Gas recognition using a neural network approach to plasma optical emission spectroscopy

Mark Hyland; Davide Mariotti; Werner Dubitzky; James A. McLaughlin; Paul Maguire

doi:10.1117/12.403630

13 October 2000 Gas recognition using a neural network approach to plasma optical emission spectroscopy

Mark Hyland, Davide Mariotti, Werner Dubitzky, James A. McLaughlin, Paul Maguire

Proceedings Volume 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III; (2000) https://doi.org/10.1117/12.403630
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

A system has been developed which enables the detection and recognition of various gases. Plasma emission spectroscopy has been used to record spectra from volatile species of acetone, vinegar, and coffee beans, along with air and nitrogen spectra. The spectra have been uniquely processed and fed into an artificial neural network program for training and recognition of unknown gases. The system as a whole can be grouped into the emerging and diverse area of artificial nose technology. The sy stem has shown to provide a solution to the recognition of simple gases and odours (air, nitrogen, acetone) and could also satisfactorily recognise more complex samples (vinegar and coffee beans). Recognition is performed in seconds; this being a positive aspect for many artificial nose applications.

Citation Download Citation

Mark Hyland, Davide Mariotti, Werner Dubitzky, James A. McLaughlin, and Paul Maguire "Gas recognition using a neural network approach to plasma optical emission spectroscopy", Proc. SPIE 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III, (13 October 2000); https://doi.org/10.1117/12.403630

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