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

Amaresh Kadival; Jayeeta Mitra; Manish Kaushal; Rajendra Machavaram

doi:10.1111/jtxs.12817

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

J Texture Stud. 2023 Dec 5. doi: 10.1111/jtxs.12817. Online ahead of print.

Authors

Amaresh Kadival¹, Jayeeta Mitra¹, Manish Kaushal², Rajendra Machavaram¹

Affiliations

¹ Department of Agricultural and Food Engineering, IIT Kharagpur, Kharagpur, India.
² Department of Chemical Engineering, IIT Kharagpur, Kharagpur, India.

PMID: 38053288
DOI: 10.1111/jtxs.12817

Abstract

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 η*, 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 , F₁ , F₂ , F_3, and Peleg constant k₂ (0.57) and negatively correlated with k₁ (-0.76).

Keywords: 3D food printing; artificial neural network; frequency sweep study; stress relaxation modeling; texture profile analysis.

Grants and funding

All India Coordinated Research Projects on Post Harvest Engineering & Technology (AICRP on PHET) funded by Indian Council of Agricultural Research (ICAR)