PREDICTION OF EXTENSOGRAPH PROPERTIES OF WHEAT‐FLOUR DOUGH: ARTIFICIAL NEURAL NETWORKS AND A GENETIC ALGORITHM APPROACH

Abstract

ABSTRACTWheat‐flour dough is a viscoelastic material with nonlinear rheological behavior. Extensograph is a useful system for dough rheological measurement. Our purpose in this research was to apply soft computation tools for predicting the extensograph properties of dough from several physicochemical properties of flour. This study used the resulting model to suggest modifications of processing conditions for reducing economic loss and minimizing product quality deterioration. A generalized feed‐forward artificial neural network (ANN) with a back‐propagation learning algorithm was employed to estimate the extensograph properties of dough. Trial and error and genetic algorithm (GA) were applied in the training phase for developing an ANN with an optimized structure. Developed ANN using GA has excellent potential for predicting the extensograph properties of dough. Sensitivity analyses were conducted to explore the ability of inputs in predicting the extensograph properties of dough. Results showed gluten index was the most sensitive input in dough extensograph characterizations.PRACTICAL APPLICATIONSExtensograph is a suitable instrument for measuring the stretching properties of dough to make reliable statements about the baking behavior of the wheat‐flour dough in practical industrial applications and in research. Rheological measurements of each batch in the production line are very useful and make online and in‐time process adjustments possible, but this is usually impractical in an industrial setting. Therefore, accurate prediction of dough rheology could provide many benefits to the baking industry for satisfying consumer demands. In the current study, genetic algorithm‐neural network approach was applied to predict extensograph properties of dough as affected by physicochemical properties of flour. In comparison with trial and error, genetic algorithm can determine an artificial neural network's topology and inputs in less time with excellent performance in prediction. According to the results of sensitivity analyses, of the seven investigated inputs, changes in gluten index have the most effect on estimating extensograph properties of dough.