A review of blind source separation in NMR spectroscopy

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Multivariate Curve Resolution for 2D Solid-State NMR spectra

We present a processing method, based on the multivariate curve resolution approach (MCR), to denoise 2D solid-state NMR spectra, yielding a substantial S/N ratio increase while preserving the lineshapes and relative signal intensities. These spectral features are particularly important in the quantification of silicon species, where sensitivity is limited by the low natural abundance of the ²⁹Si nuclei and by the dilution of the intrinsic protons of silica, but can be of interest also when dealing with other intermediate-to-low receptivity nuclei. This method also offers the possibility of coprocessing multiple 2D spectra that have the signals at the same frequencies but with different intensities (e.g.: as a result of a variation in the mixing time). The processing can be carried out on the time-domain data, thus preserving the possibility of applying further processing to the data. As a demonstration, we have applied Cadzow denoising on the MCR-processed FIDs, achieving a further increase in the S/N ratio and more effective denoising also on the transients at longer indirect evolution times. We have applied the combined denoising on a set of experimental data from a lysozyme-silica composite.

Matrix-assisted DOSY

The analysis of mixtures by NMR spectroscopy is challenging. Diffusion-ordered NMR spectroscopy enables a pseudo-separation of species based on differences in their translational diffusion coefficients. Under the right circumstances, this is a powerful technique; however, when molecules diffuse at similar rates separation in the diffusion dimension can be poor. In addition, spectral overlap also limits resolution and can make interpretation challenging. Matrix-assisted diffusion NMR seeks to improve resolution in the diffusion dimension by utilising the differential interaction of components in the mixture with an additive to the solvent. Tuning these matrix-analyte interactions allows the diffusion resolution to be optimised. This review presents the background to matrix-assisted diffusion experiments, surveys the wide range of matrices employed, including chromatographic stationary phases, surfactants and polymers, and demonstrates the current state of the art.

Library-assisted nonlinear blind separation and annotation of pure components from a single 1H nuclear magnetic resonance mixture spectra

Due to its capability for high-throughput screening ¹H nuclear magnetic resonance (NMR) spectroscopy is commonly used for metabolite research. The key problem in ¹H NMR spectroscopy of multicomponent mixtures is overlapping of component signals and that is increasing with the number of components, their complexity and structural similarity. It makes metabolic profiling, that is carried out through matching acquired spectra with metabolites from the library, a hard problem. Here, we propose a method for nonlinear blind separation of highly correlated components spectra from a single ¹H NMR mixture spectra. The method transforms a single nonlinear mixture into multiple high-dimensional reproducible kernel Hilbert Spaces (mRKHSs). Therein, highly correlated components are separated by sparseness constrained nonnegative matrix factorization in each induced RKHS. Afterwards, metabolites are identified through comparison of separated components with the library comprised of 160 pure components. Thereby, a significant number of them are expected to be related with diabetes type 2. Conceptually similar methodology for nonlinear blind separation of correlated components from two or more mixtures is presented in the Supplementary material. Single-mixture blind source separation is exemplified on: (i) annotation of five components spectra separated from one ¹H NMR model mixture spectra; (ii) annotation of fifty five metabolites separated from one ¹H NMR mixture spectra of urine of subjects with and without diabetes type 2. Arguably, it is for the first time a method for blind separation of a large number of components from a single nonlinear mixture has been proposed. Moreover, the proposed method pinpoints urinary creatine, glutamic acid and 5-hydroxyindoleacetic acid as the most prominent metabolites in samples from subjects with diabetes type 2, when compared to healthy controls.

Independent components analysis (ICA) at the "cocktail-party" in analytical chemistry

Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.

Challenges in the decomposition of 2D NMR spectra of mixtures of small molecules

Analytical methods for mixtures of small molecules require specificity (is a certain molecule present in the mix?) and speciation capabilities. NMR spectroscopy has been a tool of choice for both of these issues since its early days, due to its quantitative (linear) response, sufficiently high resolving power and capabilities of inferring molecular structures from spectral features (even in the absence of a reference database). However, the analytical performances of NMR spectroscopy are being stretched by the increased complexity of the samples, the dynamic range of the components, and the need for a reasonable turnover time. One approach that has been actively pursued for disentangling the composition complexity is the use of 2D NMR spectroscopy. While any of the many experiments from this family will increase the spectral resolution, some are more apt for mixtures, as they are capable of unveiling signals belonging to whole molecules or fragments of it. Among the most popular ones, one can enumerate HSQC-TOCSY, DOSY and Maximum-Quantum (MaxQ) NMR spectroscopy. For multicomponent samples, the development of robust mathematical methods of signal decomposition would provide a clear edge towards identification. We have been pursuing, along these lines, Blind Source Separation (BSS). Here, the un-mixing of the spectra is achieved relying on correlations detected on a series of datasets. The series could be associated with samples of different relative composition or in a classically acquired 2D experiment by the mathematical laws underlying the construction of the indirect dimension, the one not recorded by the spectrometer. Many algorithms have been proposed for BSS in NMR spectroscopy since the seminal work of Nuzillard. In this paper, we use rather standard algorithms in BSS in order to disentangle NMR spectra. We show on simulated data (both 1D and 2D HSQC) that these approaches enable us to accurately disentangle multiple components, and provide good estimates for the concentrations of compounds. Furthermore, we show that after proper realignment of the signals, the same algorithms are able to disentangle real 1D NMR spectra. We obtain similar results on 2D HSQC spectra, where the BSS algorithms are able to successfully disentangle components, and provide even better estimates for concentrations.

