Cationic Starch and Polyvinyl Alcohol Composite Sheets Produced by Extrusion-Calendering

This study evaluated the effect of cationic cassava starch with different degrees of substitution (DS: 0.019, 0.027, and 0.033 mol/mol) and polyvinyl alcohol (PVA) with different hydrolysis degrees (HD: 88 and 98%) on the properties of sheets produced by extrusion-calendering, using glycerol as a plasticizer. Machine learning (ML) models were used to predict sheet properties. The HD influenced material properties more significantly than the DS of cationic starch, with higher HD PVA yielding superior mechanical performance regardless of starch type. The sheets exhibited high thermal stability and processing continuity during extrusion-calendering, demonstrating laboratory-scale production viability. ML demonstrated that Gradient Boosting models better predicted tensile strength and elongation at break with R² ~0.98 and MAPE of the 7.1 and 2.8%, respectively; the Young's modulus was adequately predicted by the Decision Tree model (R² ~0.96 and MAPE ~8.1%) and the apparent opacity was better predicted by the Extreme Gradient Boosting model (R² ~0.90 and MAPE ~4.7%). For these adjustments, the HD was more important than DS when predicting the properties of the sheets, as observed experimentally.