Optimization of fermentation medium for triterpenoid production from Antrodia camphorata ATCC 200183 using artificial intelligence-based techniques

Abstract

In this study, alteration in morphology of submergedly cultured Antrodia camphorata ATCC 200183 including arthroconidia, mycelia, external and internal structures of pellets was investigated. Two optimization models namely response surface methodology (RSM) and artificial neural network (ANN) were built to optimize the inoculum size and medium components for intracellular triterpenoid production from A. camphorata. Root mean squares error, R ², and standard error of prediction given by ANN model were 0.31%, 0.99%, and 0.63%, respectively, while RSM model gave 1.02%, 0.98%, and 2.08%, which indicated that fitness and prediction accuracy of ANN model was higher when compared to RSM model. Furthermore, using genetic algorithm (GA), the input space of ANN model was optimized, and maximum triterpenoid production of 62.84 mg l⁻¹ was obtained at the GA-optimized concentrations of arthroconidia (1.78 × 10⁵ ml⁻¹) and medium components (glucose, 25.25 g l⁻¹; peptone, 4.48 g l⁻¹; and soybean flour, 2.74 g l⁻¹). The triterpenoid production experimentally obtained using the ANN–GA designed medium was 64.79 ± 2.32 mg l⁻¹ which was in agreement with the predicted value. The same optimization process may be used to optimize many environmental and genetic factors such as temperature and agitation that can also affect the triterpenoid production from A. camphorata and to improve the production of bioactive metabolites from potent medicinal fungi by changing the fermentation parameters.