Identification and quantification of components in ternary vapor mixtures using a microelectromechanical-system-based electronic nose

Abstract

We report the experimental details on the successful application of the electronic nose approach to identify and quantify components in ternary vapor mixtures. Preliminary results have been presented recently (L. A. Pinnaduwage et al., Appl. Phys. Lett. 91, 044105 (2007)). Our MEMS-based electronic nose is composed of a microcantilever sensor array with seven individual sensors used for vapor detection and an artificial neural network (ANN) for the pattern recognition. A set of custom vapor generators generated reproducible vapor mixtures in different compositions for training and testing of the neural network. The sensor array was selected to be capable to generating different response patterns to mixtures with different component proportions. Therefore, once the electronic nose was trained using the response patterns to various compositions of the mixture, it was able to predict the composition of “unknown” mixtures. We have studied two vapor systems: one included the nerve gas simulant dimethylmethyl phosphonate (DMMP) at parts-per-billion (ppb) concentrations and water and ethanol at parts-per-million (ppm) concentrations; the other system included acetone, water and ethanol all of which were at ppm concentrations. In both systems, individual, binary and ternary mixtures were analyzed with good reproducibility.