Modeling the physicochemical characteristics of ultra-refined low-fat feta cheese produced with fat substitutes and additional starter by artificial neural network method

Nutritional problems according to high amounts of fat in some cheeses have caused the use of fat substitutes to be investigated. Modeling the hardness, whey and pH of UF low-fat feta cheese with three levels of lecithin at zero, 1 and 2 g/kg, three levels of zero, 10 and 20 g/kg of whey powder, xanthan gum. in amounts of zero, half and 1 g/kg and additional starter of Lactobacillus paracasei in two amounts of 1 and 3 g/l with artificial neural network in order to determine the best type of transfer function, type of learning law and percentage of data used for training, evaluation and the test was performed based on the lowest error and the highest correlation coefficient. The results showed that the best model for predicting cheese hardness changes was an algorithm with a hidden layer and the number of 7 neurons, under the sigmoid transfer function with Levenberg's learning law, which could show a good correlation coefficient (0.985). For whey, a model with a hidden layer, the number of 3 neurons and the tangent transfer function and Levenberg's learning law created the best algorithm with a correlation coefficient of 0.908. Also, the pH of cheese was predicted by a model with a hidden layer, the number of 8 neurons and the sigmoid transfer function under Levenberg's learning law and the correlation coefficient was 0.8493. The best percentage of data for education, evaluation and testing of hardness, whey and pH values were obtained 5.35.60, 30.10.60 and 45.20.35 respectively