Independent component analysis applied to diffusion‐ordered spectroscopy: separating nuclear magnetic resonance spectra of analytes in mixtures

Nuclear magnetic resonance spectrum of a mixture contains the overall peaks of all the analytes. It is impossible to perform structural assignment on the mixture without the knowledge of individual spectra of the components. Spectral separation is thus an important means of teasing out pure components of a mixture before spectral assignment. We propose a strategy called diffusion‐ordered independent component analysis (DIFFICA) to achieve this task. This strategy applies independent component analysis algorithms to diffusion‐ordered spectroscopy (DOSY) to extract spectra of pure components in a mixture. DIFFICA was tested in a simulation and experimentally in two three‐component systems with and without water suppression, in 1D and 2D DOSY data. Pure spectra were achieved in both cases. The selection of diffusion parameters to guarantee pure spectra is guided by the distance correlation between separated spectra. Copyright © 2012 John Wiley & Sons, Ltd.     

Size‐exclusion chromatographic NMR of polymer mixtures

The use of chromatographic stationary phases or solvent modifiers to modulate diffusion properties in NMR experiments is now well established. Their use can be to improve resolution in the diffusion domain or to provide an insight into analyte–modifier interactions and, hence, the chromatography process. Here, we extend previous work using size‐exclusion chromatographic stationary phases to the investigation of polymer mixtures. We demonstrate that similar diffusion modulation behaviour is observed with a size‐exclusion chromatographic stationary phase that can be understood in terms of size‐exclusion behaviour. Copyright © 2014 John Wiley & Sons, Ltd.     

Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis

The paper presents sparse component analysis (SCA)‐based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on ?₁ norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds. Copyright © 2009 John Wiley & Sons, Ltd.     

Improved excitation uniformity in multiple‐quantum NMR experiments of mixtures

Multiple‐quantum ¹H NMR spectroscopy has been finding a renewed interest for its possible applications in the analysis of mixtures of small molecules, due to its simplification properties. A crucial aspect of this application of multiple‐quantum NMR is the sensitivity of the spectrum intensity to the molecular structure and to the parameterization of the experiment, which could result in the missing of some components. We demonstrate that a general scheme to overcome this drawback consists in varying the experiment parameterizations over a small number of values, selected according the values of the couplings and the relaxation rates. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

A post-modification approach to independent compo-nent analysis for resolution of overlapping GC/MS signals: from independent components to chemical components

Independent component analysis (ICA) has demonstrated its power to extract mass spectra from over-lapping GC/MS signal. However, there is still a problem that mass spectra with negative peaks at some m/z will be obtained in the resolved results when there are overlapping peaks in the mass spectra of a mixture. Based on a detail theoretical analysis of the preconditions for ICA and the non-negative property of GC/MS signals, a post-modification based on chemical knowledge (PMBK) strategy is pro-posed to solve this problem. By both simulated and experimental GC/MS signals, it was proved that the PMBK strategy can improve the resolution effectively.     

One‐dimensional TOCSY 1H NMR experiments on adducts of amino acid esters with cobalt(III) tetramethylchiroporphyrin. Prospects for the analysis of amino acid mixtures

Cobalt(III) tetramethylchiroporphyrin, CoCl(TMCP), is a useful chiral shift reagent for structure attribution, absolute configuration assignment and enantiomeric excess determination of amino acid methyl esters by ¹H NMR spectroscopy. However, it has two axial sites available for amine coordination, a structural feature which generates n(n + 1)/2 diastereomeric species and n² distinct spin systems from a mixture of n amino ester enantiomers, making the analysis of complex amino acid samples exceedingly difficult by classical 1‐D or 2‐D NMR methods when n > 3. The 1‐D TOCSY experiment is shown to be a powerful tool for the selective excitation and detection of every single component of a mixture of four amino acid methyl esters bound to CoCl(TMCP): those of(S)‐Leu, (S)‐Asp, (R)‐Asp and (S)‐Glu, for example. The potential utility of this methodology for the determination of amino acid enantiomers in carbonaceous meteorites or other extraterrestrial samples is suggested. Copyright © 2002 John Wiley & Sons, Ltd.     

