ArticleProceedings. Biological sciences2017
Phytoplankton growth rate modelling: can spectroscopic cell chemotyping be superior to physiological predictors?
Article in Proceedings. Biological sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 17 citations in OpenAlex.
- Study on the Changes in Immobilized Petroleum-Degrading Bacteria Beads in a Continuous Bioreactor Related to Physicochemical Performance, Degradation Ability, and Microbial Community.International journal of environmental research and public health · 2022Article
- A Mechanistic Model of Macromolecular Allocation, Elemental Stoichiometry, and Growth Rate in Phytoplankton.Frontiers in microbiology · 2020Article
- Glycolate from microalgae: an efficient carbon source for biotechnological applications.Plant biotechnology journal · 2019Article
- Quantitative macromolecular patterns in phytoplankton communities resolved at the taxonomical level by single-cell Synchrotron FTIR-spectroscopy.BMC plant biology · 2019Article
- Soil Particles and Phenanthrene Interact in Defining the Metabolic Profile ofFrontiers in microbiology · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Climate change has a strong impact on phytoplankton communities and water quality. However, the development of robust techniques to assess phytoplankton growth is still in progress. In this study, the growth rate of phytoplankton cells grown at different temperatures was modelled based on conventional physiological traits (e.g. chlorophyll, carbon and photosynthetic parameters) using the partial least square regression (PLSR) algorithm and compared with a new approach combining Fourier transform infrared-spectroscopy and PLSR. In this second model, it is assumed that the macromolecular composition of phytoplankton cells represents an intracellular marker for growth. The models have comparable high predictive power (R
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