Ternary Mixture Design of Soy Protein, Apple Fiber, and Corn Starch: Compositional, Color, and Techno-Functional Behavior

Soy protein, apple fiber, and corn starch are widely used functional ingredients, yet their combined effects on food matrix properties remain poorly characterized. A D-optimal mixture design (16 runs) was used to evaluate how these three components, individually and in binary and ternary combinations, determine the proximate composition, CIELab color parameters, and techno-functional properties (water and oil absorption and solubility, pH, apparent density, emulsifying and foaming capacity, least gelation concentration, and foam stability over 120 min) of model blends. Response surface models were statistically significant for 19 of the 21 responses evaluated (R2 = 0.70–1.00, p ≤ 0.05); water and oil absorption capacity did not reach significance (R2 = 0.66–0.72, p > 0.05). Formulation was the main driver of the system’s behavior. Starch increased moisture, carbohydrate content, and lightness; protein determined ash, protein content, and water solubility; and fiber dominated crude fiber, lipid content, and red–yellow chromaticity, producing the greatest total color difference relative to pure starch. Unlike composition and color, which followed largely additive, single-ingredient-driven trends, interfacial functionality was governed by strong binary interactions: a synergistic protein–fiber interaction dominated emulsifying capacity (positive) and foaming capacity (strongly negative, almost completely suppressing foam formation even at protein levels comparable to the pure-protein vertex), while a synergistic protein–starch interaction enhanced foam stability. Water and oil absorption capacities varied within narrow, statistically non-significant ranges, indicating structural rather than compositional control. These findings show that mixture design and response surface methodology can quantitatively predict and tune the physicochemical, color, and functional performance of protein–fiber–starch blends, providing a practical framework for designing plant-based functional ingredients with targeted nutritional and technological profiles.