LOW-RESOLUTION RAMAN SPECTROSCOPY FOR REAL-TIME MONITORING OF HARMFUL CYANOBACTERIAL BLOOMS IN FRESHWATER AQUACULTURE SYSTEMS

Many cyanobacterial species growing in aquaculture ponds, and other freshwater reservoirs, proliferate under elevated temperatures, light and nutrient loading, leading to the formation of harmful cyanobacterial blooms (HCBs). HCBs may affect the quality of aquaculture produce through the accumulation of off-flavors, reducing their acceptance by consumers. HCBs may also produce a large number of toxins affecting the nerves (neurotoxins), liver (hepatotoxins), and skin (dermatoxins), collectively known as cyanotoxins. Among these, the most well-known and regulated are microcystins, often found in HCB contaminated drinking water and aquaculture ponds. Microcystins were also shown to accumulate in fish tissues at levels exceeding the recommended maximal daily intake of 0.04 μg/kg body weight of human consumer. In order to properly manage and control HCBs, it is necessary to develop a system allowing real-time monitoring and early warning of HCB development. This need is not met by current technologies, which are both time-consuming and expensive. The present study tests the potential of Low-Resolution Raman Spectroscopy (LRRS) technology for developing a tool for real-time detection of developing HCBs. Raman spectra from several cyanobacterial and algal species, including Microcystis aeruginosa, were obtained using the LRRS system. The resulting database was used to develop a predictive mathematical model based on Partial Least Square Regression and Discriminant Analyses of the spectral data collected from the LRRS system. Initial results obtained from the spectroscopy and statistical analyses demonstrate the ability of the LRRS to detect, quantify and discriminate between different algal and cyanobacterial species at cell concentrations of 10⁴ cells/ml or lower, suggesting a potential for future application in monitoring the development of HCBs in freshwater environments.