A 2D proton J-resolved NMR method for direct measurements on heterogeneous foods

Improved Prediction of Carbonless NMR Spectra by the Machine Learning of Theoretical and Fragment Descriptors for Environmental Mixture Analysis

As the first multidimensional NMR approach, 2D J-resolved (2DJ) spectroscopy is distinguished by signal resolution and detection sensitivity with remarkable advantages for the exhaustive evaluation of complex mixtures and environmental samples due to its carbonless feature without the requirement of ¹³C connectivity. Generally, the 2DJ signal assignment of metabolic mixtures is problematic in spite of references to experimental NMR databases, owing to the existence of metabolic "dark matter." In this study, a new method to predict 2DJ spectra was developed with a combination of quantum mechanical (QM) computation and machine learning (ML). The predictive accuracy of J-coupling constants was evaluated using validated data. The root-mean-square deviation (RMSD) for QM computation was 3.52 Hz, while the RMSD for QM + ML was 1.21 Hz, indicating a substantial increase in predictive accuracy. The proposed model was applied to predict the 2DJ spectra of 60 standard substances and 55 components of seawater. Furthermore, two practical environmental samples were used to evaluate the robustness of the constructed predictive model. A J-coupling tree and J-split spectra produced from QM + ML of aliphatic moieties had good consistency with the experimental data, as compared with the theoretical data produced by QM computation. The predicted J-coupling tree for the J-coupling multiplet analysis of freely rotating bonds in the complex mixture, which is traditionally difficult, was interpretable. In addition, in silico identification of the J-split ¹H NMR signals, which was independent of experimental databases, aided in the discovery of new components in a mixture.

Spatial molecular-dynamically ordered NMR spectroscopy of intact bodies and heterogeneous systems

Noninvasive evaluation of the spatial distribution of chemical composition and diffusion behavior of materials is becoming possible by advanced nuclear magnetic resonance (NMR) pulse sequence editing. However, there is room for improvement in the spectral resolution and analytical method for application to heterogeneous samples. Here, we develop applications for comprehensively evaluating compounds and their dynamics in intact bodies and heterogeneous systems from NMR data, including spatial z-position, chemical shift, and diffusion or relaxation. This experiment is collectively named spatial molecular-dynamically ordered spectroscopy (SMOOSY). Pseudo-three-dimensional (3D) SMOOSY spectra of an intact shrimp and two heterogeneous systems are recorded to evaluate this methodology. Information about dynamics is mapped onto two-dimensional (2D) chemical shift imaging spectra using a pseudo-spectral imaging method with a processing tool named SMOOSY processor. Pseudo-2D SMOOSY spectral images can non-invasively assess the different dynamics of the compounds at each spatial z-position of the shrimp's body and two heterogeneous systems.

Single-Scan High-Resolution 2-D $J$ -Resolved Spectroscopy in Inhomogeneous Magnetic Fields

OBJECTIVE

A method is proposed to obtain high-resolution 2-D -resolved nuclear magnetic resonance (NMR) spectra in inhomogeneous magnetic fields.

METHODS

The proposed experiment enables the acquisition of an entire 2-D spectrum in a single scan by utilizing intermolecular double-quantum coherences and the spatial encoding of NMR observables.

RESULTS

Chemical shifts, coupling constants, and multiplet patterns are recovered even when field inhomogeneities are severe enough to completely obscure conventional NMR spectra. After intentional deshimming to yield inhomogeneous magnetic fields, the method was demonstrated on ethyl 3-bromoproprionate in acetone and on a complex mixture of organic compounds. To illustrate the technique's applicability to biological samples with intrinsic magnetic field inhomogeneities arising from macroscopic magnetic susceptibility variations, we performed the experiment on a pig bone marrow sample.

CONCLUSION

Our results show that the new method is a fast and effective tool for studying complex chemical mixtures and biological tissues.

SIGNIFICANCE

The method could potentially be useful for real-time in vivo NMR studies.