Rapid Analyte Recognition in a Device Based on Optical Sensors and the Olfactory System

• Published in: Analytical chemistry (Washington) Vol. 68; no. 13; pp. 2191 - 2202
• Main Authors: White, Joel, Kauer, John S, Dickinson, Todd A, Walt, David R
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 01.07.1996
• Subjects: Analytical chemistry; Chemistry; Exact sciences and technology; General, instrumentation; Neural networks; Optics; Sensors; Vapor; Mimetic; Immobilization; Pattern recognition; Neural network; Chemical sensor; Oxazine dye; Array; Vertebrata; Response time; Olfaction; Qualitative analysis; Fluorescent compound; Gas detector; Quantitative analysis; Optical fiber
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac9511197

We report here the development of a new vapor sensing device that is designed as an array of optically based chemosensors providing input to a pattern recognition system incorporating artificial neural networks. Distributed sensors providing inputs to an integrative circuit is a principle derived from studies of the vertebrate olfactory system. In the present device, primary chemosensing input is provided by an array of fiber-optic sensors. The individual fiber sensors, which are broadly yet differentially responsive, were constructed by immobilizing molecules of the fluorescent indicator dye Nile Red in polymer matrices of varying polarity, hydrophobicity, pore size, elasticity, and swelling tendency, creating unique sensing regions that interact differently with vapor molecules. The fluorescent signals obtained from each fiber sensor in response to 2-s applications of different analyte vapors have unique temporal characteristics. Using signals from the fiber array as inputs, artificial neural networks were trained to identify both single analytes and binary mixtures, as well as relative concentrations. Networks trained with integrated response data from the array or with temporal data from a single fiber made numerous errors in analyte identification across concentrations. However, when trained with temporal information from the fiber array, networks using “name” or “characteristic” output codes performed well in identifying test analytes.