Limits of Recognition for Simple Vapor Mixtures Determined with a Microsensor Array

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Abstract
The “limit of recognition” (LOR) has been defined as the minimum concentration at which reliable individual vapor recognition can be achieved with a multisensor array, and methodology for determining the LORs of individual vapors probabilistically on the basis of sensor array response patterns has been reported. This article explores the problems of defining and evaluating LORs for vapor mixtures in terms of the absolute and relative component vapor concentrations, where the mixture must be discriminated from those component vapors and from the subset of possible lower-order component mixtures. Monte Carlo simulations and principal components regression analyses of an extant database of calibrated responses to a set of 16 vapors from an array of 6 diverse polymer-coated surface acoustic wave sensors are used to illustrate the approach and to examine trends in LOR values among the 120 possible binary mixtures and 560 possible ternary mixtures in the data set. At concentrations exceeding the LOD, 89% of the binary mixtures could be reliably recognized (<5% error) over some composite concentration range, while only 3% of the ternary mixtures could be recognized. Most binary mixtures could be recognized only if the constituent vapor relative concentration ratio, defined in terms of multiples of the LOD for each vapor, was ≤20. Correlations with the Euclidean distance(s) separating the normalized constituent vapor response vectors allow reasonably accurate predictions of the limiting recognizable mixture composition ranges for binary and ternary cases. Results are considered in the context of using microsensor arrays for vapor detection and recognition in microanalytical systems.