Response surface methodology (RSM) and artificial neural network (ANN) integrated optimization for lipase production by Bacillus holotolerans
Author:
Publisher
Springer Science and Business Media LLC
Subject
Microbiology,Applied Microbiology and Biotechnology,Biochemistry, Genetics and Molecular Biology (miscellaneous),Food Science
Link
https://link.springer.com/content/pdf/10.1007/s43393-023-00220-0.pdf
Reference49 articles.
1. Basri M, Rahman RN, Ebrahimpour A, Salleh AB, Gunawan ER, Rahman MB. Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester. BMC Biotechnol. 2007;7:53.
2. Ebrahimpour A, Abd Rahman RN, Ean Ch’ng DH, Basri M, Salleh AB. A modeling study by response surface methodology and artificial neural network on culture parameters optimization for thermostable lipase production from a newly isolated thermophilic Geobacillus sp. strain ARM. BMC Biotechnol. 2008;8:96.
3. Lau H-L, Wong FWF, Rahman RNZRA, Mohamed MS, Ariff AB, Hii S-L. Optimization of fermentation medium components by response surface methodology (RSM) and artificial neural network hybrid with genetic algorithm (ANN–GA) for lipase production by Burkholderia cenocepacia ST8 using used automotive engine oil as substrate. Biocatal Agric Biotechnol. 2023;50:102696.
4. Khaleghi MK, Savizi ISP, Lewis NE. Shojaosadati SAJBJ: Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters. Biotechnol J. 2021;16(11):2100212.
5. Sarmah N, Mehtab V, Bugata LSP, Tardio J, Bhargava S, Parthasarathy R, Chenna SJBT. machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production. Bioresour Technol. 2022;352:127087.
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