EXTENDED DISJOINT PRINCIPAL-COMPONENTS REGRESSION-ANALYSIS OF SAW VAPOR SENSOR-ARRAY RESPONSES

The application of a disjoint principal-components regression method t o the analysis of sensor-array response patterns is demonstrated using published data from ten polymer-coated surface-acoustic-wave (SAW) se nsors exposed to each of nine vapors. Use of the method for the identi fication and quantitation of the components of vapor mixtures is shown by simulating the 36 possible binary mixtures and 84 possible ternary mixtures under the assumption of additive responses. Retaining inform ation on vapor concentrations in the classification models allows vapo rs to be accurately identified, while facilitating prediction of the c oncentrations of individual vapors and the vapors comprising the mixtu res. The effects on the rates of correct classification of placing con straints on the maximum and minimum vapor concentrations and superimpo sing error on the sensor responses are investigated.