The direct quantification of multiple ions in aqueous mixtures is achieved by combining an automated machine learning pipeline with transient potentiometric data obtained from a single miniaturized array of polymeric sensors electrodeposited on a conventional printed circuit board (PCB) substrate. A proof-ofconcept system was demonstrated by employing 16 polymeric sensors in combination with features extracted from the transient differential voltages produced by these sensors when transitioning from a reference solution to a test solution, thereby obviating the need for a conventional reference electrode. A tree-based regression model enabled concentrations of various metal cations in pure solutions to be determined in less than 2 min. In a model mixture comprising Al 3+ , Cu 2+ , Na + , and Fe 3+ , the mean relative error was found to depend on the type of ion and varied between 1% for Fe 3+ and 44% for Na + in the concentration range 1−10 mg/L. Overall, a mean relative error of 16% was obtained for quantification of these four ions across a total of 124 tests in different solutions spanning concentrations between 2 and 360 mg/L. These results demonstrate how the analytical capability of a multiselective sensor array can leverage data-driven approaches through training by examples for accelerated testing and can be proposed to complement traditional analytical tools to meet industrial demands, including traceability of chemicals.

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A proof-of-concept system comprising a miniaturized sensor array, feature extraction and machine learning pipeline was evaluated for the direct quantification of the concentrations of three major cations, Ca 2+ , Mg 2+ , and Na + , in drinking water. Feature importance methods were applied to discover dependencies between the transient potentiometric responses of sensing materials and the cation concentrations. The proposed framework supports design of cross-sensitive sensor arrays to accelerate water testing, providing a complementary approach to traditional chemical analysis for monitoring water quality.

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