https://scholars.lib.ntu.edu.tw/handle/123456789/605811
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Lee M.-H | en_US |
dc.contributor.author | Lu W.-B | en_US |
dc.contributor.author | Lu M.-K | en_US |
dc.contributor.author | FI-JOHN CHANG | en_US |
dc.creator | Lee M.-H;Lu W.-B;Lu M.-K;Chang F.-J. | - |
dc.date.accessioned | 2022-04-25T03:56:46Z | - |
dc.date.available | 2022-04-25T03:56:46Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 09619534 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123113735&doi=10.1016%2fj.biombioe.2022.106349&partnerID=40&md5=3dcc3b0c2f12579f9cf72c96d3f54301 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/605811 | - |
dc.description.abstract | Antrodia cinnamomea (A. cinnamomea) faces the challenge of coping with commercial usage in formulating nutraceuticals and functional foods in Taiwan. This research aimed to increase the biomass production of mycelia during the cultivation of A. cinnamomea using a methodology that hybrids Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RMS aimed to optimize the culture condition while ANN intended to identify the factors dominating biomass production. The Plackett-Burman design and 32 (27?2) fractional factorial designs identified four key factors. A four-factor six-level central composite design was used to investigate the correlation between the biomass and the key factors. The yield of RSM was 200% higher than that of the control medium. The proposed methodology offers reliable production of the medicinal fungus under optimum conditions in laboratory culture and reduces the cost, time and effort made, compared to the slow-growing propagation in nature. ANN opens a new opportunity of biomass prediction in microbial cultivation. Moreover, we provide the potential of hybrid RSM-ANN methods when encountering multifarious tasks in the future with the hope of bringing forward a new generation of biomass production technologies. ? 2022 Elsevier Ltd | - |
dc.relation.ispartof | Biomass and Bioenergy | - |
dc.subject | Antrodia cinnamomea | - |
dc.subject | Artificial neural network (ANN) | - |
dc.subject | Biomass production | - |
dc.subject | Mycelia growth | - |
dc.subject | Response surface methodology (RSM) | - |
dc.subject | Backpropagation | - |
dc.subject | Biomass | - |
dc.subject | Cultivation | - |
dc.subject | Ecology | - |
dc.subject | Surface properties | - |
dc.subject | Artificial neural network | - |
dc.subject | Biomass productions | - |
dc.subject | Commercial usage | - |
dc.subject | Culture conditions | - |
dc.subject | Key factors | - |
dc.subject | Optimisations | - |
dc.subject | Response surface methodology | - |
dc.subject | Response-surface methodology | - |
dc.subject | Neural networks | - |
dc.subject | artificial neural network | - |
dc.subject | biomass | - |
dc.subject | correlation | - |
dc.subject | fungus | - |
dc.subject | microbial activity | - |
dc.subject | optimization | - |
dc.subject | response surface methodology | - |
dc.subject | Taiwan | - |
dc.title | A hybrid of response surface methodology and artificial neural network in optimization of culture conditions of mycelia growth of Antrodia cinnamomea | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.1016/j.biombioe.2022.106349 | - |
dc.identifier.scopus | 2-s2.0-85123113735 | - |
dc.relation.journalvolume | 158 | - |
item.openairetype | journal article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | no fulltext | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Bioenvironmental Systems Engineering | - |
crisitem.author.orcid | 0000-0002-1655-8573 | - |
crisitem.author.parentorg | College of Bioresources and Agriculture | - |
顯示於: | 生物環境系統工程學系 |
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