Genetic Algorithm- Artificial Neural Network Modeling for Prediction the Antibacterial effect of Annatto Dye on Escherichia coli

The goal of this study was applying the genetic algorithm–artificial neural network (GA-ANN) modeling to predict the antibacterial effect of annatto dye on Escherichia coli population in mayonnaise. Annatto has antimicrobial and antioxidant properties in foods. Annatto dye was extracted and after filtration and concentration, was dried by vacuum oven. In this study, sauce samples containing 0, 0.1, 0.2 and 0.4 percent of annatto dye were prepared and stored at 4 and 25 ˚C. Sampling and colony counting were performed during 17 days and in triplicate. In order to predict the Escherichia coli population multi-layer perceptron neural network with 3 inputs and 1 output were used. Genetic algorithm method was used to optimization number of neurons in ANN hidden layer. The results showed a network with 7 neurons in hidden layer and using a Sigmoid tangent transfer function and the Levenberg–Marquardt (LM) optimization technique and 30%-20%-50% for training/ testing/ validating process can be well predict the Escherichia coli population (r=0.999) in the presence of annatto dye. Sensitivity analysis results by optimum ANN showed the storage time as the most factor for the predicting the Escherichia coli population.