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Dielectric spectroscopy with machine learning integrated approach to analysis the influence of N: P ratio on algae growth modeling for forecasting
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The integration of machine learning-based PCA, HCA, and LRM could be effective methods for estimating and forecasting algal cell biomass using dielectric spectroscopy.
“The results suggested that the integration of machine learning-based PCA, HCA, and LRM could be effective methods for estimating and forecasting algal cell biomass using dielectric spectroscopy.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [85]
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Metadata
- Authors
- Wang, S., Bingke, Y., Zhou, Y., Qu, Y., Zhang, D., Chen, J., Russel, M., & Biswas, K.
- Affiliations
- Chinese Research Academy of Environmental Sciences (CN)
- Dalian University of Technology (CN)
- Hubei Engineering University (CN)
- Peking University (CN)
- Corresponding author
- Chen, Junyi
- Russel, Mohammad
- Venue
- Microchemical Journal
- Publisher
- Elsevier BV
- Published
- 2024-05
- Volume
- 200
- Pages
- 110254
- ISSN
- 0026-265X (ISSN-L)
- OpenAlex
- W4392435160
- Cited by
- 6
- References
- 47
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 1
- FWCI
- 0.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Microchemical Journal (publishedVersion)
- Funders
- Dalian University of Technology
- Chinese Research Academy of Environmental Sciences
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Microwave and Dielectric Measurement Techniques
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- dielectric spectroscopy, N:P ratio, Desmodesmus, chlorophyll a, optical density, dielectric properties, machine learning, principal component analysis, hierarchical clustering, linear regression, algal bloom prediction
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