Synthesis and NMR spectral assignments of indol‐3‐yl pyridines through one‐pot multi‐component reaction

Asimple protocol for the efficient preparation of 6‐(ferrocene‐1‐yl)‐2‐(indol‐3‐yl)pyridine and 2‐(1H‐indol‐3‐yl)‐6‐(2‐thienyl)pyridine derivatives has been achieved through multi‐component reaction, and these compounds were thoroughly characterised by 2D NMR spectral techniques. Copyright © 2010 John Wiley & Sons, Ltd.     

Studies on crude oil‐water biphasic mixtures by low‐field NMR

Low‐field ¹H NMR was used in this work for the analysis of mixtures involving crude oils and water. CPMG experiments were performed to determine the transverse relaxation time (T₂) distribution curves, which were computed by the inverse Laplace transform of the echo decay data. The instrument's ability of quantifying water and petroleum in biphasic mixtures following different methodologies was tested. For mixtures between deionized water and petroleum, one achieved excellent results, with root mean squared error of cross‐validation (RMSECV) of 0.8% for a regression between the water content (wt %) and the relative area of the water peak in the T₂ distribution curve, or a standard deviation of 0.9% for the relationship between the water content and the relative water peak area, corrected by the relative hydrogen index of the crude. In the case of biphasic mixtures of Mn²⁺‐doped water and crude oils, the best result of RMSECV = 1.6% was achieved by using the raw magnetization decay data for a partial least squares regression. Copyright © 2012 John Wiley & Sons, Ltd.     

Cross validation and uncertainty estimates in independent component analysis

A data analysis tool, known as independent component analysis (ICA), is the main focus of this paper. The theory of ICA is briefly reviewed, and the underlying statistical assumptions and a practical algorithm are described. This paper introduces cross validation/jack-knifing and significance tests to ICA. Jack-knifing is applied to estimate uncertainties for the ICA loadings, which also serve as a basis for significance tests. These tests are shown to improve ICA performance, indicating how many components are mixed in the observed data, and also which parts of the extracted sources that contain significant information. We address the issue of stability for the ICA model through uncertainty plots. The ICA performance is compared to principal component analysis (PCA) for two selected applications, a simulated experiment and a real world application.     

Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools

A 400‐MHz ¹H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non‐Basmati long‐grain rice, and round‐grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS‐DA), soft independent modeling of class analogy, and ICA), PLS‐DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS‐DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Disentangling Complex Mixtures of Compounds with Near‐Identical 1H and 13C NMR Spectra using Pure Shift NMR Spectroscopy

The thorough analysis of highly complex NMR spectra using pure shift NMR experiments is described. The enhanced spectral resolution obtained from modern 2D HOBS experiments incorporating spectral aliasing in the ¹³C indirect dimension enables the distinction of similar compounds exhibiting near‐identical ¹H and ¹³C NMR spectra. It is shown that a complete set of extremely small Δδ(¹H) and Δδ(¹³C) values, even below the natural line width (1 and 5 ppb, respectively), can be simultaneously determined and assigned.     

Synthesis and 1H and 13C NMR structural analysis of cis‐ and trans‐2‐imino‐1,3‐ and ‐3,1‐perhydrobenzoxazines and their 3‐ and 1‐N‐methyl derivatives

cis‐ and trans‐2‐imino‐1,3‐ and ‐3,1‐perhydrobenzoxazines and the N‐methyl derivatives of the latter were synthesized from the corresponding cyclic 1,3‐amino alcohol with cyanogen bromide. The configurations of the studied compounds were confirmed by ¹H and ¹³C NMR spectra. All trans‐fused compounds exist in biased chair–chair conformations as expected, whereas the cis‐fused 1,3‐benzoxazines attain exclusively the O‐in conformations. The cis‐fused 3,1‐benzoxazines, especially the 1‐methyl‐substituted derivatives, tend to favor the N‐out form, obviously owing to the favorable axial orientation of this N‐methyl. Copyright © 2003 John Wiley & Sons, Ltd.     

