Predicting Antioxidant Synergism via Artificial Intelligence

Lipid oxidation is a major issue affecting products containing unsaturated fatty acids as ingredients or components. In specific, the formation of low molecular-weight species with diverse functional groups has the potential to impart off-odors and off-flavors. This process, also known as rancidity, and can not only impart an unpleasant taste, but also diminish the nutritional value, shelf-life, and the overall quality of products, which ultimately impacts all segments of the supply chain. The impacted products include, for instance, cosmetics, vegetable oils, seafood, processed meat, animal feed, and other food samples. While current approaches to controlling this process through the addition of antioxidants minimizes the potential organoleptic and toxic effects of these compounds, empirically predicting how these mixtures of antioxidants will behave has traditionally been one of the most challenging tasks, often leading to simple additive (or even antagonistic) instead of the desired synergistic effects. To address this current gap in knowledge, a novel artificial intelligence model was developed based on deep learning architecture to both predict the type of interaction (synergistic, additive, and antagonistic) of known mixtures as well as to unveil new antioxidant combinations.