Background: Pleurotus ostreatus, an Oyster mushroom, is the most prevalent edible mushrooms.
It served as a nutritional diet due to the presence of essential minerals and vitamins. It has offered
pleiotropic clinical applications. The production of mushroom is influenced significantly by the
process parameters. Hence, this paper is aimed to find the optimal conditions to maximize the production
of mushroom biomass using both response surface methodology and Artificial neural networks
Method: The central composite experimental design was chosen to find the optimum values of the
process parameters, viz., humidity, inoculum size, quantity of rice straw, and cooking time, to maximize
the production of oyster mushroom in a batch solid-state fermentation process. ANNs were trained
and validated using the experimental design and its response.
Results: The optimum values of humidity, inoculum size, rice straw, and cooking time were found to
be 78.4%, 8.64 g, 64.56 g, and 58.15 min, respectively. The production of Pleurotus ostreatus under
the optimized condition was 98.56 mg, which are two folds higher compared to the unoptimized production
Conclusion: The output of ANN model was compared with the finding of the response surface methodology
that showed a good agreement in the finding of the maximum production of mushroom biomass.