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.

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Alveograph is a useful device for rheological measurement of dough from biaxial extension. Our purpose in the current research was to apply soft computing tools for predicting the alveograph properties (maximum pressure for blowing the bubble [P], abscissa at bubble rupture [L], deformation energy [W]) using physicochemical properties of flour (protein, ash, wet gluten, gluten index, amylase activity, zeleny and particle size). Generalized feed-forward artificial neural networks (ANN) with back-propagation learning algorithms were employed to model the alveograph properties. A genetic algorithm (GA) was applied to optimize the parameters of the ANN's structure and inputs. Sensitivity analyses were conducted to explore the ability of the networks with using physicochemical properties of flour as inputs in predicting alveograph properties. The developed ANNs using GA were shown to have excellent potential in predicting the alveograph properties. Sensitivity analyses showed that zeleny (Sedimentation value) is the most sensitive input in predicting alveograph characteristics. PRACTICAL APPLICATIONSThe alveograph is a device to measure empirical rheological properties of dough during bubble inflation. Accurate prediction of dough rheology could provide many benefits to the baking industry, for example making inline process adjustments and modifying product formulation to satisfy consumer demands. In the current study, an ANN approach was applied to predict the alveograph properties of dough (P, L and W). The physicochemical properties of flour (protein, ash, wet gluten, gluten index, amylase activity, zeleny and particle size) and the parameters of alveography were set as inputs and outputs of the networks, respectively. A GA was applied as a suitable optimizer method to determine the best topology and inputs of the networks. The developed networks have excellent performance in prediction of outputs when compared with test data. Sensitivity analyses were also performed to investigate the suitability of inputs on estimating alveograph properties (outputs) of the dough.

Polycyclic aromatic hydrocarbons (PAHs) are a group of lipophilic organic compounds consisting of two or more fused aromatic rings. Ingestion through contaminated food and water is a major route of human exposure to PAHs. Smoked products are important sources of dietary PAHs. Therefore, this study evaluated the effect of smoking times on the content of PAH compounds. The result showed that smoking duration had a significant effect on Benzo[a]pyrene (BaP) and 4PAH indices in both cultivars (Hashemi and Domsiah) between the three test groups (nonsmoked rice, rice smoked for 10 and 14 days) (p < .05). The level of BaP index in nonsmoked rice samples was lower than the permitted limit (1 μg/kg), but increased after smoking and exceeded the permitted limit. The 4PAH index was too high in both nonsmoked and smoked rice samples and the smoking process increased further. Finally, the smoking process had a significant direct effect on PAH compounds in Hashemi and Domsiah rice during the 14 days after smoking. Practical applications The polycyclic aromatic compounds have been identified as toxic compounds in most smoked food products such as meat, rice, and fish. The staple food of most people in the Middle East is rice, whose smoked form is mostly consumed. This source contains high amounts of polycyclic aromatic hydrocarbons. It is almost impossible to accurately measure them by traditional methods. The combination of the ultrasound‐assisted extraction method and high‐performance liquid chromatography with a fluorescence detection (HPLC/FLD) device can be a great help in accurately identifying these compounds in two varieties of smoked rice. Since most smoked rice workshops were traditionally built and operated, combustion conditions were usually not controlled properly and control criteria were not considered; therefore, smoked rice with high amounts of PAH compounds was produced. There is a need for replacement of the traditional smoking process with the industrial smoking process, use wood instead of paddy, revise the rice standard, and establish control limits for PAH compounds, especially for smoked rice.