RESUMO
This study aimed to optimize the 3D printing parameters of salmon gelatin gels (SGG) using artificial neural networks with the genetic algorithm (ANN-GA) and response surface methodology (RSM). In addition, the influence of the optimal parameters obtained using the two different methodologies was evaluated for the physicochemical and digestibility properties of the printed SGG (PSGG). The ANN-GA had a better fit (R2 = 99.98%) with the experimental conditions of the 3D printing process than the RSM (R2 = 93.99%). The extrusion speed was the most influential parameter according to both methodologies. The optimal values of the printing parameters for the SGG were 0.70 mm for the nozzle diameter, 0.5 mm for the nozzle height, and 24 mm/s for the extrusion speed. Gel thermal properties showed that the optimal 3D printing conditions affected denaturation temperature and enthalpy, improving digestibility from 46.93% (SGG) to 51.52% (PSGG). The secondary gel structures showed that the ß-turn structure was the most resistant to enzymatic hydrolysis, while the intermolecular ß-sheet was the most labile. This study validated two optimization methodologies to achieve optimal 3D printing parameters of salmon gelatin gels, with improved physicochemical and digestibility properties for use as transporters to incorporate high value nutrients to the body.
RESUMO
A model (sucrose and gallic acid) solution was concentrated by block freeze concentration (BFC) at three centrifugation cycles, and the solutions were encapsulated in calcium alginate and corn starch calcium alginate hydrogel beads. Static and dynamic tests determined the rheological behavior, differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) established thermal and structural properties, and the release kinetics was evaluated under in vitro simulated digestion experiment. The highest efficiency encapsulation value was close to 96%. As the concentrated solution increased in terms of solutes and gallic acid, the solutions were fitted to the Herschel-Bulkley model. Moreover, from the second cycle, the solutions exhibited the highest values of storage modulus (G') and loss modulus (Gâ³), contributing to form a more stable encapsulation. The FTIR and DSC results demonstrated strong interactions between corn starch and alginate, establishing a good compatibility and stability in the bead formation. The kinetic release model under in vitro conditions was fitted to the Korsmeyer-Peppas model, demonstrating the significant stability of the model solutions inside the beads. Therefore, the present study proposes a clear and precise definition for the elaboration of liquid foods obtained by BFC and its incorporation inside an edible material that facilitates the controlled release in specific sites.