Microencapsulation of Beetroot Anthocyanins: Investigation of Degradation Kinetics and Modeling by Using Artificial Neural Networks

Anthocyanins are widely appreciated as natural pigments, but their use in foods and related industries is still quite limited because they are highly sensitive to heat, pH changes, light, and oxygen. Improving their stability has therefore become a key focus in developing more reliable natural color systems. In this study, beetroot anthocyanins were microencapsulated with different wall materials, maltodextrin (MD), gum arabic (GA), a simple MD/GA blend, and a ternary structure combining MD, GA, and sodium caseinate (MD/GA/SC). These systems were evaluated for their encapsulation efficiencies, antioxidant activity preservation, release behaviors, and degradation responses over a wide range of temperatures (40–100 °C) and pH levels (2.5–6.5). Remarkable findings demonstrated that the MD/GA/SC formulation provided the highest encapsulation efficiency (93.36%), superior radical-scavenging activity (88.43%), and the most controlled release profile. Moreover, this formulation demonstrated the lowest degradation rate constants at pH 2.5, 4.5, and 6.5 (2.886, 2.083, and 1.30 1/min, respectively) together with the highest activation energies at these pH levels (37.460, 52.517, and 62.045 kJ/mol, respectively), indicating a pronounced improvement in thermal stability compared with the other formulations and the free extract. An artificial neural network (ANN) model was developed to predict anthocyanin degradation. The ANN provided highly accurate predictions (R2 > 0.98, RMSE < 0.01) across all conditions and outperformed the classical first-order kinetic model. These findings highlight the potential of the MD/GA/SC matrix as a promising encapsulation system for improving anthocyanin stability. The strong performance of the ANN model also suggests that data-driven approaches can contribute meaningfully to designing more reliable microencapsulation strategies for future food and nutraceutical applications.