Quantitative analysis of ternary vapor mixtures using a microcantilever-based electronic nose

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.