Association of different prediction methods for determination of the efficiency and selectivity on neuron-based sensors

A technique has been developed to determine the efficiency and the selectivity of a single neuron-based sensor in identifying the nature of the chemical agents in an unknown sample. This has been achieved by exploiting the unique electrical identifiers, also known as “signature patterns”, generated by the neuronal cell membrane. These were generated based on the variations to the extracellular electrical activity, due to the effect of a broad range of chemical agents. We demonstrate the prediction capability of the sensor in identifying the nature of an unknown test sample from a combination of three chemical agents, namely, ethanol, pyrethroid, and hydrogen peroxide. This was achieved through a two-step process. The first step was experimentally achieved by in situ recording of the changes to the extracellular electrical activity from the sensing sites or the array of microelectrodes that form the platform for patterning neurons. Simultaneous optical characterization of the cell array during the sensing process was performed to identify the associated physiological changes. The second step was mathematical and was based on developing a library of signature patterns for a set of concentrations of the various combinations of the three chemical agents. Two variants of the nearest neighbor algorithm scheme - (a) partial distance search method, and (b) search tree method, were implemented for the accurate detection of all the components with varying concentrations in the test samples of unknown nature. This technique exhibits reliability in identification up to parts-per-billion (ppb) sensitivity. The capability of standardization of this technique for potential commercial applications is also discussed.