Genome-inspired molecular identification in organic matter via Raman spectroscopy
Yun Liu, Nicola Ferralis, L. Taras Bryndzia, and Jeffrey C. Grossman

TL;DR
This paper introduces a novel genome-inspired method combining ab initio calculations and data mining to enhance Raman spectroscopy for detailed, non-destructive molecular identification in organic matter, overcoming previous limitations.
Contribution
It presents a new collective molecular fingerprinting approach that enables detailed molecular identification in organic matter using Raman spectra, integrating computational and data-driven techniques.
Findings
Successfully identified molecular fingerprints in organic matter
Quantified chemical properties like aromatic cluster size and H/C ratio
Enabled non-destructive chemical signature detection and biosignature preservation assessment
Abstract
Rapid, non-destructive characterization of molecular level chemistry for organic matter (OM) is experimentally challenging. Raman spectroscopy is one of the most widely used techniques for non-destructive chemical characterization, although it currently does not provide detailed identification of molecular components in OM, due to the combination of diffraction-limited spatial resolution and poor applicability of peak-fitting algorithms. Here, we develop a genome-inspired collective molecular structure fingerprinting approach, which utilizes ab initio calculations and data mining techniques to extract molecular level chemistry from the Raman spectra of OM. We illustrate the power of such an approach by identifying representative molecular fingerprints in OM, for which the molecular chemistry is to date inaccessible using non-destructive characterization techniques. Chemical properties…
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Taxonomy
TopicsMass Spectrometry Techniques and Applications · Computational Drug Discovery Methods · Metabolomics and Mass Spectrometry Studies
