Prediction of extruded aquafeed physical quality parameters through a dough viscosity model

Author:

Cheng Hongyuan12ORCID,Samuelsen Tor A.3,Sørensen Mette4,Xue Min1,Li Junguo1

Affiliation:

1. Feed Research Institute Chinese Academy of Agriculture Sciences Beijing China

2. National Food Institute Technical University of Denmark Kgs. Lyngby Denmark

3. Nofima AS Bergen Norway

4. Faculty of Bioscience and Aquaculture Nord University Bodø Norway

Abstract

AbstractA widely used dough viscosity model in food extrusion was adopted and employed for the analysis and prediction of extruded aquafeed physical quality parameters. The data for the analysis were collected from previously published articles. The analysis was based on the assumption that pellet physical quality parameters, such as bulk density, oil adsorption, hardness, and durability, are correlated to the dough viscosity property changes in the extrusion process. The physical qualities of feed were modeled using the modified viscosity model. The model was evaluated using the data collected from experiments conducted in three different pilot extrusion systems. The results showed that the new model has the capability to predict the physical quality parameters of extruded aquafeed pellets in the three studied scenarios. The absolute average deviations of the model regression for the pellet qualities were 8.5% for hardness, 4.8% for bulk density, 6.5% for oil adsorption, 15.7% for Holmen durability, and 0.7% for pellet diameter. The new model can correlate the physical qualities of aquafeed pellets across different recipes, extruder configurations, and systems.Practical ApplicationsFood or aquatic feed extrusion process operation heavily depends on the operator's experiences and is still a black‐box process. In this study, a dough viscosity model was applied to explain the relationship between extrusion variables and extruded pellet physical qualities. The new model can be used for a quantitative description of a feed formulation extrusion process and feed pellet quality optimization.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

NordForsk

Norges Forskningsråd

Publisher

Wiley

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