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

A novel method for the characterization and classification of unknown vapors based on the response on an away of polymer-coated acoustic wave vapor sensors is presented. Unlike existing classification algorithms, the method does not require that the system be trained on all samples to be identified. Instead, the solvation parameters of the unknown vapor are estimated given the sensor responses and the linear solvation energy relationship coefficients of the sorbent polymer coatings. The vapors can then be identified from a database of candidate vapor parameters. The method is implemented in a way that is analogous to multivariate calibration with classical least squares, where the individual vapor parameters are treated as pure compounds. It is not necessary to know the vapor concentration of the vapor to perform the classification. In principle, it is possible to estimate the concentration of an unknown vapor for which the system has not been trained or calibrated. It is also possible to implement the method using inverse least-squares models, based on training samples. This new method for characterizing and classifying unknown compounds based on the responses of a multivariate sorption detector is demonstrated with synthetic data.