Machine learning-driven predictive modeling for lipid oxidation stability in emulsions: A smart food safety strategy

Background: Lipid oxidation in emulsions, especially those rich in polyunsaturated fatty acids, poses significant food safety challenges by compromising product quality, nutritional value, and shelf life. This degradation is often initiated at the oil-water interface by reactive compounds, with key contributing factors including emulsifier type, interfacial properties, and environmental conditions. Scope and approach: This study investigates intrinsic and extrinsic factors driving lipid oxidation and evaluates the application of machine learning (ML) algorithms to enhance oxidative stability. By leveraging supervised and unsupervised learning techniques, complex data relationships are uncovered, enabling predictive modeling to optimize formulations and reduce reliance on traditional experimental methods. Key findings and conclusions: ML-based predictive models offer actionable insights for mitigating lipid oxidation, ensuring safer, more stable emulsions with extended shelf life. These advancements minimize the formation of harmful byproducts, directly addressing food safety risks while enhancing quality and sustainability. This research highlights ML's transformative potential in food emulsion science and calls for interdisciplinary collaboration to unlock further opportunities in predictive modeling and smart food safety technologies.