Automated Mixture Analysis via Structural Evaluation

TL;DR

This paper introduces a machine learning and graph-based approach to accurately identify mixture components in spectroscopic data, leveraging chemical relevance and priors to improve analysis efficiency and accuracy.

Contribution

The paper presents a novel method combining molecular embeddings and graph ranking to enhance mixture component identification in spectroscopy.

Findings

Achieves over 97% accuracy in mixture component identification

Efficient analysis process suitable for complex spectroscopic data

Integrates chemical priors to improve component likelihood estimation

Abstract

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.