A hybrid of response surface methodology and artificial neural network in optimization of culture conditions of mycelia growth of Antrodia cinnamomea
Journal
Biomass and Bioenergy
Journal Volume
158
Date Issued
2022
Author(s)
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
Subjects
Antrodia cinnamomea
Artificial neural network (ANN)
Biomass production
Mycelia growth
Response surface methodology (RSM)
Backpropagation
Biomass
Cultivation
Ecology
Surface properties
Artificial neural network
Biomass productions
Commercial usage
Culture conditions
Key factors
Optimisations
Response surface methodology
Response-surface methodology
Neural networks
artificial neural network
biomass
correlation
fungus
microbial activity
optimization
response surface methodology
Taiwan
SDGs
Type
journal article