Non-invasive monitoring of microalgae cultivations using hyperspectral imager
Pääkkönen, S., Pölönen, I., Raita-Hakola, A.-M., Carneiro, M., Cardoso, H., Mauricio, D., Rodrigues, A. M. C., & Salmi, P. (2024). Non-invasive monitoring of microalgae cultivations using hyperspectral imager. Journal of Applied Phycology, Early online. https://doi.org/10.1007/s10811-024-03256-4
Julkaistu sarjassa
Journal of Applied PhycologyTekijät
Päivämäärä
2024Tekijänoikeudet
© The Author(s) 2024
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 diferent 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.
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Julkaisija
Springer NatureISSN Hae Julkaisufoorumista
0921-8971Asiasanat
Julkaisuun liittyvä(t) tutkimusaineisto(t)
https://doi.org/10.23729/1e576402-1ccc-4974-a392-e014bd6cec38Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/213605226
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Elinkeinoelämän kanssa verkottunut tutkimus, BFLisätietoja rahoituksesta
Open Access funding provided by University of Jyväskylä (JYU). This research was funded by the European Union - NextGenerationEU via Business Finland, funding decision number 7134/31/2021Lisenssi
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