Micro-algae are well-established food sources, used as food-supplements, protein-rich supplements of food and drink products, or as an environmentally-friendly animal feed. Beyond their use as food or feed, micro-algae are likely to become a key factor in increasing the sustainability of developing cultivated meat industries. However, there is an inherent conflict between economic and quality/safety considerations in algal cultivation systems. While quality and safety issues can best be regulated by cultivation in closed, strictly regulated systems, cost-effective production typically relies on cultivation in open or semi-open ponds or raceways. These systems are prone to contamination by competing species such as the toxigenic cyanobacterial species Mycrocystis aeruginosa. Detection of contamination events relies primarily on microscopic examination, with an effective detection limit of ~106 contaminating cells/ml. As a result, many commercial micro-algal products contain contaminating photosynthetic micro-organisms, some of which may be hazardous to consumers. Here we test the ability of a Low Resolution Raman Spectroscopy system (LRRS), combined with machine-learning algorithms, to quantify microalgal cultures and differentiate between several algal and cyanobacterial species. We further test the ability of this system to detect and quantify contaminating species on the background of a dense culture of spirulina (Arthrospira platensis). Our findings indicate a potential for similar systems for real-time monitoring of micro-algal bioreactors, allowing early detection of contamination events to improve product quality and safety and minimize losses due to process downtime and loss of product.
Dr. Orr Shapiro
Raman Spectroscopy, in Combination with Machine Learning Algorithms, for Monitoring the Quality and Safety of Algal Biomass
Volcani Institute, Agricultural Research Organization, Israel