Hot-air drying optimization and kinetic modeling of novel functional green vegetable bars

Abstract

This study presents the development of a novel gluten-free functional green vegetable bar (GVB) and its drying process optimization by evaluating the effects of drying temperature, final moisture content, and bar thickness on key quality parameters. Box–Behnken design within the Response Surface Methodology (RSM), Artificial Neural Network (ANN) modeling, and chemometric analysis were employed to predict and refine drying time, texture, and sensory properties. Thin-layer drying models and effective moisture diffusivity (Deff) were also analyzed to characterize drying kinetics. RSM results showed high model accuracy (R² values above 0.83), and the optimum conditions were identified as 83.73 °C drying temperature, 35% final moisture content, and 0.66 cm bar thickness. ANN demonstrated superior prediction performance (R² >0.99 in training) with slightly differing optimal conditions (78.56 °C, 32.12%, 0.5 cm). The Page and Modified Page models best described drying behavior, while Deff ranged between 2.13 × 10⁻¹⁰ and 5.98 × 10⁻¹⁰ m²/s. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) techniques confirmed consistent clustering based on sensory and physicochemical data. Correlation analysis highlighted strong interactions among color, texture, and sensory attributes. These findings suggest that integrated modeling approaches, combining RSM, ANN, and multivariate statistical analyses, provide a robust framework for optimizing GVB drying processes while maintaining product quality.