Low-Resolution Raman Spectroscopy for the detection of contaminant species in algal bioreactors

Olubunmi E. Adejimi, Timea Ignat, Giji Sadhasivam, Varda Zakin, Ze'ev Schmilovitch, Orr H. Shapiro

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Fouling of aquatic systems by harmful microalgal and cyanobacterial species is an environmental and public health concern. Microalgal bioreactors are engineered ecosystems for the cultivation of algal biomass to meet the increasing demand for alternative protein sources and algae-derived products. Such bioreactors are often open or semi-open ponds or raceways that are prone to contamination by contaminant photosynthetic microorganisms, including harmful cyanobacterial species (HCBs). HCBs affect the quality of products through the accumulation of off-flavours, reducing their acceptance by consumers, and through the production of several different toxins collectively known as cyanotoxins. The density of cultured species within the bioreactor environment creates difficulty in detecting low concentrations of contaminant cells, and there is currently no technology enabling rapid monitoring of contaminations. The present study demonstrates the potential of Low-Resolution Raman Spectroscopy (LRRS) as a tool for rapid detection of low concentrations of HCBs within dense populations of the spirulina (Arthrospira platensis) cultures. An LRRS system adapted for the direct measurement of raw biomass samples was used to assemble a database of Raman spectral signatures, from eight algal and cyanobacterial strains. This dataset was used to develop both quantitative and discriminative chemometric models. The results obtained from the chemometric analyses demonstrate the ability of the LRRS to detect and quantify algal and cyanobacterial species at concentrations as low as 103 cells/mL and to robustly discriminate between species at concentrations of 104 cells/mL. The LRRS and chemometric analyses were further able to detect the presence of low concentrations (103cells/mL) of contaminating species, including the toxic cyanobacterium Microcystis aeruginosa, within dense (>107 cells/mL) spirulina cultures. The results presented provide a first demonstration of the potential of LRRS technology for real-time detection of contaminant species within microalgal bioreactors, and possibly for early detection of developing harmful algal blooms in other aquatic ecosystems.

Original languageEnglish
Article number151138
JournalScience of the Total Environment
StatePublished - 25 Feb 2022
Externally publishedYes


  • Algal bioreactors
  • Food safety
  • Harmful cyanobacterial blooms
  • Machine learning
  • Microcystis
  • Raman spectroscopy
  • Spirulina

ASJC Scopus subject areas

  • Pollution
  • Waste Management and Disposal
  • Environmental Engineering
  • Environmental Chemistry


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