https://scholars.lib.ntu.edu.tw/handle/123456789/631051
標題: | Develop a hybrid machine learning model for promoting microbe biomass production | 作者: | Kow, Pu Yun Lu, Mei Kuang Lee, Meng Hsin Lu, Wei Bin FI-JOHN CHANG |
關鍵字: | Adaptive Neuro-Fuzzy Inference System (ANFIS); Antrodia cinnamomea; Artificial Neural Network (ANN); Biomass; Response Surface Methodology (RSM) | 公開日期: | 1-二月-2023 | 出版社: | ELSEVIER SCI LTD | 卷: | 369 | 來源出版物: | Bioresource Technology | 摘要: | 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. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144060191&doi=10.1016%2fj.biortech.2022.128412&partnerID=40&md5=c4fb957fa461d65d65aeeb3c50df68bf https://scholars.lib.ntu.edu.tw/handle/123456789/631051 |
ISSN: | 09608524 | DOI: | 10.1016/j.biortech.2022.128412 |
顯示於: | 生物環境系統工程學系 |
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