InterSpin: Integrated Supportive Webtools for Low- and High-Field NMR Analyses Toward Molecular Complexity

InterSpin (http://dmar.riken.jp/interspin/) comprises integrated, supportive, and freely accessible preprocessing webtools and a database to advance signal assignment in low- and high-field NMR analyses of molecular complexities ranging from small molecules to macromolecules for food, material, and environmental applications. To support handling of the broad spectra obtained from solid-state NMR or low-field benchtop NMR, we have developed and evaluated two preprocessing tools: sensitivity improvement with spectral integration, which enhances the signal-to-noise ratio by spectral integration, and peaks separation, which separates overlapping peaks by several algorithms, such as non-negative sparse coding. In addition, the InterSpin Laboratory Information Management System (SpinLIMS) database stores numerous standard spectra ranging from small molecules to macromolecules in solid and solution states (dissolved in polar/nonpolar solvents), and can be searched under various conditions using the following molecular assignment tools. SpinMacro supports easy assignment of macromolecules in natural mixtures via solid-state ¹³C peaks and dimethyl sulfoxide-dissolved ¹H-¹³C correlation peaks. InterAnalysis improves the accuracy of molecular assignment by integrated analysis of ¹H-¹³C correlation peaks and ¹H-J correlation peaks of small molecules dissolved in D₂O or deuterated methanol, which supports easy narrowing down of metabolite candidates. Finally, by enabling database interoperability, SpinLIMS's client software will ultimately support scientific discovery by facilitating sharing and reusing of NMR data.

Combined maximum-quantum and DOSY 3D experiments provide enhanced resolution for small molecules in mixtures

We illustrate here as the combination of high-order maximum-quantum (MaxQ) and Diffusion-Ordered SpectroscopY (DOSY) NMR experiments in a 3D layout allows superior resolution for crowded NMR spectra. Non-uniform sampling (NUS) allows compressing the experimental time effectively to reasonable durations. Because diffusion effects were encoded within multiple-quantum coherences, increased sensitivity to magnetic field gradients is observed, requiring compensation for convection effects. The experiment was demonstrated on the spectra of a mix of small polyaromatic molecules. Specifically, in the case analyzed, the experiment provided an extreme simplification through the MaxQDOSY-MaxQ projection plane that presents one peak per molecule. Copyright © 2016 John Wiley & Sons, Ltd.

19 F NMR matrix-assisted DOSY: a versatile tool for differentiating fluorinated species in mixtures

NMR is the most versatile tool for the analysis of organic compounds and, in combination with Diffusion-Ordered Spectroscopy ('DOSY'), can give information on compounds in complex mixtures without the need for physical separation. In mixtures where the components' diffusion coefficients are nearly identical, for example because of similar sizes, Matrix-Assisted DOSY ('MAD') can help separate the signals of different constituents, resolving their spectra. Unfortunately, DOSY (including MAD) typically fails where signals overlap, as is common in ¹ H NMR. Using ¹⁹ F NMR avoids such problems, because the great sensitivity of the ¹⁹ F chemical shift to local environment leads to very well-dispersed spectra. Another advantage is the absence of any ¹⁹ F background signals from the matrices typically used, avoiding interference with the analyte signals. In this study, differentiation among fluorophenol and fluoroaniline isomers was evaluated using normal and reverse micelles-of sodium dodecyl sulfate (SDS), cetyltrimethylammonium bromide (CTAB) and dioctyl sodium sulfosuccinate (AOT)-as matrices. These surfactants provide useful diffusion separation in these difficult mixtures, with all the solutes interacting with the matrices to different extents, in some cases leading to differences in diffusion coefficient of more than 30%. The best matrices for separating the signals of both acid and basic species were shown to be AOT and CTAB, which are useful over a wide range of surfactant concentration. Copyright © 2016 John Wiley & Sons, Ltd.

Development of polycarbonate-containing block copolymers for thin film self-assembly applications

High quality block copolymers are needed for thin film self-assembly and directed self-assembly applications.