Prediction of oxidative stability in model emulsions for preterm infant formula: A QSAR-inspired framework for...

This study demonstrates the first application of Quantitative Structure-Activity Relationship (QSAR)-inspired computational principles to preterm/low birth weight infant formula emulsion processing optimization. Under combined homogenization-sterilization conditions, elevated homogenization pressure and cycle numbers reduced oil droplet size and increased interfacial protein content, leading to enhanced absolute zeta potential, emulsifying activity, and stability. However, excessive homogenization (e.g., 2-3 cycles under high pressure) triggered droplet aggregation, reduced interfacial protein coverage, and intensified protein-lipid co-oxidation, as demonstrated by elevated concentrations of N'-formyl-l-kynurenine, di-tyrosine, and malondialdehyde. Curve fitting and correlation analysis identified significant relationships between homogenization cycles number/pressure, emulsifying activity/stability indices, and co-oxidation levels. A predictive regression model (R² = 0.967, p < 0.05) successfully quantified the effects of homogenization parameters and interfacial characteristics on oxidative stability (malondialdehyde content). Hierarchical clustering identified the S-2-40 sample demonstrated optimal emulsifying performance with minimal co-oxidation products. These findings establish theoretical and technological foundations for engineering high-stability preterm infant formulas.