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Potentiometric electronic tongues (ETs) leveraging trends in miniaturization and internet of things (IoT) bear promise for facile mobile chemical analysis of complex multi-component liquids, such as beverages. In this work, hand-crafted feature extraction from the transient potentiometric response of an array of low-selective miniaturized polymeric sensors is combined with a data pipeline for deployment of trained machine learning models on a cloud back-end or edge device. The sensor array demonstrated sensitivity to different organic acids and exhibited interesting performance for the fingerprinting of fruit juices and wines, including differentiation of samples through supervised learning based on sensory descriptors and prediction of consumer acceptability of aged juice samples. Product au-thentication, quality control and support of sensory evaluation are some of the applications that are expected to benefit from integrated electronic tongues that facilitate the characterization of complex properties of multi-component liquids.

A reconfigurable integrated electronic tongue and its use in accelerated analysis of juices and wines