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| - Method for Unknown Vapor Characterization andClassification Using a Multivariate SorptionDetector. Initial Derivation and Modeling Based onPolymer-Coated Acoustic Wave Sensor Arrays andLinear Solvation Energy Relationships
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| - A novel method for the characterization and classificationof unknown vapors based on the response on an array ofpolymer-coated acoustic wave vapor sensors is presented.Unlike existing classification algorithms, the method doesnot require that the system be trained on all samples tobe identified. Instead, the solvation parameters of theunknown vapor are estimated given the sensor responsesand the linear solvation energy relationship coefficientsof the sorbent polymer coatings. The vapors can then beidentified from a database of candidate vapor parameters.The method is implemented in a way that is analogous tomultivariate calibration with classical least squares, wherethe individual vapor parameters are treated as purecompounds. It is not necessary to know the vapor concentration of the vapor to perform the classification. Inprinciple, it is possible to estimate the concentration ofan unknown vapor for which the system has not beentrained or calibrated. It is also possible to implement themethod using inverse least-squares models, based ontraining samples. This new method for characterizing andclassifying unknown compounds based on the responsesof a multivariate sorption detector is demonstrated withsynthetic data.
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