SIMULTANEOUS MULTIRESPONSE OPTIMIZATION OF THE MEDIUM FOR SUBMERGED FERMENTING IRPEX LACTEUS FR. MYCELIA USING DESIRABILITY FUNCTION

The aid of this paper was to optimize the fermentation medium of Irpex lacteus Mycelia for simultaneously enhancing the yields of mycelium, adenosine, intracellular polysaccharide, Cordyceps acid and protein. A sequential statistical strategy was investigated during this optimization process, which consisted of desirability function (DF), Plackett-Burman design (PBD), Box-Behnken design (BBD), Multi-quadratic regression (MQR), artificial neuron networks (ANN) and genetic algorithm (GA). Desirability value (Dv) developed by DF was used as criterion. Suitable carbon sources and nitrogen sources were chosen by single-factor test firstly. PBD combined with linear modeling method was used for identifying the significant component, BBD was used for further optimization. MQR and ANN were used for modeling the BBD data. Specially, MQR model was used for determining the individual effects and mutual interaction effects of the tested variables on Dv, ANN model was used for Dv prediction. While the ANN model was developed, genetic algorithm (GA) was employed to search for the optimum medium which was as follow (g/L): lactose 29.81, yeast extract powder 15, beef extract 15, KH2PO4·5H 2O 0.65, MgSO4·7H 2O 0.6, NaCl 0.0078 and VB1 0.281, with expected maximum Dv of 0.6302. The validation experiments with the optimum fermentation media were implemented in triplicate and the average Dv was 0.6245 which was twice as that without optimization.