Recognition of volatile organic compounds using SnO2 sensor array and pattern recognition analysis

A sensor array with 10 sensors integrated on a substrate was developed to r ecognize various kinds and quantities of volatile organic compounds (VOCs), such as benzene, toluene, ethyl alcohol, methyl alcohol, and acetone. The sensor array consists of gas-sensing materials using SnO2 as the base mater ial, plus a heating element based on a meandered platinum layer, all deposi ted on the substrate. The sensors on the sensor array are designed to produ ce a uniform thermal distribution and show a high and broad sensitivity and reproductivity to low concentrations through the usage of nano-sized sensi ng materials with high surface areas and different additives. By utilizing the sensing signals of the array with an artificial neural network, a recog nition system can then be implemented for the classification and quantifica tion of VOCs. The characteristics of the multi-dimensional sensor signals o btained from 10 sensors are analyzed using the principal component analysis (PCA) technique, and a gas pattern recognizer is implemented using a multi -layer neural network with an error-back-propagation learning algorithm. Si mulation and experimental results demonstrated that the proposed gas recogn ition system is effective in identifying VOCs. For real-time processing, a DSP board can be used to implement the proposed VOC recognition system in c onjunction with a neural network. (C) 2001 Elsevier Science B.V. All rights reserved.