Influence of starch-protein interactions on the digestibility and chemical properties of a 3D-printed food matrix based on salmon by-product proteins.

This study evaluated the influence of starch-protein interactions on the chemical properties and digestibility of a 3D-printed gel based on salmon by-product protein. Changes in the starch-protein interactions of the stable cornstarch (CS, 15%) and salmon protein isolate (SPI, 4%-12%) printable gels during the in vitro gastrointestinal digestion process were studied by principal component analysis. Protein-rich printed gels increased resistant starch content by 18.

Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility Properties.

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 (R = 99.

Encapsulation of Concentrated Solution Obtained by Block Freeze Concentration in Calcium Alginate and Corn Starch Calcium Alginate Hydrogel Beads.

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%.