Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production.
- Resource Type
- Academic Journal
- Authors
- Sarmah N; Department of Process Engineering & Technology Transfer, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India; Chemical and Environmental Engineering, School of Engineering, RMIT University, Melbourne, VIC 3001, Australia.; Mehtab V; Department of Process Engineering & Technology Transfer, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.; Bugata LSP; Department of Applied Biology, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India.; Tardio J; Centre for Advanced Materials and Industrial Chemistry, RMIT University, Melbourne, VIC 3001, Australia.; Bhargava S; Centre for Advanced Materials and Industrial Chemistry, RMIT University, Melbourne, VIC 3001, Australia.; Parthasarathy R; Centre for Advanced Materials and Industrial Chemistry, RMIT University, Melbourne, VIC 3001, Australia; Chemical and Environmental Engineering, School of Engineering, RMIT University, Melbourne, VIC 3001, Australia.; Chenna S; Department of Process Engineering & Technology Transfer, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India. Electronic address: sumana@iict.res.in.
- Source
- Publisher: Elsevier Applied Science Country of Publication: England NLM ID: 9889523 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2976 (Electronic) Linking ISSN: 09608524 NLM ISO Abbreviation: Bioresour Technol Subsets: MEDLINE
- Subject
- Language
- English
A hybrid machine learning (ML) aided experimental approach was proposed in this study to evaluate the growth kinetics of Candida antarctica for lipase production. Different ML models were trained and optimized to predict the growth curves at various substrate concentrations. Results on comparison demonstrate the superior performance of the Gradient boosting regression (GBR) model in growth curves prediction. GBR-based growth kinetics was found to be matching well with the results of the conventional experimental approach while significantly reducing the experimental effort, time, and resources by ∼ 50%. Further, the activity and enzyme kinetics of lipase produced in this study was investigated on hydrolysis of p-nitrophenyl butyrate resulting in a maximum lipase activity of 24.07 U at 44 h. The robustness and significance of developed kinetic models were ensured through detailed statistical analysis. The application of the proposed hybrid approach can be extended to any other microbial process.
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