TY - JOUR
T1 - Vapor recognition with small arrays of polymer-coated microsensors. A comprehensive analysis
AU - Park, Jeongim
AU - Groves, William A.
AU - Zellers, Edward T.
PY - 1999/9/1
Y1 - 1999/9/1
N2 - A comprehensive analysis of vapor recognition as a function of the number of sensors in a vapor-sensor array is presented. Responses to 16 organic vapors collected from six polymer-coated surface acoustic wave (SAW) sensors were used in Monte Carlo simulations coupled with pattern recognition analyses to derive statistical estimates of vapor recognition rates as a function of the number of sensors in the array (≤6), the polymer sensor coatings employed, and the number and concentration of vapors being analyzed. Results indicate that as few as two sensors can recognize individual vapors from a set of 16 possibilities with <6% average recognition error, as long as the vapor concentrations are > 5 x LOD for the array. At lower concentrations, a minimum of three sensors is required, but arrays of 3-6 sensors provide comparable results. Analyses also revealed that individual- vapor recognition hinges more on the similarity of the vapor response patterns than on the total number of possible vapors considered. Vapor mixtures were also analyzed for specific 2-, 3-, 4-, 5-, and 6-vapor subsets where all possible combinations of vapors within each subset were considered simultaneously. Excellent recognition rates were obtainable for mixtures of up to four vapors using the same number of sensors as vapors in the subset. Lower recognition rates were generally observed for mixtures that included structurally homologous vapors. Acceptable recognition rates could not be obtained for the 5- and 6-vapor subsets examined, due, apparently, to the large number of vapor combinations considered (i.e., 31 and 63, respectively). Importantly, increasing the number of sensors in the array did not improve performance significantly for any of the mixture analyses, suggesting that for SAW sensors and other sensors whose responses rely on equilibrium vapor-polymer partitioning, large arrays are not necessary for accurate vapor recognition and quantification.
AB - A comprehensive analysis of vapor recognition as a function of the number of sensors in a vapor-sensor array is presented. Responses to 16 organic vapors collected from six polymer-coated surface acoustic wave (SAW) sensors were used in Monte Carlo simulations coupled with pattern recognition analyses to derive statistical estimates of vapor recognition rates as a function of the number of sensors in the array (≤6), the polymer sensor coatings employed, and the number and concentration of vapors being analyzed. Results indicate that as few as two sensors can recognize individual vapors from a set of 16 possibilities with <6% average recognition error, as long as the vapor concentrations are > 5 x LOD for the array. At lower concentrations, a minimum of three sensors is required, but arrays of 3-6 sensors provide comparable results. Analyses also revealed that individual- vapor recognition hinges more on the similarity of the vapor response patterns than on the total number of possible vapors considered. Vapor mixtures were also analyzed for specific 2-, 3-, 4-, 5-, and 6-vapor subsets where all possible combinations of vapors within each subset were considered simultaneously. Excellent recognition rates were obtainable for mixtures of up to four vapors using the same number of sensors as vapors in the subset. Lower recognition rates were generally observed for mixtures that included structurally homologous vapors. Acceptable recognition rates could not be obtained for the 5- and 6-vapor subsets examined, due, apparently, to the large number of vapor combinations considered (i.e., 31 and 63, respectively). Importantly, increasing the number of sensors in the array did not improve performance significantly for any of the mixture analyses, suggesting that for SAW sensors and other sensors whose responses rely on equilibrium vapor-polymer partitioning, large arrays are not necessary for accurate vapor recognition and quantification.
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U2 - 10.1021/ac9902401
DO - 10.1021/ac9902401
M3 - Article
C2 - 10489533
AN - SCOPUS:0033198573
SN - 0003-2700
VL - 71
SP - 3877
EP - 3886
JO - Analytical Chemistry
JF - Analytical Chemistry
IS - 17
ER -