST database searches of the GC/MS data some of the major metabolites that are detected by NMR are also visible by GC/MS. Since in contrast to NMR every peak in GC corresponds to a single metabolite, the electron ionisation spectra can be used to quickly identify metabolites of interest if their reference spectra are present in a searchable database. In summary, we show that GC is a method that can be used as a complementary tool to NMR for metabolite profiling of urine samples.     

Weighted two-band target entropy minimization for the reconstruction of pure component mass spectra: Simulation studies and the application to real systems

A method is proposed, on the basis of a recently developed algorithm--Band Target Entropy Minimization (BTEM)--to reconstruct mass spectra of pure components from mixture spectra. This method is particular useful in dealing with spectral data with discrete features (like mass spectra). Compared to the original BTEM, which has been applied to differentiable spectroscopies such as Fourier-transfer infrared spectroscopy (FTIR), ultraviolet (UV), Raman, and nuclear magnetic resonance (NMR), the latest modifications were obtained through: (1) Reformulating the objective function using the peak heights instead of their derivatives; (2) weighting the abstract vector VT to reduce the effect of noise; (3) using a two-peak targeting strategy (tBTEM) to deal with strongly overlapping peaks; and (4) using exhaustive search to locate all the component spectra. A set of 50 multi-component mass spectra was generated from ten reference experimental pure component spectra. Many of the compounds chosen have common MS fragments and therefore, many of the pure component spectra have considerable intensity in same data channels. In addition, a set of MS spectra from a real system with four components was used to examine the newly developed algorithm. Successful reconstruction of the ten component spectra of the simulated system and the four component spectra of the real system was rapidly achieved using the new tBTEM algorithm. The advantages of the new algorithm and its implication for rapid system identification of unknown mixtures are readily apparent.     

Automatic assignment of 1H‐NMR spectra of small molecules

A novel data‐evaluation procedure for the automatic atom to peak or multiplet assignment of ¹H‐NMR spectra of small molecules has been developed using a fast and robust expert system. The applicability and reliability of the method are demonstrated by comparison of a manually assigned database of ¹H‐NMR spectra with the assignments produced by the automatic procedure. The results of this analysis show an excellent success ratio, indicating that this new algorithm can have a major impact as a time saving tool for the organic chemist. A new graphical feature used to illustrate both the stability and quality of the elementary assignments is also introduced. Copyright © 2013 John Wiley & Sons, Ltd.     

Determination of nicotine in tobacco with second-order spectra data of charge-transfer complex in ethanol–water binary solvents processed by parallel factor analysis

A new spectrophotometric method for the determination of nicotine in mixtures without pre-separation has been proposed. Nicotine could react with 2,4-dinitrophenol through a charge-transfer reaction to form a colored complex. The second-order data from the visible absorption spectra of the complex in a series of ethanol–water binary solvents with various water volume fractions could be expressed as the combination of two bilinear data matrices. With the bilinear model, the second-order spectra data of mixtures containing nicotine and other interferents could be analysed by using second-order calibration algorithms, and the determination of nicotine in the mixtures could be achieved. The algorithm used here was parallel factor analysis. The method has been successfully used to determine nicotine in tobacco samples with satisfactory results.     

A robust clustering approach for NMR spectra of natural product extracts

A robust method was developed to cluster similar NMR spectra from partially purified extracts obtained from a range of marine sponges and a plant biota. The NMR data were acquired using microtiter plate NMR (VAST) in protonated solvents. A sample data set which contained several clusters was used to optimize the protocol. The evaluation of the robustness was performed using three different clustering methods: tree clustering analysis, K-means clustering and multidimensional scaling. These methods were compared for consistency using the sample data set and the optimized methodology was applied to clustering of a set of spectra from partially purified biota extracts.     

Resolving the problem of chromatographic overlap by 3D cross correlation (3DCC) processing of LC, MS and NMR data for characterization of complex glycan mixtures

Chromatographic overlap is a common problem in the analysis of complex mixtures. As a result, it is not possible to identify the components because each resulting NMR or MS spectrum contains multiple components. We introduce three-dimensional cross correlation (3DCC) that dissects NMR spectra of a mixture into spectra of the individual components without actually separating them. Correlation of peaks from MS and NMR profiles along a common LC time domain yields 3DCC NMR spectra of pure components correlated with a mass and a retention time. The method requires an LC run followed by fractionation and recording of MS and NMR spectra. The method is applicable to mixtures of any classes of molecules. Here, we demonstrate its application to a mixture of complex glycans obtained from a glycoprotein. Fourteen glycans eluting within only 3 min showed heavy overlap in the chromatographic run. 3DCC allowed their direct characterization without separation. Some of these structures from the glycoprotein bovine fibrinogen had not previously been described. The 3DCC procedure has been implemented in standard software. Actually, 3DCC can be used for any combination of separation techniques, like LC or GC, combined with two characterization methods like UV, IR, Raman, NMR or MS.     

