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

Abstract

We 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. We detected parts-per-billion concentrations of dimethyl methyl phosphonate vapor in ternary mixtures with parts-per-million concentrations of water and ethanol. To our knowledge, this is the first successful quantitative analysis of vapor mixtures with more than two components with an electronic nose.