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Ajdari Z. 1, 2, Ebrahimpour A. 3, Manan M. A. 4, Ajdari D. 1, Abbasiliasi S. 2, Hamid M. 5, Mohamad R. 2, Ariff A. B. 2
1 Department of Marine Biotechnology, Iranian Fisheries Research Organization, Tehran, Iran;
2 Department of Bioprocess Technology, Faculty of Biotechnology and Biomolecular Science, Putra Malaysia University, Selangor, Serdang, Malaysia;
3 Department of Food Science, Faculty of Food Science and Technology, Putra Malaysia UNiversity, Selangor, Serdang, Malaysia;
4 Biotechnology Research Center, Malaysian Agricultural Research and Development Institute, Kuala Lumpur, Malaysia;
5 Department of Microbiology, Faculty of Biotechnology and Biomolecular Science, Putra Malaysia University, Selangor, Serdang, Malaysia
Aim. Sporulation plays a vital role in fungi production, but it is of eminence importance in industrial fermentation.
Methods. In the current study, response surface methodology and an artificial neural network were employed to optimize the carbon and nitrogen sources in order to improve sporulation of Monascus purpureus FTC5391, a new local isolate. The best models for optimization of culture parameters were achieved by a multilayer full feedforward incremental back propagation network, and a modified response surface model using backward elimination, where the optimum condition for sporulation was: sucrose 10%, yeast extract 0.23%, casamino acid 0.5%, sodium nitrate 0.12 %, potato starch 0.13%, dextrose 1.73%, potassium nitrate 0.16%. In the optimum condition, the experimental sporulation was 2.61 X 106 spores/mL/12days, which was 2.6-fold higher than the standard condition (sucrose 5%, yeast extract 0.15%, casamino acid 0.25%, sodium nitrate 0.3%, potato starch 0.2%, dextrose 1%, potassium nitrate 0.3%).
Results and conclusion. The results of the response surface methodology and artificial neural network showed that all carbon and nitrogen sources tested had a significant effect on sporulation and growth rate (Pvalue <0.05). In addition, using the response surface methodology and artificial neural network together could cover some disadvantages of each method separately, and enable a more proper prediction model.