Develop a hybrid machine learning model for promoting microbe biomass production

Since the cultivation condition of microbe biomass production (mycelia yield) involves a variety of factors, it's a laborious process to obtain the optimal cultivation condition of Antrodia cinnamomea (A. cinnamomea). This study proposed a hybrid machine learning approach (i.e., ANFIS-NM) to identify the potent factors and optimize the cultivation conditions of A. cinnamomea based on a 32 fractional factorial design with seven factors. The results indicate that the ANFIS-NM approach successfully identified three key factors (i.e., glucose, potato dextrose broth, and agar) and significantly boosted mycelia yield. The interpretability of ANFIS rules made the cultivation conditions visually interpretable. Subsequently, a three-factor five-level central composite design was used to probe the optimal yield. This study demonstrates the proposed hybrid machine learning approach could significantly reduce the time consumption in laboratory cultivation and increase mycelia yield that meets SDGs 7 and 12, hitting a new milestone for biomass production.

Automated summary

A hybrid machine learning approach (ANFIS-NM) was proposed to optimize the cultivation conditions of Antrodia cinnamomea (A. cinnamomea) based on a 32 fractional factorial design. The approach successfully identified three key factors and significantly boosted mycelia yield. It reduces time consumption and increases mycelia yield, showing great potential for biomass production.