IBM Research believes the low-cost Hypertaste could serve a wide range of industrial and scientific users be it for on-the-fly water quality checks or enabling beverage producers to identify counterfeit products or check raw materials. The quick, in-situ identification and classification of liquids would also be relevant to the pharmaceutical and healthcare industries.
Here, one big advantage of having the machine learning models running on the cloud is that the sensors could be rapidly reconfigured from anywhere without changing the hardware. Only a few changes of parameters in the machine learning models would make them adjust to the new sensor set. Crowdsourcing sensing data through field-deployed connected sensors would further reinforce the learning.
As emphasized by the researchers, fooling a combinatorial sensing system such as Hypertaste is much harder than fooling individual analyte-specific lab tests, as there is no single substance on which the identification relies.