A Method for Chemometric Classification of Unknown Vapors from the Responses of an Array of Volume-Transducing Sensors

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Abstract
A method for the characterization and classification of unknown vapors based on the responses on an array of polymer-based volume-transducing vapor sensors is presented. Unlike conventional pattern recognition methods, the sensor array pattern vector is converted into another vector containing vapor descriptors. Equations are developed to show how this approach can be applied to arrays of sensors where each sensor responds to the fractional volume increase of the polymer upon vapor sorption. The vapor sorption step of the response is modeled with linear solvation energy relationships using solvation parameters as vapor descriptors. The response model also includes the vapor concentration, the sensitivity to fractional volume increases, and the specific volume of the vapor as a liquid. The response model can be solved for the vapor descriptors given the array responses and sensitivity factors, following an approach described previously for purely gravimetric sensors. The vapors can then be classified from a database of candidate vapor descriptors. Chemiresistor vapor sensors coated with composite polymer films containing conducting particles represent a volume-transducing sensor technology to which this new classification method should apply. Preliminary equations are also presented for sensors that respond on the basis of both the mass and the volume of a sorbed vapor. Surface acoustic wave sensors with acoustically thin polymer films that respond to both mass and modulus effects may fit this classification approach.

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