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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.