This paper (purchase required) describes the development of an array of 14 sensors based on colourimetric reactants immobilised on a paper or plastic support. The advantage of this approach, rather than traditional “e-tongue” systems based on reactions in liquid solution, is that it enabled the development of a “dipstick” test that could be taken into the field rather than having to send test samples to a laboratory. The authors report that they successfully used Machine Learning to train the system to discriminate between different botanical and local geographic origins of Iranian honey and also to discriminate when honeybees within these specific classifications had been illicitly fed with sugar syrups. It shows the potential of a cheap field-based test which would be trained and used for verification testing at the beginning of honey supply chains where very specific "authentic" classification references are available, rather than relying on testing further up the chain when chemometric classification becomes much more diffuse and difficult.
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