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.

微囊藻sp。是世界上研究最多的蓝细菌属之一。一旦在原水中被鉴定出来，建议在水处理厂频繁分析细胞密度和有毒代谢产物（微囊藻毒素）。但是，这两种分析程序都非常耗时且劳动强度大，从而使可能受到污染的最终水到达用户手中。最好通过在线设备来确定与毒素产生有关的易于测量的参数，这将减轻这一问题，并有助于自来水公司提高水的安全性并降低运营成本。但是，这些设备仍然具有精度限制，并且需要有效的数学模型来将光信号转换为蓝细菌密度或蓝藻毒素浓度。在这种情况下，铜绿微囊藻培养使用多元线性回归模型。尽管所有变量与总微囊藻毒素之间存在显着相关性（p <0.05），但仍建立了仅涉及藻蓝蛋白和脱镁叶绿素浓度的简化且精确的模型（调整后的R ² = 0.824），从而提供了一种简便而廉价的预测方法原水中微囊藻毒素的浓度。