Welcome to Chinese Journal of Biological Control,Today is

journal1 ›› 2017, Vol. 33 ›› Issue (2/3): 258-265.DOI: 10.16409/j.cnki.2095-039x.2017.02.017

• RESEARCH REPORTS • Previous Articles     Next Articles

Optimization of Solid-state Fermentation Culture for Biocontrol Agent Non-pathogenic Fusarium oxysporum FJAT-9290 by Response Surface Methodology

XIAO Rongfeng, ZHENG Meixia, LIU Bo, CHEN Yanping, ZHU Yujing, GE Cibin   

  1. Institute of Agricultural Bio-resources Research, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China
  • Received:2016-10-10 Online:2017-06-08 Published:2017-04-08

Abstract: The medium components were optimized for solid-state fermentation of a biocontrol agent, non-pathogenic Fusarium oxysporum FJAT-9290. The agricultural by-products, such as bran or pig raising litter from microbial fermentation beds (MFB), were used as basic culture media. A single factor experiment was carried out to investigate the effects of kernel, sucrose and sodium nitrate on the conidia of the strain. Then, the process parameters were optimized by using Box-Behnken experimental design and the results were analyzed with response surface methodology (RSM). A multiple quadratic regression model with conidia as response value was established to determine the optimal medium component. The results showed that the regression model performed highly significant difference (P<0.0001) and could be used to fit the experiment. When MFB and bran with a weight ratio of 17:3 were used as basic culture media, the optimal additive amounts of kernel, sucrose and sodium nitrate were confirmed to be 39.14%, 2.97% and 0.30%, respectively. The average conidia of the strain could reach to 2.48×108 conidia/g under condition of the optimal medium component. The RSM analysis also indicated that the influence on conidia were kernel>sucrose>sodium nitrate, and the interaction between sucrose and sodium nitrate was the most significant.

Key words: non-pathogenic Fusarium oxysporum, solid-state fermentation, culture medium optimization, response surface methodology

CLC Number: