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

Abstract

<b><i>Background:</i></b> Spinosyns, products of secondary metabolic pathway of <i>Saccharopolyspora spinosa</i>, show high insecticidal activity, but difficulty in enhancing the spinosad yield affects wide application. The fermentation process is a key factor in this case. <b><i>Methods:</i></b> The response surface methodology (RMS) and artificial neural network (ANN) modeling were applied to optimize medium components for spinosad production using <i>S. spinosa </i>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. <b><i>Results:</i></b> The regression coefficients (R²) 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. <b><i>Conclusion:</i></b> The hybrid ANN/GA approach provides a viable alternative to the conventional RSM approach for the modeling and optimization of fermentation processes.