Comparison of Trial and Error and Genetic Algorithm in Neural Network Development for Estimating Farinograph Properties of Wheat-flour Dough

Abstract

Background and Objectives: Rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. Our purpose in the present research is to apply soft computation tools for predicting the rheological properties of dough out of simple measurable factors. Materials and Methods: One hundred samples of white flour were collected from different provinces of Iran. Seven physic-chemical properties of flour and Farinogram parameters of dough were selected as neural network’s inputs and outputs, respectively. Trial-and-error and genetic algorithm (GA) were applied for developing an artificial neural network (ANN) with an optimized structure. Feed-forward neural networks with a back-propagation learning algorithm were employed. Sensitivity analyses were conducted to explore the ability of inputs in changing the Farinograph properties of dough. Results: The optimal neural network is an ANN-GA that evolves a four-layer network with eight nodes in the first hidden layer and seven neurons in the second hidden layer. The average of normalized mean square error, mean absolute error and correlation coefficient in estimating the test data set was 0.222, 0.124 and 0.953, respectively. According to the results of sensitivity analysis, gluten index was selected as the most important physicochemical parameter of flour in characterization of dough’s Farinograph properties. Conclusions: An ANN is a powerful method for predicting the Farinograph properties of dough. Taking advantages of performance criteria proved that the GA is more powerful than trial-and-error in determining the critical parameters of ANN’s structure, and improving its performance. Keywords: Artificial neural network, Genetic algorithm, Rheological characterization, Wheat-flour dough

Keywords

• Artificial neural network
• Genetic algorithm
• Rheological characterization
• Wheat-flour dough