APPLICATION OF D-OPTIMAL MIXTURE DESIGN AND ARTIFICIAL NEURAL NETWORK IN OPTIMIZING THE COMPOSITION OF FLOURS FOR PREPARATION OF GLUTEN-FREE BREAD

Abstract

D-optimal Mixture Design (DMD) combined with Numerical optimization (NO) and Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) were used in this study to optimize the proportions of pearl millet flour (PMF), red lentil flour (RLF), and mung bean flour (MLF) for preparing gluten-free bread. Based on the value of mean squared error, absolute average deviation and coefficient of determination, the ANN model was found superior to DMD models in predicting the value of responses. The optimum composition of flour obtained using the DMD method was 69.44 g of PMF, 21 g of RLF and 9.56 g of MLF, whereas using the ANNGA technique, it was 68.25 g of PMF, 23.12 g of RLF and 8.63 g of MLF. Sensory analysis indicated that the bread prepared using these two compositions were in the “like slightly” category in terms of overall acceptability.