  Electronic tongues based on potentiometry offer the prospect of rapid and
continuous chemical fingerprinting for portable and remote systems. The present
contribution presents a technology platform including a miniaturized electronic
tongue based on electropolymerized ion-sensitive films, microcontroller-based
data acquisition, a smartphone interface and cloud computing back-end for data
storage and deployment of machine learning models. The sensor array records a
series of differential voltages without use of a true reference electrode and
the resulting time-series potentiometry data is used to train supervised
machine learning algorithms. For trained systems, inferencing tasks such as the
classification of liquids are realized within less than 1 minute including data
acquisition at the edge and inference using the cloud-deployed machine learning
model. Preliminary demonstration of the complete electronic tongue technology
stack is reported for the classification of beverages and mineral water.
