Ultrasound–assisted extraction optimization of phenolic compounds from Psidium guajava L. using artificial neural network-genetic algorithm

Abstract

Artificial Neural Network-Genetic Algorithm (ANN-GA) was used in this work to model the extraction parameters using Ultrasound Assisted Extraction (UAE) in producing phenolic compounds of guava (Psidium guajava L.) leaves. The ANN-GA model was used to evaluate and predict the effect of frequency, temperature, time, and their interaction under optimum conditions. The controlled parameters were frequency (20-40 kHz), temperature (2535 °C), and sonication time (20-40 minutes). Furthermore, the input and output parameters were modeled by LM-back propagation ANN. The optimization variable was then determined by GA. ANN trial and error method was used to determine the bias, weight, and number of hidden layer nodes. The fitness function for optimization with GA used the bias and weight which were obtained from ANN. The prediction of total phenolic on guava leaves using the ANN model shows the mean absolute precentage error value is 13.07. Based on the experiment the optimum conditions were obtained at 40 kHz, 35°C,30 minutes and resulted 589.02 ppm. While ANN-GA model obtained optimum results at 39.79 kHz, 34.83°C, 20 min, then the mean absolute error optimization was 3.46. It can be concluded that ANN-GA model can be used for optimizing the phenolic yields of guava leaves.