Machine learning applied to the prediction of the quality of thermoplastic starch

Thermoplastic starch (TPS) has emerged as a biodegradable alternative to conventional plastics. However, the literature indicates that its functional performance varies with its composition and processing conditions, hindering efficient material design with traditional trial-and-error approaches. To address this need, this study develops machine learning-based predictive models to estimate the mechanical, thermal, permeability, barrier, and biodegradability properties of TPS as a function of formulation and processing variables. A dataset of 98 variables and 962 observations, obtained from previous studies, was constructed for this purpose. The CRISP-ML(Q) methodology was followed, and three supervised algorithms were implemented: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN). Model performance was evaluated using hold-out validation (70:20:10) and standard regression metrics. The results showed that XGBoost is the algorithm with the best overall predictive ability. The highest levels of accuracy were achieved for WVP (R 2 = 0.981) and Td (R 2 = 0.939). The main predictive limitations were observed in some ANN models, especially with the variable’s Tensile strength, Young's modulus, and T50, which reported negative or near-zero R 2 values. The developed models constitute a practical decision-support tool, allowing researchers and non-specialist users to virtually evaluate different combinations of formulation and processing conditions before conducting laboratory tests, thus reducing the number of experiments during the initial stages of new material development.