Examination of Mechanisms for Formation of Volatile Aldehydes from Oxidation of Oil-Based Systems

A comprehensive two-scale model for predicting the oxidizability of fatty acid methyl ester mixtures

The intricate mechanisms of oil thermooxidation and their accurate prediction have long been hampered by the combinatory nature of propagation and termination reactions involving randomly generated radicals. To unravel this complexity, we suggest a two-scale mechanistic description that connects the chemical functions (scale 1) with the molecular carriers of these functions (scale 2). Our method underscores the importance of accounting for cross-reactions between radicals in order to fully comprehend the reactivities in blends. We rigorously tested and validated the proposed two-scale scheme on binary and ternary mixtures of fatty acid methyl esters (FAMEs), yielding three key insights: (1) The abstraction of labile protons hinges on the carrier, defying the conventional focus on hydroperoxyl radical types. (2) Termination reactions between radicals adhere to the geometric mean law, exhibiting symmetric collision ratios. (3) The decomposition of hydroperoxides emerges as a monomolecular process above 80 °C, challenging the established combinatorial paradigm. Applicable across a wide temperature range (80 °C to 200 °C), our findings unlock the production of blends with controlled thermooxidation stability, optimizing the use of vegetable oils across applications: food science, biofuels, and lubricants.

Generative Algorithm for Molecular Graphs Uncovers Products of Oil Oxidation

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.

Acetone as Indicator of Lipid Oxidation in Stored Margarine

Margarine contains a minimum of 80% fat and is therefore prone to lipid oxidation. While lipid oxidation in vegetable oils and o/w emulsions has been thoroughly investigated, studies about the oxidative stability and the identification of potential indicators of lipid oxidation in margarine are scarce. To evaluate the oxidative stability and to indicate the progress of lipid oxidation, four different types of industrial margarine (M1–M4), which differed in their composition of the minor ingredients and the oil phase, were stored at 15 °C for 180 days and analyzed at days 0, 1, 7, 14, 28, 56, 99, and 180 regarding peroxides, conjugated dienes, oxidized triacylglycerols, and volatiles. The peroxide value and the conjugated dienes increased up to 4.76 ± 0.92 meq O₂/kg oil and 14.7 ± 0.49 in M2, respectively. The oxidative stability decreased by a maximum of 50.9% in M4. We detected three different epoxidized triglycerides—TAG54:1 (O), TAG54:2 (O) and TAG54:3 (O)—in M3. Acetone could be identified, for the first time, as lipid oxidation product in stored margarine by headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). It increased in all types of margarine during storage by a maximum of 1070 ppb in M2. Acetone might be used as a new indicator for lipid oxidation in margarine.

Modeling the Evolution of Crosslinked and Extractable Material in an Oil-Based Paint Model System

The construction of mechanistic models for the autoxidation of fatty acid or ester substrates found in oil paint binders is a challenging undertaking due to the complexity of the large crosslinked species that form, and the small molecules that volatilize. Building models that capture this product diversity are made possible by automating the process of network generation. This work presents a microkinetic model for the autoxidation of ethyl linoleate catalyzed by cobalt(II) 2-ethyl hexanoate. The mechanism size was controlled by using a rate-based criterion to include the most kinetically relevant reactions from among the millions of possible reactions generated. The resulting model was solved and compared to experimental metrics. Quantities such as hexanal production and the consumption of unsaturated moieties were in good agreement with experiment. Finally, the model was used to explore the effect of the catalyst concentration and temperature on key measurables.