Modeling total microcystin production by Microcystis aeruginosa using multiple regression

Microcystis sp. is one of the most studied genus of cyanobacteria worldwide. Once it has been identified in raw water, frequent analyses of cell density and toxic metabolites (microcystins) are recommended at the water treatment plants. However, both analytical procedures are highly time-consuming and labor-intensive, allowing the potentially contaminated finished water to reach customers. The identification of easily measurable parameters related to toxin production, preferably by on-line equipment, would mitigate this issue and help water companies to improve water safety and decrease operating costs. However, these devices still have precision limitations and need efficient mathematical models for converting light signals into cyanobacteria densities or cyanotoxin concentrations. In this scenario, this research aimed to develop a mathematical correlation between microcystin production and cell age and density, chlorophyll-a, pheophytin and phycocyanin in a Microcystis aeruginosa culture using a multiple linear regression model. Despite the significant correlation (p < 0.05) found between all the variables and total microcystin, a simplified and precise model (Adjusted R² = 0.824) involving only phycocyanin and pheophytin concentrations was developed in order to provide an initial attempt to easily and cheaply predict microcystin concentration in raw water.