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A method for determining the optimal set of polymer sensor coatings to include in a surface acoustic wave (SAW) sensor array for the analysis of organic vapors is described. The method combines an extended disjoint principal components regression (EDPCR) pattern recognition analysis with Monte Carlo simulations of sensor responses to rank the various possible coating selections and to estimate the ability of the sensor array to identify any set of vapor analytes. A data base consisting of the calibrated responses of 10 polymer-coated SAW sensors to each of six organic solvent vapors from three chemical classes was generated to demonstrate the method. Responses to the individual vapors were linear over the concentration ranges examined, and coatings were stable over several months of operation. Responses to binary mixtures were additive functions of the individual component responses, even for vapors capable of strong hydrogen bonding. The EDPCR-Monte Carlo method was used to select the four-sensor array that provided the least error in identifying the six vapors, whether present individually or in binary mixtures. The predicted rate of vapor identification (87%) was experimentally verified, and the vapor concentrations were estimated within 10% of experimental values in most cases. The majority of errors in identification occurred when an individual vapor could not be differentiated from a mixture of the same vapor with a much lower concentration of a second component. The selection of optimal coating sets for several ternary vapor mixtures is also examined. Results demonstrate the capabilities of polymer-coated SAW sensor arrays for analyzing of solvent vapor mixtures and the advantages of the EDPCR-Monte Carlo method for predicting and optimizing performance.
Zellers et al. (Wed,) studied this question.