Multi‐response optimization of process parameters of saponin‐based model foam using Taguchi method and gray relational analysis coupled with principal component analysis

Abstract

Foam is a two-phase system in which continuous liquid and discontinuous gas phases interact. Maintaining equilibrium between these phases can only be achieved by optimizing the properties of the foam. The aim of the current research is to optimize process parameters (PPs) such as protein type, hydrocolloid concentration, hydrocolloid type, and mixing temperature in the preparation of model foam using the Taguchi method (TM) and gray relational analysis (GRA) in conjunction with principal component analysis (PCA). The experiments were performed using the Taguchi orthogonal array (L16) and then the effects of PPs on the overrun (OR), bubble size (BS), and loss tangent (LT) were investigated. The results showed that GRA-PCA performed better than TM in optimizing the multiple responses. Consequently, the foam optimized for OR, LT, and BS could be prepared by whipping a sugar-containing solution with saponin (0.096%), whey protein concentrate (0.5%) and pectin (0.05%) at 80°C. Practical applications There is a need for alternative foaming agents for foam production in the food industry. For this purpose, optimization methods such as Taguchi method (TM) and gray relational analysis (GRA)- principal component analysis (PCA) were used to determine better foaming properties (higher foamability and more elasticity) of the test sample with a combination of Gypsophila saponin, milk proteins, and hydrocolloids. As a result, it was found that GRA-PCA is a more effective optimization method than TM in multi-response optimization of food foams.