A study of vapor recognition and quantification by polymer-coated multitransducer (MT) arrays is described. The primary data set consists of experimentally derived sensitivities for 11 organic vapors obtained from 15 microsensors comprising five cantilever, capacitor, and calorimeter devices coated with five different sorptive-polymer films. These are used in Monte Carlo simulations coupled with principal component regression models to assess expected performance. Recognition rates for individual vapors and for vapor mixtures of up to four components are estimated for single-transducer (ST) arrays of up to five sensors and MT arrays of up to 15 sensors. Recognition rates are not significantly improved by including more than five sensors in an MT array for any specific analysis, regardless of difficulty. Optimal MT arrays consistently outperform optimal ST arrays of similar size, and with judiciously selected 5-sensor MT arrays, one-third of all possible ternary vapor mixtures are reliably discriminated from their individual components and binary component mixtures, whereas none are reliably determined with any of the ST arrays. Quaternary mixtures could not be analyzed effectively with any of the arrays. A "universal" MT array consisting of eight sensors is defined, which provides the best possible performance for all analytical scenarios. Accurate quantification is predicted for correctly identified vapors.
The discrimination of simple vapor mixtures from their components with polymer-coated multitransducer (MT) arrays as a function of the absolute and relative concentrations of those components is explored. The data set consists of calibrated responses to 11 organic vapors from arrays of 5 or 8 microsensors culled from a group of 5 cantilever, 5 capacitor, and 5 calorimeter transducers coated with 1 of 5 different sorptive-polymer films. Monte Carlo methods are applied to simulate error-enhanced composite responses to all possible binary and ternary mixtures of the 11 vapors, and principal component regression models are established for estimating expected rates of recognition as a function of mixture composition. The limit of recognition (LOR), defined as the maximum recognizable mixture composition range, is used as the metric of performance. With the optimal 8-sensor MT array, 19 binary and 3 ternary mixtures could be identified (i.e., discriminated from their components) with <5% error. The binary-mixture LORs are shown to decrease with increases in the baseline noise levels and random sensitivity variations of the sensors, as well as the similarity of the vapors. Importantly, most of the binary LOR contours are significantly asymmetric with respect to composition, and none of the mixtures could be recognized with <5% error at component relative concentration ratios exceeding 20:1. Discrimination of ternary mixtures from their components and binary subcomponent mixtures is possible only if the relative concentration ratio between any two of the components is <5:1. In comparing binary LORs for the best five-sensor single-transducer (ST) array to those of the best five-sensor MT array, the latter were larger in nearly all cases. The implications of these results are considered in the context of using such arrays as detectors in microanalytical systems with upstream chromatographic modules.
An accurate SO2 prediction method for using broadband continuous-wave diffuse reflectance near infrared (NIR) spectroscopy is proposed. The method fitted the NIR spectra to a Taylor expansion attenuation model, and used the simulated annealing method to initialize the nonlinear least squares fit. This paper investigated the effect of potential spectral interferences that are likely to be encountered in clinical use, on SO2 prediction accuracy. The factors include the concentration of hemoglobin in blood, the volume of blood and volume of water in the tissue under the sensor, reduced scattering coefficient, µs', of the muscle, fat thickness and the source-detector spacing. The SO2 prediction method was evaluated on simulated muscle spectra as well as on dual-dye phantoms which simulate the absorbance of oxygenated and deoxygenated hemoglobin.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.