The I/O transform of a chemical sensor

A number of sensing technologies, using a variety of transduction principles, have been proposed for non-invasive chemical sensing. A fundamental problem common to all these sensing technologies is determining what features of the transducer's signal constitute a chemical fingerprint that allows for precise analyte recognition. Of particular importance is the need to extract features that are robust with respect to the sensor's age or stimulus intensity. Here, using pulsed stimulus delivery, we show that a sensor's operation can be modeled as a linear input-output (I/O) transform. The I/O transform is unique for each analyte and can be used to precisely predict a temperature-programmed chemiresistor's response to the analyte given the recent stimulus history (i.e. state of an analyte delivery valve being open or closed). We show that the analyte specific I/O transforms are to a certain degree stimulus intensity invariant and can remain consistent even when the sensor has undergone considerable aging. Significantly, the I/O transforms for a given analyte are highly conserved across sensors of equal manufacture, thereby allowing training data obtained from one sensor to be used for recognition of the same set of chemical species with another sensor. Hence, this proposed approach facilitates decoupling of the signal processing algorithms from the chemical transducer, a key advance necessary for achieving long-term, non-invasive chemical sensing.