Non-invasive monitoring of microalgae cultivations using hyperspectral imager

High expectations are placed on microalgae as a sustainable source of valuable biomolecules. Robust methods to control microalgae cultivation processes are needed to enhance their efficiency and, thereafter, increase the profitability of microalgae-based products. To meet this need, a non-invasive m...

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Bibliographic Details
Published in:Journal of applied phycology 2024-08, Vol.36 (4), p.1653-1665
Main Authors: Pääkkönen, Salli, Pölönen, Ilkka, Raita-Hakola, Anna-Maria, Carneiro, Mariana, Cardoso, Helena, Mauricio, Dinis, Rodrigues, Alexandre Miguel Cavaco, Salmi, Pauliina
Format: Article
Language:English
Subjects:
Algae
Aquatic microorganisms
Aquatic plants
Artificial neural networks
Biomass
Biomedical and Life Sciences
Biomolecules
Control methods
Cultivation
Ecology
Economics
Freshwater & Marine Ecology
Hyperspectral imaging
Laboratory experimentation
Life Sciences
Microalgae
Monitoring
Monitoring methods
Monoculture
Neural networks
Phytoplankton
Plant Physiology
Plant Sciences
Regression models
Robust control
Vegetation index
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Summary:High expectations are placed on microalgae as a sustainable source of valuable biomolecules. Robust methods to control microalgae cultivation processes are needed to enhance their efficiency and, thereafter, increase the profitability of microalgae-based products. To meet this need, a non-invasive monitoring method based on a hyperspectral imager was developed for laboratory scale and afterwards tested on industrial scale cultivations. In the laboratory experiments, reference data for microalgal biomass concentration was gathered to construct 1) a vegetation index-based linear regression model and 2) a one-dimensional convolutional neural network model to resolve microalgae biomass concentration from the spectral images. The two modelling approaches were compared. The mean absolute percentage error (MAPE) for the index-based model was 15–24%, with the standard deviation (SD) of 13-18 for the different species. MAPE for the convolutional neural network was 11–26% (SD = 10–22). Both models predicted the biomass well. The convolutional neural network could also classify the monocultures of green algae by species (accuracy of 97–99%). The index-based model was fast to construct and easy to interpret. The index-based monitoring was also tested in an industrial setup demonstrating a promising ability to retrieve microalgae-biomass-based signals in different cultivation systems.
ISSN:0921-8971
1573-5176
DOI:10.1007/s10811-024-03256-4