Method for Unknown Vapor Characterization and Classification Using a Multivariate Sorption Detector. Initial Derivation and Modeling Based on Polymer-coated Acoustic Wave Sensor Arrays and Linear Solvation Energy Relationships.

October 15, 1999

Journal Article

Method for Unknown Vapor Characterization and Classification Using a Multivariate Sorption Detector. Initial Derivation and Modeling Based on Polymer-coated Acoustic Wave Sensor Arrays and Linear Solvation Energy Relationships.

Abstract

A novel method for the characterization and classification of unknown vapors based on the response on an array of polymer-coated acoustic wave vapor sensors is presented. Unlike exisiting classification algorithms, the method does not require that the system be trained on the samples to be identified. Instead, the solvation energy relationship (LSER) coefficients of the sorbent polymer coatings. The vapors can then be identified from a database of candidate vapor parameters. In principle, it is possible to estimate the concentration of an unkown vapor for which the system has not been trained or calibrated. This new method for characterizing and classifying unknown compounds based on the responses of a multivariate sorption detector is demonstrated with synthetic data.

Revised: November 16, 1999 | Published: October 15, 1999

Citation

Grate J.W., B.M. Wise, and M.H. Abraham. 1999. Method for Unknown Vapor Characterization and Classification Using a Multivariate Sorption Detector. Initial Derivation and Modeling Based on Polymer-coated Acoustic Wave Sensor Arrays and Linear Solvation Energy Relationships. Analytical Chemistry 71, no. 20:4544-4553. PNNL-SA-31026.