Calibration-free on-site detection of microcystin-LR using integrated biosensing, multi-parameter water quality...

Microcystin-lysine-arginine (MC-LR) is a potent hepatotoxic cyanotoxin produced during harmful algal blooms and is recognized as a global environmental concern due to its widespread occurrence in surface waters. Screen-printed carbon electrode (SPCE) biosensors offer a rapid and low-cost approach for MC-LR detection. However, their electrochemical responses are strongly influenced by water quality conditions (e.g., pH, electrical conductivity, total dissolved solids), often necessitating frequent, condition-specific recalibration. To address this limitation, this study developed an integrated framework that combines SPCE-based antibody biosensing with multi-parameter water quality monitoring and machine learning (ML) for rapid and on-site detection of MC-LR. Field samples collected across Florida yielded over 200 observations integrating biosensor impedance responses with concurrent measurements of pH, EC, turbidity, and UV₂₅₄. Among the evaluated ML models, Extreme Gradient Boosting (XGBoost) achieved the highest predictive performance (NSE = 0.89, RMSE = 13.21), enabling a unified model applicable across diverse water matrices. Shapley Additive Explanations (SHAP) analysis identified impedance (Z') as the dominant predictive feature, followed by EC, pH, UV₂₅₄, and turbidity, highlighting the influence of matrix-driven interfacial effects on biosensor response. Overall, this work establishes a robust, data-driven framework for characterizing biosensor-water matrix interactions and provides a practical strategy for faster and more accurate on-site measurement of MC-LR in complex environmental waters.