Multiway analysis of preconcentrator-sampled surface acoustic wave chemical sensor array data

New data processing methods for preconcentrator-sampled surface acoustic wave (SAW) sensor arrays are described. The preconcentrator-sampling procedure is used to collect and concentrate analyte vapors on a porous solid sorbent. Subsequent thermal desorption provides a crude chromatographic separation of the collected vapors prior to exposure to the SAW array. This article describes experiments to test the effects of incorporating retention information into the pattern-recognition procedures and to explore the feasibility of multiway classification methods. Linear discriminant analysis (LDA) and nearest-neighbor (NN) pattern-recognition models are built to discriminate between SAW sensor array data for four toxic organophosphorus chemical agent vapors and one agent simulant collected under a wide variety of conditions. Classification results are obtained for three types of patterns: (a) first-order patterns; (b) first-order patterns augmented with the time of the largest peak; and (c) second-order patterns with the use of the SAW frequency for each sensor over a broad time window. Classification models for the second-order patterns are also developed with the use of unfolded and multiway partial least-squares discriminants (uPLSD and mPLSD) and NN and LDA of the scores from unfolded and multiway principal-component analysis (uPCA and mPCA). It is determined that classification performance improves when information about the desorption time is included. Treating the preconcentrator-sampled SAW sensor array as a second-order analytical instrument and using a classification model based upon either uPLSD, uPCA-LDA, or NN results in the correct identification of 100% of the patterns in the prediction set. With the second-order patterns, the other pattern-recognition algorithms only do slightly worse.