Abstract
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. [Metho 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. [Result 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. [Conclusion Artificial neural network-genetic algorithm can effectively select the best extraction conditions of polysaccharides in this study.
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