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Lentinula edodes Substrate Formulation Using Multilayer Perceptron-Genetic Algorithm: A Critical Production Checkpoint

Summary

This research focused on optimizing the growing conditions for shiitake mushrooms using advanced computer modeling techniques. The study found the ideal mixture of agricultural waste materials (bagasse, wheat bran, and sawdust) that produces the best mushroom growth. This has important implications for both commercial mushroom producers and environmental sustainability. Impacts on everyday life: – More efficient production of nutritious and medicinal shiitake mushrooms – Reduced agricultural waste through recycling into mushroom substrate – Lower production costs could lead to more affordable mushroom products – Environmental benefits from reusing industrial byproducts – Improved access to natural medicines and health-promoting foods

Background

Shiitake (Lentinula edodes) is one of the most widely grown and consumed mushroom species worldwide, valued as both a food and medicine source due to its rich nutrient content including minerals, vitamins, and bioactive compounds. The reuse of agricultural and industrial residues as growing substrates is important from both ecological and economic perspectives.

Objective

To analyze and optimize the running length (RL) and running rate (RR) of L. edodes cultured on different substrate compositions using multilayer perceptron-genetic algorithm (MLP-GA) and various regression models. The study aimed to determine the optimal ratios of bagasse, wheat bran, and beech sawdust for maximum mycelial growth.

Results

The MLP-GA models showed superior prediction accuracy (92% and 97%) compared to regression models (52% and 71%) for RL and RR respectively. Optimization analysis revealed that maximum L. edodes running length (10.69 cm) could be achieved with a substrate containing 15.1% bagasse, 45.1% wheat bran, and 10.16% beech sawdust at 28.43 days. The highest running rate (0.44 cm/day) was obtained with 30.7% bagasse and 90.4% wheat bran.

Conclusion

The study demonstrated that MLP-GA is an effective mathematical tool for predicting and optimizing complex systems like medicinal mushroom growth. The high degree of fit between forecasted and actual values confirmed the superior performance of the developed MLP-GA models compared to traditional regression approaches.
  • Published in:Frontiers in Microbiology,
  • Study Type:Experimental Research,
  • Source: 10.3389/fmicb.2024.1366264
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