Optimizing the Conditions for Polysaccharides Ultrasonic-assisted Extraction in Mycelium of Paecilomyces tenuipes Pt196 Using Statistical Approach

Abstract

Purpose) In order to enhance the extraction rate of polysaccharides, a series of statistical approach was used to optimize the ultrasonic-assisted extraction conditions from the mycelium of Paecilomyces tenuipes Pt196. (Method) The optimization of conditions was carried out in two stages. Firstly, the effects of various experimental parameters considered for the investigation (ultrasonic power, ultrasonic time and liquid- solid ratio) were studied using the method of single factor test design experiments. Secondly, a 15-run Box-Behnken design was performed to optimize the extraction conditions of polysaccharides. The experimental results of Box-Behnken design were analyzed by response surface methodology and artificial neural network together with genetic algorithm. (Results) The optimum conditions for polysaccharides extraction obtained by the application of artificial neural network-genetic algorithm were ultrasonic time 345s, ultrasonic power 320 W and liquid-solid ratio 95 mL·g -1 . extraction conditions to obtain highest extraction rate of polysaccharides. Response surface methodology (RSM) is introduced as one of the most commonly used tool to study the effects of individual measured parameters and their interactions on response variable (10) . This methodology can greatly reduce the number of experiments and enhance the work efficiency (11) . Another approach used in this study is artificial neural networks (ANN) coupled with genetic algorithm (GA). Unlike the regression method, ANN is a powerful and reliable tool for solving nonlinear problems (12) . The genetic algorithm is an efficient optimization procedure based on natural evolution. It can be used to optimize the complex problems. Many experimental studies show that ANN - GA is powerful tool in the design of optimal process in fermentation research and superior to RSM (13) . The present work attempts to relate the effects of the extraction conditions on the extraction rate of polysaccharides from the mycelium of Paecilomyces tenuipes Pt196 using a sequential statistical strategy including Box-Behnken design (BBD), multivariate quadratic regression (MQR) model and ANN model.