Microbial electrochemical sensors have been used to monitor water quality, with electroactive biofilms (EABs) serving as a core sensing element. However, since the bioelectric signals are incapable of recognizing different toxicants, the application of biosensors in complicated contamination is limited. With machine learning (ML) as a novel method to analyze bioelectric signals, we first quantified multiple toxicants with microbial electrochemical sensors. In this study, a batch of biosensors were shocked by mixed toxicants (MnCl 2 , NaNO 2 , and tetracycline hydrochloride (TCH)) at random concentrations. Regression ML models using different algorithms and datasets were developed and evaluated for prediction accuracy. The most accurate models for MnCl 2 , NaNO 2 , and TCH were trained with the algorithms of support vector machine, neural networks, and a generalized linear model. And the training set consisting of drop ratios at all time points showed the best accuracy for MnCl 2 and NaNO 2 , while the most accurate model for TCH was trained with the drop ratio at 6 h. Here, we demonstrated that by integrating machine learning, a microbial electrochemical sensor is able to quantify multiple toxicants simultaneously, providing a fundamental of multiple-parameter biotoxicity detection for environmental monitoring.
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