Quantification of casein phosphorylation with conformational interpretation using Raman spectroscopy

Raman spectroscopy is emerging as a powerful method for obtaining both quantitative and qualitative information from biological samples. One very interesting area of research, for which the technique has rarely been used, is the detection, quantification and structural analysis of post-translational modifications (PTMs) on proteins. Since Raman spectra can be used to address both of these questions simultaneously, we have developed near infrared Raman spectroscopy with appropriate chemometric approaches (partial least squares regression) to quantify low concentration (4 microM) mixtures of phosphorylated and dephosphorylated bovine alpha(s)-casein. In addition, we have used these data in conjunction with Raman optical activity (ROA) spectra and NMR to assess the structural changes that occur upon phosphorylation.     

Data processing in on-line laser mass spectrometry of inorganic, organic, or biological airborne particles

A general solution for data processing of large numbers of micrometer- or submicrometer-particle mass spectra in aerosol analysis is described. The method is based on immediate evaluation of bipolar laser desorption ionization mass spectra acquired in an on-line (impact-free) time-of-flight instrument. The goal of the procedure is a characterization of the particle population under investigation in terms of chemical composition of particle classes, particle distributions, size distributions, and time courses, rather than an investigation of each individual particle. After automatic peak analysis of each newly acquired bipolar mass spectrum, the mass spectral information is statistically evaluated by a fuzzy clustering algorithm, providing for an immediate attribution of the particle to predefined particle classes. The particle distributions over these classes can be monitored as a function of time and particle size range. Definition of the particle classes as used for on-line evaluation is performed in an earlier step, either by manual approach, or by selection from a particle class database, or, as in most cases, by fuzzy clustering of a set of particle mass spectra from the population (the aerosol) under investigation. Definition of the particle classes is depending only on the distinguishability of the spectra patterns of different particles. It is not necessary for the clustering approach to fully “understand” the mass spectra. The range of possible applications of the method is therefore very broad. Particles dominated by inorganic components, as typically observed in aerosol chemistry for example, can be investigated the same way as organic particles (e.g., from smoke or automobile exhaust) or even biological particles such as bacteria, yeast, or pollen. The data processing method has been successfully applied in several fields of stationary applications and will be employed in mobile instruments for large scale field studies in atmospheric chemistry, engine combustion research, and the characterization of house dust.     

Characterization of the measurement error structure in 1D H NMR data for metabolomics studies

NMR-based metabolomics is characterized by high throughput measurements of the signal intensities of complex mixtures of metabolites in biological samples by assaying, typically, bio-fluids or tissue homogenates. The ultimate goal is to obtain relevant biological information regarding the dissimilarity in patho-physiological conditions that the samples experience. For a long time now, this information has been obtained through the analysis of measured NMR signals via multivariate statistics.NMR data are quite complex and the use of such multivariate statistical methods as principal components analysis (PCA) for their analysis assumes that the data are multivariate normal with errors that are identical, independent and normally distributed (i.e. iid normal). There is a consensus that these assumptions are not always true for these data and, thus, several methods have been devised to transform the data or weight them prior to analysis by PCA. The structure of NMR measurement noise, or the extent to which violations of error homoscedasticity affect PCA results have neither been characterized nor investigated.A comprehensive characterization of measurement uncertainties in NMR based metabolomics was achieved in this work using an experiment designed to capture contributions of several sources of error to the total variance in the measurements. The noise structure was found to be heteroscedastic and highly correlated with spectral characteristics that are similar to the mean of the spectra and their standard deviation. A model was subsequently developed that potentially allows errors in NMR measurements to be accurately estimated without the need for extensive replication.     

