Dual-Plasticizer Chemistry Dictates Properties of Post-Maltodextrin-Extracted Rice Protein-based Films
DOI:
https://doi.org/10.52151/jae2026633.2020Keywords:
biodegradable film, glycerol, interpretable modelling, machine learning, sorbitolAbstract
Protein-based films made from post-maltodextrin-extracted broken rice protein (PMEBR) were investigated. The aim was to use the underutilized PMEBR with a glycerol-sorbitol dual-plasticizer system coupled with interpretable machine learning to identify threshold-based rules that balance strength, flexibility, and water responsiveness of the films produced. Films containing 4%-12% protein (w/v) were plasticized with glycerol, sorbitol, and their mixtures, then tested for tensile strength, elongation at break, thickness, moisture content, solubility, and swelling ratio. Multivariate statistical analysis combined with interpretable machine-learning (ML) models was used to identify structure-property relationships beyond traditional single-factor approaches. Spearman's rank correlation and principal component analysis confirmed that plasticizer chemistry primarily determines film behaviour, distinguishing glycerol-rich, sorbitol-rich, and mixed-plasticizer regimes. Tree-based ML models showed the best overall test performance across the six film properties with a mean test R2 of 0.98, outperforming linear regression (mean R2: 0.63) and shallow neural-network/MLP models (mean R2: 0.60). Glycerol mainly affected extensibility, moisture absorption, and the reduction of tensile strength beyond approximately 15%-25% glycerol (w/w of protein). In contrast, sorbitol-controlled solubility, swelling, and dimensional stability, with transitions near 45%-75% sorbitol (w/w of protein). Protein concentration mainly influenced film thickness and baseline strength. Decision-tree analysis translated these nonlinear interactions into clear formulation rules, while ridge regression enabled rapid predictions of film properties within the studied range. Overall, adjusting glycerol-sorbitol ratios rather than total plasticizer content was identified as the key strategy for customizing PMEBR-based film performance. The ML models developed are applicable within the studied formulation space (4%-12% protein and tested plasticizer ranges) and should be viewed as formulation guidance tools rather than universal predictors. These findings provide an interpretable, data-driven framework for designing sustainable protein-based packaging materials from rice-processing byproducts.
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