Since the introduction of gas sensor arrays in 1982, they have been tested in numerous fields but still suffer from weak sensitivity and selectivity in gas mixtures. To account for that, the focus lays on new evaluation algorithms (e.g. artificial neural networks with deep learning) following recent advances in computing capabilities. However, often the underlying hardware (i.e. sensors) is the limiting factor. As a result, the often applied "black box" approach correlating sensor signals with chemical perception (e.g., woody taste of wine) holds high risk of bogus correlations as the relevant analyte, responsible for the actual odor, aroma or disease, might not be generating the measured sensor outputs in the first place.
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