Single-Scan Diffusion-Ordered NMR Spectroscopy of SABRE-Hyperpolarized Mixtures

The analysis of complex mixtures of dissolved molecules is a major challenge, especially for systems that gradually evolve, e. g., in the course of a chemical reaction or in the case of chemical instability. 1D NMR is a fast and non-invasive method suitable for detailed molecular analysis, though of low sensitivity. Moreover, the spectral resolution of proton, the most commonly used and most sensitive stable isotope in NMR, is also quite limited. Spatially encoded (SPEN) experiments aim at creating in one acquisition a 2D data set by simultaneously performing different 1D sub-experiments on different slices of the NMR tube, at the price of an extra loss of sensitivity. Choosing translational diffusion coefficients as the additional dimension (the so-called DOSY approach) helps to recover proton spectra of each molecule in a mixture. The sensitivity limitation of SPEN NMR can, on the other hand, be addressed with hyperpolarization methods. Within hyperpolarization methods, signal amplification by reversible exchange (SABRE), based on parahydrogen, is the cheapest and the easiest one to set up, and allows multi-shot experiments. Here we show that the spectra of a mixture's components at millimolar concentration are resolved in few seconds by combining the SABRE, SPEN and DOSY concepts.     

Simultaneous determination of penicillin and ampicillin by spectral fibre-optical enzyme optodes and multivariate data analysis based on transient signals obtained by flow injection analysis

A multicomponent detection system using optical biosensors and flow injection analysis is described. The analysis of mixtures containing penicillin and ampicillin was realised by evaluating dynamic measurements of Phenol Red spectra in penicillinase optodes in combination with a diode array spectrometer. A variety of optodes has been produced by changing the composition of the receptor gel and the working pH. A set of characteristic quantities (describing dynamic and static features) could be obtained for each optode. These were used to compare the predictivity of classical multivariate calibration methods as well as of an artificial neural network. In addition, different algorithms were applied for the evaluation of the spectral data in order to select the most appropriate method for feature extraction. In consequence, the information obtained from the multivariate calibration models was used to set up an optimal sensor array consisting of four optodes with different types of penicillinase at different working pH.     

Extraction of multiple pure component H and C NMR spectra from two mixtures: Novel solution obtained by sparse component analysis-based blind decomposition

Sparse component analysis (SCA) is demonstrated for blind extraction of three pure component spectra from only two measured mixed spectra in ¹³C and ¹H nuclear magnetic resonance (NMR) spectroscopy. This appears to be the first time to report such results and that is the first novelty of the paper. Presented concept is general and directly applicable to experimental scenarios that possibly would require use of more than two mixtures. However, it is important to emphasize that number of required mixtures is always less than number of components present in these mixtures. The second novelty is formulation of blind NMR spectra decomposition exploiting sparseness of the pure components in the wavelet basis defined by either Morlet or Mexican hat wavelet. This enabled accurate estimation of the concentration matrix and number of pure components by means of data clustering algorithm and pure components spectra by means of linear programming with constraints from both ¹H and ¹³C NMR experimental data. The third novelty is capability of proposed method to estimate number of pure components in demanding underdetermined blind source separation (uBSS) scenario. This is in contrast to majority of the BSS algorithms that assume this information to be known in advance. Presented results are important for the NMR spectroscopy-associated data analysis in pharmaceutical industry, medicine diagnostics and natural products research.     

Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis: application to foods and related products

The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non‐negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple‐to‐use‐interactive self‐modeling mixture analysis (SIMPLISMA) and multivariate curve resolution‐alternating least squares (MCR‐ALS)] are applied for simultaneous ¹H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight‐component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography–mass spectrometry as well as the MCR‐ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures. Copyright © 2014 John Wiley & Sons, Ltd.     

Structures and Intermolecular Interactions in Dimethyl Sulfoxide-Water System Studied by All-atom Molecular Simulations

An all-atom dimethyl sulfoxide (DMSO) and water model have been used for molecular dy-namics simulation. The NMR and IR spectra are also performed to study the structures and interactions in the DMSO-water system. And there are traditional strong hydrogen bondsand weak C—H…O contacts existing in the mixtures according to the analysis of the radial distribution functions. The insight structures in the DMSO-water mixtures can be classified into different regions by the analysis of the hydrogen-bonding network. Interestingly, the molar fraction of DMSO 0.35 is found to be a special concentration by the network. It is the transitional region which is from the water rich region to the DMSO rich region. The sta-ble aggregates of (DMSO)_m·S=O…HW—OW·(H₂O)_n might play a key role in this region.Moreover, the simulation is compared with the chemical shifts in NMR and wavenumbers in IR with concentration dependence. And the statistical results of the average number hydrogen bonds in the MD simulations are in agreement with the experiment data in NMR and IR spectra.