Modeling of coconut milk residue incorporated rice‐corn extrudates properties using multiple linear regression and artificial neural network

Abstract

The effect of extrusion screw speed (200, 250, and 300 rpm), barrel temperature (100, 120, and 140 °C), and formulation (Coconut milk residue [CMR] 10–20%, corn flour 20–30% and rice flour 60%) on product characteristics like expansion ratio, bulk density, water solubility and water absorption index, compression force, and cutting strength were investigated using multiple linear regression (MLR) and artificial neural network (ANN). The coefficient of determination ( R to the power 2) of MLR ranged between 0.34 and 0.84, and the sum of squared error (SSE) ranged between 0.0009 and 292.51. Whereas, the ( R to the power 2) of ANN ranged between 0.41 and 0.94, and SSE ranged between 0.0001 and 214.81. This indicates its superior performance over MLR in the present study. The extrusion condition of 15% CMR, 25% corn flour, and 60% rice flour, at 220 rpm screw speed, and 140 °C barrel temperature were determined as optimum conditions for development of coconut milk residue incorporated rice‐corn based extrudates with a desirability value of 0.95 using MLR with optimum responses of expansion ratio 3.19, bulk density 0.08 gram per cubic centimeters, water absorption index 5.69 millilitres per gram, compression force 20.80 N, and cutting strength 10.81 N.