Abstract
NMR offers tremendous advantages in analysis of molecular complexity of natural mixtures, such as crude biological extracts, supramacromolecular complexes, and geochemical samples as well as intact cells and tissues. Here, I introduce recent applications of several NMR approaches for evaluation of human and environmental health (i.e., maintaining a homeostatic state) by metabolic profiling and data science. Further challenges in addressing macromolecular complexity include supramolecular structures, composition, and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, I also introduce sample preparation techniques for molecular complexity studies as well as solid-state NMR approaches. Because solution and solid-state NMR can produce numerical matrix data (e.g., chemical shifts versus intensity) with high reproducibility and inter-institution convertibility, further data science approaches are desired, such as multivariate analysis and machine learning. Therefore, I also introduce informatics techniques for data pretreatment before solid-state NMR, for feature extraction from heterogeneously measured spectroscopic data and for extraction of submerged information using data science approaches.
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Kikuchi, J. (2018). NMR Analysis of Molecular Complexity. In: The Nuclear Magnetic Resonance Society of Japan (eds) Experimental Approaches of NMR Spectroscopy. Springer, Singapore. https://doi.org/10.1007/978-981-10-5966-7_17
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