Multilevel component analysis of time-resolved metabolic fingerprinting data

Genomics-based technologies in systems biology have gained a lot of popularity in recent years. These technologies generate large amounts of data. To obtain information from this data, multivariate data analysis methods are required. Many of the datasets generated in genomics are multilevel datasets, in which the variation occurs on different levels simultaneously (e.g. variation between organisms and variation in time). We introduce multilevel component analysis (MCA) into the field of metabolic fingerprinting to separate these different types of variation. This is in contrast to the commonly used principal component analysis (PCA) that is not capable of doing this: in a PCA model the different types of variation in a multilevel dataset are confounded.

MCA generates different submodels for different types of variation. These submodels are lower-dimensional component models in which the variation is approximated. These models are easier to interpret than the original data. Multilevel simultaneous component analysis (MSCA) is a method within the class of MCA models with increased interpretability, due to the fact that the time-resolved variation of all individuals is expressed in the same subspace.

MSCA is applied on a time-resolved metabolomics dataset. This dataset contains ¹H NMR spectra of urine collected from 10 monkeys at 29 time-points during 2 months. The MSCA model contains a submodel describing the biorhythms in the urine composition and a submodel describing the variation between the animals. Using MSCA the largest biorhythms in the urine composition and the largest variation between the animals are identified.

Comparison of the MSCA model to a PCA model of this data shows that the MSCA model is better interpretable: the MSCA model gives a better view on the different types of variation in the data since they are not confounded.     

Proton high‐field NMR study of tomato juice

A detailed analysis of the proton high‐field (600 MHz) NMR spectra of tomato juice and pulp is reported for the first time. A combination of J‐resolved, COSY, TOCSY, DOSY, ¹H–¹³C HSQC and ¹H–¹³C HMBC 2D sequences was used to assign each spin system and to separate the components of the complex patterns in the 1D overlapped proton spectra. To obtain resolved proton spectra of tomato pulps the high‐resolution magic angle spinning technique was used; a comparison with the liquid‐state NMR spectra of the corresponding juices was accomplished. On the basis of the assignments made, the chemical composition of tomato juices from two cultivars (Red Setter and Ciliegino) was determined. Copyright © 2003 John Wiley & Sons, Ltd.     

Construction of stable multivariate calibration models using unsupervised segmented principal component regression

In multivariate spectral calibration by principal component regression (PCR), the principal components (PCs) are calculated from the response data measured at all employed instrument channels; however some channels are redundant and their responses do not possess useful information. Thus, the extracted PCs possess mixed information from both useful and redundant channels. In this work, we propose a segmentation approach based on unsupervised pattern recognition to identify the most informative spectral region and then to construct a stable multivariate calibration model by PCR. In this method, the instrument channels are clustered into different segments via Kohonen self‐organization map. The spectral data of each segment are then subjected to PCA and the derived PCs are used as input variables for an inverse least square (ILS) regression model employing stepwise selection of the informative PCs. The proposed method was evaluated by the analysis of four simulated and six experimental data sets. It was found that our proposed method can model the above data sets with prediction errors lower than conventional partial least squares (PLS) and PCR methods. In addition, the prediction ability of our method was better than the previously reported models for these data sets. Copyright © 2011 John Wiley & Sons, Ltd.     

