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The authors report the identification and quantification of the components of a ternary vapor mixture using a microcantilever-based electronic nose. An artificial neural network was used for pattern recognition. Dimethyl methyl phosphonate vapor in ppb concentrations and water and ethanol vapors in ppm concentrations were quantitatively identified either individually or in binary and ternary mixtures at varying concentrations.

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 recently been presented [L. A. Pinnaduwage et al., Appl. Phys. Lett. 91, 044105 (2007)]. Our microelectromechanical-system-based electronic nose is composed of a microcantilever sensor array with seven individual sensors used for vapor detection and an artificial neural network for 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 of generating different response patterns to mixtures with different component proportions. Therefore, once the electronic nose was trained by 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 at ppb concentrations and water and ethanol at 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. Author post-print ofZhao, W., Pinnaduwagel, L., Leis, J.W., Gehl, A.C., Allman, S.L., Shepp,A. and Mahmud, K.K. (2007) Quantitative analysis of ternary vapor mixtures using a microcantilever-based electronic nose. Applied Physics Letters, 91 (4).