Summary
The monitoring of pollution in the environment is of increasing importance. Atmospheric pollutants can generally be detected using Fourier Transform Infrared Spectroscopy (FTIR), but automatic identification of these pollutants from their spectrum is not trivial. It is often desirable to not only identifi pollutants but also to estimate their concentration, so that it is possible to determine whether the level in the environment exceeds safe working limits. This chapter describes the use of Neural Networks, combined with Genetic Algorithm optimization, to develop software tools that are capable of identifying atmospheric pollutants, determining whether their concentration exceeds a defined threshold, and, if required, determining that concentration.
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Cartwright, H.M., Porter, A. (2003). Real-time Monitoring of Environmental Pollutants in the Workplace Using Neural Networks and FTIR Spectroscopy. In: Cartwright, H.M., Sztandera, L.M. (eds) Soft Computing Approaches in Chemistry. Studies in Fuzziness and Soft Computing, vol 120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36213-5_8
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DOI: https://doi.org/10.1007/978-3-540-36213-5_8
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