A model that predicts resistant starch in a dog kibble using a small-scale twin-screw extruder

Abstract

AbstractThe objective of this work was to modify extrusion parameters to yield greater resistant starch (RS) in a kibble and create a model to predict its concentration. A dog food was extruded through a small-scale twin-screw extruder as a central composite design with 6 central points (replicates) and 14 single replicates. There were three factors tested at three levels: corn particle size, extruder shaft speed, and in-barrel moisture (IBM). The remaining processing inputs were kept constant. Chemical and physical starch analyses were performed. A model to predict RS was created using the REG procedure from SAS. Pearson correlations between extrusion parameters and starch analyses were conducted. A model to predict RS was created (R2adj= 0.834; P < .0001). Both SME and extrudate temperature had a high negative correlation with RS and RVA raw starch. Results suggest that low mechanical energy and high IBM increase kibble RS.