OPTIMAL COATING SELECTION FOR THE ANALYSIS OF ORGANIC VAPOR MIXTURES WITH POLYMER-COATED SURFACE-ACOUSTIC-WAVE SENSOR ARRAYS

A method for determining the optimal set of polymer sensor coatings to include in a surface acoustic wave (SAW) sensor array for the analysi s of organic vapors is described, The method combines an extended disj oint principal components regression (EDPCR) pattern recognition analy sis with Monte Carlo simulations of sensor responses to rank the vario us possible coating selections and to estimate the ability of the sens or 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 generate d to demonstrate the method. Responses to the individual vapors were l inear over the concentration ranges examined, and coatings were stable over several months of operation. Responses to binary mixtures were a dditive functions of the individual component responses, even for vapo rs capable of strong hydrogen bonding. The EDPCR-Monte Carlo method wa s used to select the four-sensor array that provided the least error i n identifying the six vapors, whether present individually or in binar y mixtures. The predicted rate of vapor identification (87%) was exper imentally verified, and the vapor concentrations were estimated within 10% of experimental values in most cases. The majority of errors in i dentification occurred when an individual vapor could not be different iated from a mixture of the same vapor with a much lower concentration of a second component. The selection of optimal coating sets for seve ral ternary vapor mixtures is also examined. Results demonstrate the c apabilities of polymer-coated SAW sensor arrays for analyzing of soh e nt vapor mixtures and the advantages of the EDPCR-Monte Carlo method f or predicting and optimizing performance.