Understanding drying processes of steam-blanched Wolffia globosa under microwave vacuum drying through data-driven and semi-theoretical modeling

Steam blanching and microwave vacuum drying (MVD) are increasingly applied to preserve heat-sensitive, protein-rich biomaterials such as Wolffia globosa . However, accurately modeling moisture removal under MVD remains challenging due to the highly nonlinear and stage-dependent nature of microwave-induced drying. This study investigates the combined effects of steam blanching pretreatment and microwave power (540, 720, and 900 W at 5 kPa) on the drying kinetics of W. globosa and evaluates the capability of machine-learning models to predict moisture evolution in comparison with conventional semi-theoretical models. The results indicated that steam-blanched samples consistently exhibited higher maximum drying rates across all power levels, increasing from 1.088 to 1.298 gw/(gdm·min) at 540 W and from 1.974 to 2.126 gw/(gdm·min) at 900 W. The pretreatment also reduced total drying time from 19.5 to 18.0 min at 540 W, while drying time remained unchanged at higher microwave powers. Among the evaluated semi-theoretical models, the Midilli equation provided the best fit to condition-specific experimental data (R² = 0.9981–0.9994). However, the generalized Midilli model showed systematic underestimation at low moisture ratios across operating conditions (R² = 0.9865, RMSE = 0.9990). In contrast, the k-nearest neighbors (k-NN) model demonstrated strong generalized predictive capability across multiple drying conditions, achieving R² = 0.9798 and RMSE = 0.0481 on the testing dataset. Beyond improved predictive accuracy, the machine-learning framework effectively captured stage-dependent drying behavior patterns that conventional generalized semi-theoretical models failed to represent. These findings highlight the potential of data-driven approaches for modeling complex drying behavior under microwave vacuum conditions. The accurate prediction of moisture ratio may further support the optimization of MVD processes for W. globosa -based food ingredients.