HyperTaste Lab—A Notebook with a Machine Learning Pipeline for Chemical Sensor Arrays

The cross-sensitivity of materials in low-selective sensor arrays, namely e-noses and e-tongues, results in a convoluted sensor array response, which renders traditional analytical methods for data processing ineffective. Machine learning approaches can help discover the latent information in such data, and various data processing methods, including unsupervised and supervised techniques, have been proposed to calibrate those devices. In this study, we demonstrate HyperTaste Lab—a notebook with a machine learning pipeline for potentiometric sensor arrays. The ability of the notebook to process raw data produced by model sensor arrays comprising cross-sensitive and/or ion-selective electrodes is demonstrated for the characterization of drinking water and consumer beverages. We describe the modular data processing and machine learning framework that can be applied by sensor researchers to accommodate different signal modalities and perform various downstream tasks, such as the verification of a product's originality, the estimation of ion concentrations, and the quantitative prediction of sensory descriptors.