Spectroscopic separation of 13C NMR spectra of complex isomeric mixtures by the CSSF‐TOCSY‐INEPT experiment

Isomeric mixtures from synthetic or natural origins can pose fundamental challenges for their chromatographic separation and spectroscopic identification. A novel 1D selective NMR experiment, chemical shift selective filter (CSSF)‐TOCSY‐INEPT, is presented that allows the extraction of ¹³C NMR subspectra of discrete isomers in complex mixtures without physical separation. This is achieved via CSS excitation of proton signals in the ¹H NMR mixture spectrum, propagation of the selectivity by polarization transfer within coupled ¹H spins, and subsequent relaying of the magnetization from ¹H to ¹³C by direct INEPT transfer to generate ¹³C NMR subspectra. Simple consolidation of the subspectra yields ¹³C NMR spectra for individual isomers. Alternatively, CSSF‐INEPT with heteronuclear long‐range transfer can correlate the isolated networks of coupled spins and therefore facilitate the reconstruction of the ¹³C NMR spectra for isomers containing multiple spin systems. A proof‐of‐principle validation of the CSSF‐TOCSY‐INEPT experiment is demonstrated on three mixtures with different spectral and structural complexities. The results show that CSSF‐TOCSY‐INEPT is a versatile, powerful tool for deconvoluting isomeric mixtures within the NMR tube with unprecedented resolution and offers unique, unambiguous spectral information for structure elucidation. Copyright © 2014 John Wiley & Sons, Ltd.     

Blind decomposition of low‐dimensional multi‐spectral image by sparse component analysis

A multilayer hierarchical alternating least square nonnegative matrix factorization approach has been applied to blind decomposition of low‐dimensional multi‐spectral image. The method performs blind decomposition exploiting spectral diversity and spatial sparsity between materials present in the image and, unlike many blind source separation methods, is invariant with respect to statistical (in)dependence among spatial distributions of the materials. As opposed to many existing blind source separation algorithms, the method is capable of estimating the unknown number of materials present in the image. This number can be less than, equal to, or greater than the number of spectral bands. The method is validated on underdetermined blind source separation problems associated with blind decomposition of experimental red‐green‐blue images composed of four materials. Achieved performance has been superior when compared against methods based on minimization of the ℓ₁‐norm: linear programming and interior‐point methods. In addition to tumor demarcation, as demonstrated in the paper, other areas that can also benefit from the proposed method include cell, chemical, and tissue imaging. Copyright © 2009 John Wiley & Sons, Ltd.     

NMR analysis of multiple samples using parallel coils: improved performance using reference deconvolution and multidimensional methods

Improvements in the Multiplex Sample NMR method are investigated to explore its capabilities of analyzing multiple samples simultaneously. Issues of quantitation and resolution in the multiple-coil probe are examined in one- and two-dimensional experiments. Improvements in quantitation are shown to result from the use of reference deconvolution for one-dimensional experiments, while the use of two-dimensional methods has much improved resolution and shows the potential for significantly increased parallelism. A multiplicative scheme is shown to be an easily implemented, effective method for generating individual sub-spectra from individual samples.     

1H, 13C and 15N NMR spectral analysis of substituted 1,2,3,4‐tetrahydro‐pyrido[1,2‐a]pyrimidines

The NMR spectroscopic data of a series of thirty‐four 3‐acylpyrido[1,2‐a]pyrimidinium salts are analyzed, which were prepared as either perchlorates or chlorides. Methyl group substituted 3‐aroyltetrahydropyrido[1,2‐a]pyrimidines with the methyl substituent in positions 6, 8 and 9 as well as both in positions 6 and 8 were investigated bearing various aroyl substituents. Unequivocal assignment of all resonances was achieved via two‐dimensional ¹H,¹H‐COSY measurements, ¹H,¹³C and ¹H,¹⁵N HSQC as well as HMBC experiments, and important diagnostic CH and NH couplings in the heteroaromatic ring system are evaluated. The influence of the methyl substituents was analyzed on the proton, carbon and nitrogen shifts. A significant effect of the counter ion on some chemical shifts of the nuclei under discussion of the pyridopyrimidines is found, allowing the indirect detection of the anion, which is confirmed by direct measurement of the ³⁵Cl nucleus of the perchlorates. Copyright © 2013 John Wiley & Sons, Ltd.