Paecilomyces tenuipes N45 submerged fermentation condition optimization using artificial neural network-genetic algorithm

Abstract

For improving the antifungal activities of Paecilomyces tenuipes N45 against Candida albicans, artificial neural network-genetic algorithm was employed to optimize its fermentation condition in this paper. Key factors were chosen by Plackett-Burman design firstly. Then, Box-Behnken design was used to further optimize the optimum value of three key factors. Finally, response surface methodology and artificial neural network-genetic algorithm were used to model and optimize the experimental results obtain from Box-Behnken design. The obtained optimal culture conditions was as follows: pH 7.207, agitation speed 150 r/min, temperature 25.8 °C, inoculation amount 5%, culture volume 100 mL/250 mL flask, culture time 4 d, seed age 1.0 d. The predicted diameter of inhibition zone of P. tenuipes N45 under the optimum fermentation conditions was 1.38 cm. Five validation experiments were implemented with the optimum fermentation conditions and their average diameter of inhibition zone was 1.40 cm, which was 1.22-fold increased compared with the original conditions. The relative error between the experimental values and the expected value was 1.45 %, which indicated that the expected value fit experimental values.