Generative Algorithm for Molecular Graphs Uncovers Products of Oil Oxidation

Area of Science:

• Chemistry
• Materials Science
• Computational Chemistry

Background:

• Triglyceride (or triacylglycerol, TAG) autoxidation is a complex process with numerous intermediate species and reactions.
• Mass spectrometry (MS) measurements reveal this complexity, but attributing specific mass peaks to molecular structures is challenging.
• Existing methods lack the detail to fully elucidate the reaction network in autoxidation systems.

Purpose of the Study:

• To develop a computational method for discovering the chemical reaction network in triglyceride autoxidation.
• To provide a means to match experimental mass spectra with predicted molecular structures.
• To enhance the understanding of complex autoxidation systems in oils.

Main Methods:

• A graph theory-based algorithm was developed for automatic discovery of reaction networks.
• The algorithm analyzes mass spectrometry data to identify reaction pathways.
• The methodology was demonstrated using electrospray ionization-mass spectrometry (ESI-MS) data for triolein autoxidation.

Main Results:

• The graph theory algorithm successfully identified the complex reaction network underlying triglyceride autoxidation.
• The developed method allows for the matching of experimentally measured mass spectra with computationally predicted molecular graphs.
• The study provides a robust protocol for analyzing the autoxidation of triolein.

Conclusions:

• The graph theory approach offers a novel solution for deciphering complex chemical systems like TAG autoxidation.
• This methodology significantly aids in the structural elucidation of intermediates and products in oil autoxidation.
• The protocol is adaptable for investigating various oils and their mixtures, advancing analytical capabilities in materials science.