Prediction of Extensograph Properties of Wheat‐flour Dough: Artificial Neural Networks and a Genetic Algorithm Approach

Abstract

Wheat‐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 APPLICATIONS Extensograph 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.

Impact of Flour Protein Content and Freezing Conditions on the Quality of Frozen Dough and Corresponding Steamed Bread

“…It was found that both R max and extensibility increased with protein levels for frozen dough, and this is in agreement with results reported by other researchers (Abbasi et al 2012;Faegestad et al 2000;Lin et al 2003;Uthayakumaran et al 1999). Using flours with protein contents of 10-13 %, Abbasi et al (2012) showed a positive correlation between protein content and extensibility, and between protein content and resistance to extension, with correlation coefficients of 0.4 and 0.3, respectively.…”

“…Using flours with protein contents of 10-13 %, Abbasi et al (2012) showed a positive correlation between protein content and extensibility, and between protein content and resistance to extension, with correlation coefficients of 0.4 and 0.3, respectively. Those cited studies above were done on fresh dough, and thus, the results of this study provided evidence that frozen dough maintained similar relationships between extensional rheological properties and protein content as compared to fresh dough.…”

Dough bread from refined wheat flour partially replaced by grape peels: Optimizing the rheological properties

“…ANNs have been used in prediction of some parameters in various processes (Alvarez, 2009;Chakraborty & Shrivastava, 2019;Kashaninejad, Dehghani, & Kashiri, 2009;Koji c et al, 2019;Lamrini, Della Valle, Trelea, Perrot, & Trystram, 2012;Sablani, Baik, & Marcotte, 2002) and food quality evaluation (Broyart & Trystram, 2003;Funes et al, 2015;Goyache et al, 2001) or to predict dough rheological properties (farinograph peak, extensibility, and maximum resistance) from the torque developed during mixing (Ruan, Almaer, & Zhang, 1995). Other studies revealed that ANNs were used to predict the parameters of farinograph (Abbasi, Emam-Djomeh, & Seyedin, 2011), extensograph, alveograph, and fundamental rheological properties of dough from several physicochemical properties of flour (Abbasi et al, 2012;Abbasi, Emam-Djomeh, & Ardabili, 2014;Abbasi & Mohammadifar, 2015).…”

Assessment of the influence of gluten quality on highland barley dough sheet quality by different instruments

“…Farinograph and extensograph are the most common empirical evaluation equipment to indicate the development of the dough during mixing (Ktenioudaki et al, 2011), which have been widely used in the evaluation of processing performance of bread, steamed bread, noodles, and other products. And there is a certain correlation between the indexes measured by the two instruments (Abbasi et al, 2012; Huang et al, 2010; Miś et al, 2012).…”