Feature importance methods unveiling the cross-sensitive response of an integrated sensor array to quantify major cations in drinking water

Gianmarco Gabrieli, Michal Muszynski, Patrick W. Ruch

Abstract: 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> , Mg <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> , and Na <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , 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.