Applying hyperspectral transmittance for inter-genera classification of cyanobacterial and algal cultures

Olubunmi E. Adejimi, Giji Sadhasivam, Ze'ev Schmilovitch, Orr H. Shapiro*, Ittai Herrmann*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In the mass production systems of microalgal species, it is important to ensure the safety and quality of the biomass and product. This requires effective monitoring tools that are sensitive, rapid and simple to use. In this study, hyperspectral transmittance spectroscopy (HTS) was applied for the detection, cell density quantification and classification of algal and cyanobacterial species. A database of HTS data was assembled from samples of seven algal and cyanobacterial species at different cell densities and used for quantifying and classifying the species, using chemometric and machine learning algorithms. The results obtained showed the ability to quantify the species with a detection limit of 104 cells/mL for the support vector machine models applied, and classify the species at concentrations >105 cells/mL. The current study suggests that HTS is applicable for cell density quantification. HTS was used to distinguish between cell cultures of cyanobacteria and algae and was further able to distinguish between cyanobacteria species as well as algal species. In addition, reducing the dimensions (number of spectral bands) of HTS data using feature selection and aggregation improved the classification accuracy. Thus, HTS is recommended as an effective tool for monitoring and management of microalgal bioreactors.

Original languageAmerican English
Article number103067
JournalAlgal Research
Volume71
DOIs
StatePublished - Apr 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Biophotonics
  • Bioreactor
  • Chemometric analysis
  • Machine learning
  • Monitoring
  • Spirulina

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