Modelling and optimization of extrusion cooking of broken rice - defatted soy flour-vegetable waste using genetic algorithms

Modelling and optimization of extrusion cooking of broken rice-defatted soy flour-radish leaf and empty green pea pod was done using genetic algorithm (GA). The extrusion process was designed in Box-Behnken and aimed at finding the levels of independent variables viz., moisture content (12-20 %), screw speed (300-500 rpm), die temperature (130°C- 170°C) cereal proportion of vegetable waste powder (65-85 %) and defatted soy powder (VW-SP: 15-35 %) using Response Surface Methodology. The responses for the extrudate properties studied were expansion ratio (ER), bulk density (BD), water absorption index (WAI), water solubility index (WSI), specific mechanical energy (SME), protein content (PC), crude fibre (CF), hardness (H), colour change (CC), and overall acceptability (OA). For the optimum process conditions, the equations of ER, WAI, WSI, PC, CF, and OA were maximized and BD, CC, SME and H were minimized. Both individual and common optimization approaches indicated that die temperature of 110°C and CP: VW-SP proportion ratio of 85:15 were necessary for all extrudate properties, except for PC and CF. The optimum value of feed moisture content was 12 % for all extrudate properties, except for BD and CF; whereas, optimum value of screw speed was 300 rpm, except for BD, WSI, and CC. The common optimum extrusion process conditions obtained were 85 % cereal proportion (15 % VW-SP) with 12 % feed moisture content, 110°C die temperature and 500 rpm screw speed. The values predicted for common optimum conditions matched with the experimental extrudate properties more closely than those for individual optimum process conditions.