AI makes sense of taste to classify liquids

July 29, 2019 //By Julien Happich
Scientists at IBM Research have trained machine learning algorithms with potentiometry data from an array of multi-analyte electrochemical sensors being dipped into various liquids.

The idea being that as for the human sense of taste, a sensing device could rely on the ability of few individual sensors to respond simultaneously to different chemicals (combinatorial sensing) to get a holistic sensing pattern or a global fingerprint of the liquid being tested (or tasted).

Dubbed Hypertaste, the small lime-slice shaped AI-assisted e-tongue packs an array of electropolymerized ion-sensitive films with a microcontroller for data acquisition. When the Hypertaste device is clipped on the side of a glass, the sensor’s electrodes dip into the liquid to be tasted and a series of differential voltages can be recorded, collectively yielding a unique signature – the liquid’s fingerprint - to be analyzed and classified on the cloud by a trained AI algorithm. That digital fingerprint can then be compared to a database of known liquids, and the algorithm figures out which liquids in the database are most chemically similar to the liquid being tasted.

The new approach was presented at the 2019 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) in a paper titled “A portable potentiometric electronic tongue leveraging smartphone and cloud platforms”. The researchers report that for trained systems, inferencing tasks such as the classification of liquids are realized within less than one minute including data acquisition at the edge and inference using a cloud-deployed machine learning model.

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