Applied Biochemistry and Biotechnology

, Volume 118, Issue 1–3, pp 33–46 | Cite as

Evolutionary operation-factorial design technique for optimization of conversion of mixed agroproducts into gallic acid

  • Gargi Mukherjee
  • Rintu Banerjee


This article presents the optimization of gallic acid production using filamentous fungi from tannin-rich mixed substrates taking into account the interaction effects of six variable process parameters. The methodology adopted for optimization was the evolutionary operation (EVOP)-factorial design technique. This technique combines the factorial method for designing experiments with the EVOP methodology for analyzing the experimental results systematically and arriving at conclusions according to its decision-making procedure. Standard deviation and error limits based on 95% confidence were calculated according to the relationship given in the literature. It was found that the best combinations of the process parameters at the optimum levels were 30°C, 80% relative humidity, pH 5.0, 48-h incubation period, 3 mL of induced inoculum, and 35 g of mixed substrate, resulting in a gallic acid yield of 94.8% under modified solid-state fermentation.

Index Entries

Mixed substrates modified solid-state fermentation optimization evolutionary operation-factorial design filamentous fungi gallic acid 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kennedy, M. and Krouse, D. (1999), J. Ind. Microbiol. Biotechnol. 23, 456–475.CrossRefGoogle Scholar
  2. 2.
    Fisher, R. A. (1926), J. Min. Agric. 33, 503–513.Google Scholar
  3. 3.
    Lekha, P. K. and Lonsane, B. K. (1997), Adv. Appl. Microbiol. 44, 215–260.PubMedCrossRefGoogle Scholar
  4. 4.
    Hadi, T. A., Banerjee, R., and Bhattacharyya, B. C. (1994), Bioprocess Eng. 11, 239–243.Google Scholar
  5. 5.
    Kar, B., Banerjee, R., and Bhattacharyya, B. C. (1999), J. Ind. Microbiol. Biotechnol. 23, 173–177.CrossRefGoogle Scholar
  6. 6.
    Mukherjee, G. and Banerjee, R. (2003), Chem. Today 21(1/2), 59–62.Google Scholar
  7. 7.
    Pandey, A., Selvakumar, P., Soccol, C. R., and Nigam, P. (1999), Curr. Sci. 77(1), 149–162.Google Scholar
  8. 8.
    Banerjee, R. and Bhattacharyya, B. C. (1993), Biotechnol. Bioeng. 41, 67–71.CrossRefGoogle Scholar
  9. 9.
    Tunga, R., Banerjee, R., and Bhattacharyya, B. C. (1999), J. Biosci. Bioeng. 87, 125–131.CrossRefGoogle Scholar
  10. 10.
    Davis, O. L. (1954), in Design and Analysis of Industrial Experiments, Hafner, New York, pp. 440–480.Google Scholar
  11. 11.
    Adler, Y. P., Markos, E. V., and Granovsky, Y. V. (1975), in The Design of Experiments to Find Optimal Conditions, Mir Publishers, Moscow, pp. 118–144.Google Scholar
  12. 12.
    Box, G. E. P. and Hunter, J. S. (1959), Technometric 1, 77–95.CrossRefMathSciNetGoogle Scholar
  13. 13.
    Pearson, E. S. and Hartley, H. O. (1962), in Biometrika Tables for Statisticians Vol. 1 (2nd ed.), University Press, Cambridge, UK, p. 46.Google Scholar
  14. 14.
    Adler, Y. P. and Granovsky, Y. V. (1972), A Review of Applied Studies by Experimental Design, Reprint No. 33 (2nd ed.), LSM, Moscow State University, Moscow.Google Scholar
  15. 15.
    Banerjee, R. and Bhattacharyya, B. C. (2003), Biochem. Eng. J. 13, 149–155.CrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2004

Authors and Affiliations

  1. 1.Department of Agriculture and Food Engineering, Indian Institute of TechnologyMicrobial Biotechnology and Downstream Processing LaboratoryKharagpurIndia

Personalised recommendations