Generative Algorithm for Molecular Graphs Uncovers Products of Oil Oxidation

Abstract

The autoxidation of triglyceride (or triacylglycerol, TAG) is a poorly understood complex system. It is known from mass spectrometry measurements that, although initiated by a single molecule, this system involves an abundance of intermediate species and a complex network of reactions. For this reason, the attribution of the mass peaks to exact molecular structures is difficult without additional information about the system. We provide such information using a graph theory-based algorithm. Our algorithm performs an automatic discovery of the chemical reaction network that is responsible for the complexity of the mass spectra in drying oils. This knowledge is then applied to match experimentally measured mass spectra with computationally predicted molecular graphs. We demonstrate this methodology on the autoxidation of triolein as measured by electrospray ionization-mass spectrometry (ESI-MS). Our protocol can be readily applied to investigate other oils and their mixtures.

Constellation: An Open-Source Web Application for Unsupervised Systematic Trend Detection in High-Resolution Mass Spectrometry Data

“…However, their code does not perform an unsupervised search for series of identical KMD values, a key part of Constellation’s trend finding algorithm. Other examples of software algorithms which perform automated discovery of patterns in MS data (but not specifically the KMD space) exist, such as Orlova et al, who developed a generative algorithm for automatic discovery of the multiple chemical reaction pathways in complex mass spectra of drying oils . Another is from Klingberg et al, who used a machine learning approach to assist with the identification of unknown compounds in a nontargeted synthetic opioid screening using LC-HRMS (QTOF) .…”