Evaluation of gamma ray effect on wheat bakery properties in Omid, Roshan and Tabasai cultivars by artificial neural network

The genome of a plant is the most critical factor to control bakery- quality trait in wheat, where it can bemade by applying genetic variation upon using mutagens for its improvement. In this study, chemical and Farinograph experiments were investigated in T-66-58-60, O-64-1-10, RO-1, RO-3 and RO-5 lines from Tabassi, Omid, and Roshan cultivar, respectively. Also, the sigmoid transfer function was used for the assessment of factors by the model of feed-forward neural network with training method of levenberg-Marquardt algorithm. The chemical traits of Zeleny number, the hardness, wet gluten and protein content in the RO-3 line increased significantly compared with the control. Also, water absorption percentage and valorimeter value increased substantially in the O-64-1-10, whereas it was shown that the dough softening after 10 and 20 minutes decreased significantly compared with the control. The protein content, bread volume, Farinograph quality number and E10 properties had the most significant impact on the neural network model. The results show a positive effect of the irradiation on the improvement of dough quality properties.