application of artificial neural network and genetic algorithm for predicting three important parameters in bakery industries

farinograph is the most frequently used equipment for empirical rheological measurements of dough. it’suseful to illustrate quality of flour, behavior of dough during mechanical handling and texturalcharacteristics of finished products. the percentage of water absorption and the development time of doughare the most important parameters of farinography for bakery industries during production. however,farinograph quality number is also a profitable factor for rapid evaluation of flour. our purpose in presentresearch is to apply artificial neural networks (anns) for predicting three important parameters offarinograph from simple measurable physicochemical properties of flour. genetic algorithm (ga) was alsoapplied in the training phase for optimizing different parameters of ann’s structure and inputs. sensitivityanalyses were also conducted to explore the ability of inputs in predicting the networks outputs. two neuralnetworks were developed; the first for modeling water absorption and dough development time and thesecond for modeling farinograph quality number. both developed anns using ga have excellent potentialin predicting the farinograph properties of dough. in developed models, gluten index and zeleny, suitableparameters for qualitative measurements of samples, played the most important role for predicting doughfarinograph characterisations.