Statistical optimization of process variables for the large-scale production of Metarhizium anisopliae conidiospores in solid-state fermentation

Bioresour Technol. 2008 Apr;99(6):1530-7. doi: 10.1016/j.biortech.2007.04.031. Epub 2007 Jun 21.

Abstract

Optimization of conidial production was achieved by response surface methodology (RSM), a powerful mathematical approach widely applied in the optimization of fermentation process, using the three substrates; rice, barley and sorghum at variable pH, moisture content and yeast extract concentrations. These three factors were found to be important, affecting Metarhizium anisopliae spore production. A 2(3) full factorial central composite design and RSM were applied to determine the optimal concentration of each variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. Moisture content of 75.68% for sorghum, 73.21% for barley and 22.34% for rice produced optimal results. Maximal conidial yield was recorded for rice at a pH of 7.01; at 7.06 for sorghum and at 6.76 for barley.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aspergillus / metabolism
  • Bioreactors
  • Biotechnology / instrumentation
  • Biotechnology / methods*
  • Fermentation
  • Hordeum
  • Hydrogen-Ion Concentration
  • Industrial Microbiology
  • Metarhizium / metabolism*
  • Models, Theoretical
  • Oryza
  • Regression Analysis
  • Reproducibility of Results
  • Sorghum