Detection of multiple per- and polyfluoroalkyl substances (PFAS) using a biological brain-based gas sensor

Per- and polyfluoroalkyl substances (PFAS) are man-made compounds that bioaccumulate in environments. Current PFAS detection technologies encounter difficulty in detecting trace concentrations and require complex data processing, limiting their on-site applicability. By leveraging biological chemical sensing systems (insect olfaction) we can detect broad ranges of PFAS. Advanced combinatorial coding mechanism of insects at the level of olfactory sensory neurons enables highly sensitive and specific odor detection. Here, we harness the locust olfactory system to differentiate several PFAS. In-vivo extracellular neural recordings displayed unique odor-evoked responses for multiple PFAS at environmental concentrations. Using population neuronal response, we classified multiple PFAS with an average 87% accuracy. Machine learning algorithms incorporated separate training and testing datasets, reaching a 61% accuracy. Overall, our study demonstrates the first biological olfaction based broad PFAS detection system.