This introductory article describes the content of the October 2004 issue of MRS Bulletin focusing on novel materials and applications of electronic noses and tongues.The articles in this issue review the state of the art in materials, devices, and data processing algorithms used in electronic olfaction and taste systems. The most common gas- and liquid-phase analyte detection tools are presented and compared with traditional chemical analysis instrumentation such as gas chromatography/mass spectroscopy systems. Metal oxides, polymer/polymer composites, and dyes are covered in these articles as key sensing materials. Resistive, optical, electrochemical, and other types of electronic nose and tongue systems are reviewed, and their use in diverse applications, including environmental and food-quality monitoring and medical diagnostics, is discussed.

An electronic nose that uses an array of 32 polymer–carbon black composite sensors has been developed, trained, and tested. By selecting a variety of chemical functionalities in the polymers used to make sensors, it is possible to construct an array capable of identifying and quantifying a broad range of target compounds, such as alcohols and aromatics, and distinguishing isomers and enantiomers (mirror-image isomers). A model of the interaction between target molecules and the polymer–carbon black composite sensors is under development to aid in selecting the array members and to enable identification of compounds with responses not stored in the analysis library.

This article describes the use of an electronic nose (the Cyranose 320) to sense and identify three species of bacteria responsible for ear, nose, and throat (ENT) infections. Gathered data were a complex mixture of different chemical compounds. An innovative approach for classifying the bacteria data was investigated by using a combination of several clustering algorithms. The best results suggest that the three classes of bacteria examined can be predicted with up to 98% accuracy, allowing more precise diagnosis of ENT infection in patients. This type of bacteria data analysis and feature extraction is difficult, but it can be concluded that combined use of the analysis methods described here can solve the feature extraction problem with very complex data and enhance the performance of electronic noses.