MYCOLab Ai Logo - gold and army green

Enhanced Production of Mycelium Biomass and Exopolysaccharides of Pleurotus ostreatus by Integrating Response Surface Methodology and Artificial Neural Network

Summary

This research focuses on improving the production of valuable compounds from oyster mushroom using advanced optimization techniques and artificial intelligence. The study demonstrates how modern technology can enhance the efficiency of producing beneficial mushroom compounds. Impacts on everyday life: • More efficient production of natural health-promoting compounds • Potential for more affordable mushroom-based supplements and medicines • Advancement in sustainable biotechnology processes • Development of user-friendly software tools for bioprocess optimization • Improved methods for producing natural antioxidants and immune-boosting compounds

Background

Pleurotus ostreatus is an edible fungus valued for its culinary, nutritional, and pharmaceutical properties. While traditionally grown on solid substrates, submerged cultivation offers a quicker, easier, and more controlled method for producing mycelium biomass and bioactive compounds. The optimization of cultivation conditions is crucial for enhancing mycelium biomass and exopolysaccharide (EPS) production.

Objective

This study aimed to enhance the production of mycelium biomass and exopolysaccharides from Pleurotus ostreatus in submerged fermentation by optimizing culture conditions using Response Surface Methodology (RSM) and developing predictive models using Artificial Neural Network (ANN).

Results

Under optimized conditions (temperature 22.9°C, pH 5.6, and agitation 138.9 rpm), maximum biomass yield of 36.45 g/L and EPS yield of 6.72 g/L were achieved. The ANN model with optimized network structure demonstrated high prediction accuracy with an R² value of 0.99 and mean squared error of 1.9 for the validation set.

Conclusion

The study successfully demonstrated the effectiveness of combining GA-RSM and ANN approaches for optimizing and predicting mycelium biomass and EPS production from P. ostreatus. The developed ANN-based GUI software provided accurate predictions while saving time and resources compared to traditional methods.
  • Published in:Bioresource Technology,
  • Study Type:Experimental Research,
  • Source: 10.1016/j.biortech.2024.130577
Scroll to Top