Application of artificial neural network coupling multiobjective particle swarm optimization algorithm to optimize<i>Pleurotus ostreatus</i>extraction parameters
Ayşenur Gürgen, Мustafa Sevindik
Abstract
In this research, extraction parameters were optimized for the maximization of the antioxidants of mushrooms. Firstly, the total antioxidant status (TAS) and total oxidant status (TOS) of mushrooms under different extraction parameters were determined. Then, artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO) were applied for modeling and optimization of the extraction process, respectively. The three variables affecting this process were extraction temperature (40 to 70 °C), extraction time (4 to 10 h) and extraction concentration (0.25 to 2 mg/mL). The developed ANN-based model could predict with a correlation coefficient of 0.986 and 0.991 for TAS and TOS values, respectively. The optimum extraction parameters were extraction temperature of 40.721 °C, extraction time of 6.267 h, and extraction concentration of 2 mg/mL. These results indicated that the ANN-MOPSO hybrid model provides an accurate prediction and optimization method for TAS and TOS values of P. ostreatus extracts.