1Department of Processing and Food Engineering, School of Electrical Engineering and Information Technology, Punjab Agricultural University, Ludhiana-141004
2School of Electrical Engineering and Information Technology, Punjab Agricultural University, Ludhiana-141004
Modeling and optimization of extrusion process was done with the help of genetic algorithm (GA). The response surface methodology (RSM) was used for the development of regression equations for the extrudate properties such as expansion ratio (ER), bulk density (BD), water absorption index (WAI), water solubility index (WSI), protein content (PC), crude fiber (CF), antioxidant capacity (AC), hardness (H), color change (CC) and overall acceptability (OA) with four independent variables: die temperature (0C), screw speed (rpm), corn proportion (%) and feed moisture content (%). For the optimum process conditions the equations of ER, WAI, WSI, PC, CF, AC, OA were maximized and BD, CC and H were minimized. Both individual and common optimization approaches indicated that die temperature of 1100C and corn grits: mosambi pomace-pulse powder (MPPP) ratio of 70: 30 were necessary for all the extrudate properties except for color change (CC). The optimum value of feed moisture content was 12% for all the extrudate properties except for OA, CC and AC whereas, optimum value of screw speed was 300 rpm except for BD, WSI and CC. The common optimum extrusion process conditions obtained were 70% corn proportion (30% MPPP) with 16% feed moisture content, 110°C die temperature and 300 rpm screw speed. The values predicted for common optimum conditions matched experimental extrudate properties more closely than those for individual optimum process conditions.
Modeling, optimization, genetic algorithm, response surface methodology
