Optimization of Culture Medium for Maximal Production of Spinosad Using an Artificial Neural Network - Genetic Algorithm Modeling.

BACKGROUND

Spinosyns, products of secondary metabolic pathway of Saccharopolyspora spinosa, show high insecticidal activity, but difficulty in enhancing the spinosad yield affects wide application. The fermentation process is a key factor in this case.

METHODS

The response surface methodology (RMS) and artificial neural network (ANN) modeling were applied to optimize medium components for spinosad production using S. spinosa strain CGMCC4.1365. Experiments were performed using a rotatable central composite design, and the data obtained were used to construct an ANN model and an RSM model. Using a genetic algorithm (GA), the input space of the ANN model was optimized to obtain optimal values of medium component concentrations.

RESULTS

The regression coefficients (R(2)) for the ANN and RSM models were 0.9866 and 0.9458, respectively, indicating that the fitness of the ANN model was higher. The maximal spinosad yield (401.26 mg/l) was obtained using ANN/GA-optimized concentrations.

CONCLUSION

The hybrid ANN/GA approach provides a viable alternative to the conventional RSM approach for the modeling and optimization of fermentation processes.