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

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 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 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 g/cm ³ , water absorption index 5.69 ml/g, compression force 20.80 N, and cutting strength 10.81 N. Practical applications: Coconut milk residue, which is rich in dietary fiber and polyphenols, is the main underutilized co-product of virgin coconut oil, coconut milk powder, coconut milk yogurt, and flavored coconut milk processing industries. It can be incorporated into the rice-corn mixture to produce a healthy snack food by extrusion. Hence, this study was focused on optimizing the extrusion conditions and flour ratio using multiple linear regression and artificial neural network to obtain a desirable extruded product. The promising results suggest that CMR can be incorporated with rice and corn to produce extrudates with improved nutrition.