Machine learning-based prediction models unleash the enhanced production of fucoxanthin in Isochrysis galbana
This study highlights the potential of machine learning models, particularly Random Forest, to optimize parameters influencing microalgal growth and fucoxanthin production. This approach offers a more efficient alternative to conventional methods, providing valuable insights into improving fucoxanthin production in microalgal cultivation. The findings suggest that leveraging diverse ML models can enhance the predictability and efficiency of fucoxanthin production, making it a promising tool for industrial applications.
Authors
- R. Chandrasekaran (ORCID: https://orcid.org/0000-0002-1553-0907)
- Brigitte Vannier (ORCID: https://orcid.org/0000-0002-6870-507X)
- Annapurna Jonnalagadda (ORCID: https://orcid.org/0000-0002-4698-7707)
- Janani Manochkumar
- Siva Ramamoorthy (ORCID: https://orcid.org/0000-0001-7509-8602)
- Aswani Kumar Cherukuri (ORCID: https://orcid.org/0000-0001-8455-9108)
- Dao Janjaroen
Institutions
- Chulalongkorn University (TH)
- Université de Poitiers (FR)
- Vellore Institute of Technology University (IN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2024-10-16
- DOI
- https://doi.org/10.3389/fpls.2024.1461610
- Citations
- 15
- Primary Topic
- Algal biology and biofuel production
- Type
- article
- Field-Weighted Citation Impact
- 1.54