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*Corresponding author: [email protected]

A thorough water quality assessment requires sample testing for a set of parameters. In field measurements, it is not necessary to precisely determine water composition. Field measurements can be planned only to roughly understand the necessity of in-depth lab-based analysis. This can be done by developing methods that are reagent free, independent of high-end instrumentation, and facilitative of multiplexed detection. While using multiplexed detection techniques, it is necessary to identify hidden signals using a probabilistic/predictive approach. This can be achieved by analysing the data from the measurements using AI and ML-based prediction techniques. The spectroscopy or electrochemistry-based signals exhibit minor sample-to-sample variations, which can be processed with data analytics tools to predict multiple analytes in water. Due to advancements in the miniaturization of sensors and cloud computing-based data analysis technologies, it has become possible to realize such data analysis-based multi-analyte sensing and detection methods.

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