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

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, Ca2+, Mg2+, 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.