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 by citrus-peel extract. The hybrid model shrank the experimental search space by 65% and predicted tensile strength, water-vapour transmission rate and antimicrobial efficacy with R²>0.94. The optimal film delivered a tensile strength of \SI{3.5}{\mega\pascal}, a \SI{31}{\percent} drop in water-vapor 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 optimization can accelerate the development of high-performance, biodegradable packaging and provides a transferable framework for next-generation sustainable food-contact materials.

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