Non-invasive monitoring of microalgae cultivations using hyperspectral imager

• Published in: Journal of applied phycology Vol. 36; no. 4; pp. 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: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2024; Springer Nature B.V
• Subjects: Algae; Aquatic microorganisms; Aquatic plants; Artificial neural networks; biochemical compounds; Biomass; Biomedical and Life Sciences; Biomolecules; Control methods; Cultivation; Ecology; Economics; Freshwater & Marine Ecology; hyperspectral imagery; Hyperspectral imaging; Laboratory experimentation; Life Sciences; Microalgae; Monitoring; Monitoring methods; Monoculture; Neural networks; Phytoplankton; Plant Physiology; Plant Sciences; profitability; regression analysis; Regression models; Robust control; species; standard deviation; vegetation; Vegetation index; Model comparison; Green microalgae; Non-invasive monitoring; Vegetation indices; Convolutional neural network; Hyperspectral imaging
• ISSN: 0921-8971; 1573-5176
• DOI: 10.1007/s10811-024-03256-4

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.