Leveraging machine learning for the optimization of reinforced rapeseed protein-gelatin edible coatings for enhanced food preservation

• Expanded 13 data points to 1,000 using Gaussian noise for data augmentation. • ML models showed R 2 : 0.9517–0.9998 and MSE: 0.0001–0.5537. • Optimized film had T.S. 28.94 MPa, E% 22.61, and WVP 4.40 × 10 2 g·mm/m 2 ·day·kPa. • Model predictions showed strong correlation with experimental results. • Edible film coating extended fruit shelf life by 7 days at ambient conditions. Optimizing component ratios is crucial for enhancing bioplastic formulations. Traditional optimization is time-consuming, while data-driven methods require large datasets. To address this, we augmented thirteen experimental data points to 1,000, enhancing machine learning (ML) model accuracy. Three ML models were trained to optimize the formulation of a rapeseed protein-gelatin nanocomposite reinforced with cellulose nanocrystals (CNC) and cross-linked with citric acid. The models showed high predictive performance (R 2 : 0.9517–0.9998; MSE: 0.0001–0.5537; MAE: 0.0018–0.4221). The optimal formulation (1:1:5.5:3.87) yielded a film with 28.94 ± 1.18 MPa tensile strength, 22.61 ± 2.12 % elongation, and 4.40 ± 0.04 g mm/d·m 2 ·kPa × 10 2 water vapor permeability. A film based on this formulation was applied as a coating on fruits and showed an increased shelf life, verifying its practical application. Our results show that the integration of generated data enhances ML predictive performance, capturing complex nonlinear relationships between input and output variables, thus balancing the mechanical and barrier properties of biopolymer films. This work highlights the potential of data generation and ML in materials science by providing a pathway to accelerate the development of sustainable bio-based plastics.