Machine-learning driven design of bio-based active food packaging films with improved mechanical properties

Abstract

Bio-based active packaging films offer a sustainable route to replace petro-plastic laminates, but their multicomponent formulations complicate rational design. We report a machine-learning driven workflow that couples response surface methodology with artificial neural networks to optimise starch–chitosan films plasticised with glycerol, reinforced with beeswax and ZnO, and activated using citrus-peel extract. The hybrid model shrank the experimental search space by 65% and predicted tensile strength, the water-vapour transmission rate and antimicrobial efficacy with R² > 0.94. The optimal film delivered a tensile strength of 3.5 Mpascal, a 31% drop in water-vapour permeability and a >3 log CFU reduction against E. coli, while remaining fully soil-biodegradable within 45 days. Fourier-transform infrared spectra confirmed hydrogen-bond-mediated compatibility between polysaccharide chains and bioactives, explaining the improved mechanical integrity. This study demonstrates that data-guided optimisation can accelerate the development of high-performance, biodegradable packaging and provides a transferable framework for next-generation sustainable food-contact materials.