Regulation of tapioca starch 3D printability by yeast protein: Rheological, textural evaluation, and machine learning prediction

This article investigated the effects of yeast protein (YP) on gel rheology, texture, and 3D printability of tapioca starch and the feasibility of Principal component analysis (PCA) and support vector machine (SVM) algorithms for classification and prediction of printability. The results indicated that increasing YP content enhanced the viscosity, storage and loss moduli, and hardness, thereby improving extrudability and supportability of 3D printing. The addition of 15% YP exhibited the best 3D printing performance, but excessively high YP addition hindered ink extrusion. PCA analysis based on rheological and texture indices categorized the ink's 3D printing performance into four classes: poor support and low printing accuracy; good support but low printing accuracy; good support and high printing accuracy; and non-smooth extrusion. Furthermore, SVM algorithm used texture data to predict printability classification, with the highest prediction accuracy (91.67%) achieved at polynomial kernel among four different kernel functions. These results confirm that YP can serve as a potential ink for 3D printing and underscore SVM's efficacy in predicting ink's 3D printing performance. • YP enhanced 3D printing accuracy by regulating extrudability and supportability. • YP improved rheological properties and texture by regular molecular arrangement. • The inks 3D printability can be classified into four categories by PCA analysis. • SVM models can accurately predict ink printing performance based on texture data. • The poly kernel function had the highest prediction accuracy of 91.67%.