Identifying chemical odors rapidly and accurately is critical in a variety of fields. Due to the limited human sense of smell, much effort has been dedicated to the development of electronic sensing devices. Despite some recent progress, such devices are still no match for the capabilities of biological (animal) olfactory sensors, which are light, robust, versatile, and sensitive. Consequently, scientists are turning to a new approach: Bio-Hybrid sensors. These sensors combine animal biological sensors with electronic components to achieve maximum detection and classification while conveying a comprehensible signal to the end user. In this work, we created a bio-hybrid odor discriminator utilizing the desert locust's primary olfactory apparatus - its antennae, together with simple electroantennogram technology and artificial intelligence tools for signal analysis. Our discriminator is able to differentiate between at least eight pure odors and two mixtures of different odorants, independently of odorant concentration. With four orders of magnitude higher sensitivity than gas chromatography-mass spectrometry, it is able to detect the presence of less than 1 ng of volatile compounds and, compared to other bio-hybrid sensors available today, it can be easily operated by an unskilled individual. This study thus opens up the future for robust and simple bio-hybrid robotic sensing devices that can be widely deployed.
Keywords: Bio-hybrid systems; Biosensors; Electroantennogram; Insects olfaction; Machine-learning; Odor-discrimination.
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