Optimization of the Formulation of Corn-based Gluten-free Raised Bread Using Whey Protein Concentrate and Microbial Transglutaminase

In this research, optimization of the formulation of corn-based gluten-free bread with whey protein concentrate and microbial transglutaminase enzyme was conducted with the purpose of minimizing baking loss and hardness and maximizing the specific volume, porosity and color index L* response surface Methodology and finally, estimating these responses by the neural network. The effects of two concentrations of microbial transglutaminase enzyme in the range of 0 to 1.5% (0, 0.75 and 1.5%) and whey protein in the range of 0 to 6%(0, 3 and 6%) by central composite design on specific volume parameters, baking loss, porosity, bread moisture content, hardness and color indexes L*, a* and b* of bread crust were investigated. Investigating the results indicated that the optimum conditions for the production of gluten-free bread based on Corn flour is created when the enzyme and whey protein concentrations are 0.37% and 4.54%, respectively. By increasing the enzyme, baking loss, hardness, moisture and a* index increased, but L* and b* indexes decreased. The results also indicated that by increasing the enzyme in the formulation of samples, we firstly observe increase and then decrease in porosity rate. Increasing the whey protein concentration in Corn flour-based gluten-free bread formulation resulted in an increase in specific volume and L* index, and a decrease in bread moisture, hardness and a* and b* indexes. The results of artificial neural network modeling indicated that a network with a hidden layer containing 7 neurons, with an 8-7-2 layout (a network with 2 inputs, 7 nodes (neurons) in the hidden layer and 8 outputs), has the best result in predicting the mentioned outputs. This network indicated the highest accuracy among the mentioned topologies with a correlation coefficient of 1.00 and mean square error of 0.0003.