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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.

Response surface methodology (RSM) was used to optimize sporulation medium components for improving spores concentration in I. lacteus fermentation broth. Five factors of sporulation medium components, glucose (X1), peptone (X2), yeast extract (X3) MgSO4·7H2O (X4) and VB1 (X5), were chosen to optimize through a five-level, five-factor central composite design (CCD) base on single factor experiments. The concentration of medium components which influenced the sporulation of I. lacteus was further optimized using RSM. The results show that The medium that consists of 4.15 g/L peptone, 2.04 g/L yeast extract, 5.10 g/L glucose, 0.17 g/L MgSO4·7H2O and 0.03 g/L VB1 enhance spores concentration significantly . The final spores concentration (2.19×105 /mL) by using the optimized medium was about five times higher than normal medium.

An experimental mixture design coupled with data analysis by means of both response surface methodology(RSM) and artificial neural network(ANN) was applied to explore the optimum process parameters for adenosine extraction from cultured mycelium of Cordyceps militaris. With the extraction rate of adenosine as index, the critical factors selected for the investigation were extracting temperature, extracting time and solid-liquid radio. The results obtained by the application of ANN were more reliable since better statistical parameters were obtained. The optimum extraction procedure was as follow: extracting time 2.3 h, extracting temperature 48 °C, solid-liquid ratio 1:38 g⋅mL-1. Under the optimal conditions, the corresponding response value predicted for adenosine production was 4.59 mg g-1, which was confirmed by validation experiments.