Automated Mixture Analysis via Structural Evaluation

The determination of chemical mixture components is vital
to a
multitude of scientific fields. Oftentimes spectroscopic methods are
employed to decipher the composition of these mixtures. However, the
sheer density of spectral features present in spectroscopic databases
can make unambiguous assignment to individual species challenging.
Yet, components of a mixture are commonly chemically related due to
environmental processes or shared precursor molecules. Therefore,
analysis of the chemical relevance of a molecule is important when
determining which species are present in a mixture. In this paper,
we combine machine-learning molecular embedding methods with a graph-based
ranking system to determine the likelihood of a molecule being present
in a mixture based on the other known species and/or chemical priors.
By incorporating this metric in a rotational spectroscopy mixture
analysis algorithm, we demonstrate that the mixture components can
be identified with extremely high accuracy (≥97%) in an efficient
manner.