Statistical Modeling of Nutritional Enhancement During Thermal Processing of Solanum torvum

Abstract

Conventional thermal processing lacks a mechanistic understanding of nutritional enhancement, resulting in suboptimal outcomes and energy inefficiency. This study developed a statistical framework to optimize mineral bioaccessibility and protein enhancement kinetics during Solanum torvum thermal processing through comprehensive kinetic modeling. Statistical analysis revealed complex biphasic iron enhancement kinetics (a two-stage process with rapid initial enhancement followed by slower continued improvement) consistent with established plant food processing mechanisms but newly characterized for S. torvum, with rapid cellular disruption (k₁ = 0.145 min⁻¹, where k represents the speed of the process) followed by slower complex dissociation (k₂ = 0.032 min⁻¹). The integrated predictive framework achieved strong accuracy (R² > 0.92, prediction errors < 8%) using mixed effects modeling, bootstrap validation (repeated sampling for reliability testing), and Monte Carlo simulation (computer-based uncertainty analysis). Activation energy analysis (19.4–47.3 kJ/mol, representing the energy barrier that must be overcome for reactions to proceed) distinguished temperature-dependent mechanisms, while optimization identified laboratory-scale processing windows for iron bioaccessibility (67.5°C–69.2°C) and protein enhancement (58.3°C–62.1°C). This methodology provides a tool for transitioning from empirical processing toward model-guided optimization, offering a modeling framework that, following systematic pilot-scale validation, could support industrial optimization efforts with quantified uncertainties.