Prediction of viscoelastic properties of peanut-based 3D printable food ink

Viscoelastic properties of 3D printable peanut-based food ink were investigated using frequency sweep and relaxation test. The incorporation of xanthan gum (XG) improved the shear thinning behavior (n value ranging from 0.139 to 0.261) and lowered the eta*, G ', and G '' values, thus making food ink 3D printable. The addition of XG also caused a downward shift in the relaxation curve. This study evaluates the possibility of an artificial neural network (ANN) approach as a substitute for the Maxwell three-element and Peleg model for predicting the viscoelastic behavior of food ink. The results revealed that all three models accurately predicted the decay forces. The inclusion of XG decreased the hardness and enhanced the cohesiveness, so enabling the 3D printing of food ink. The hardness was highly positively correlated with Maxwell model parameters F-e, F-1, F-2, F-3,F- and Peleg constant k(2) (0.57) and negatively correlated with k(1) (-0.76).