Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/74613
Title: HUMANN-based computational neural system for the determination of pollutants using fluorescence measurements
Authors: Suárez Araujo, C. P. 
Santana Rodríguez, J.J. 
García Báez, P. 
Betancort Rodriguez,Juana Rosa 
UNESCO Clasification: 2301 química analítica
120317 Informática
Keywords: Unsupervised Artificial Neural Network
HUMANN
Polychlorinated Byphenyls
Polychlorinated Dibenzofurans
Fluorescence Spectrometry
Issue Date: 2003
Conference: 8th International Conference of Engineering Applications of Neural Networks (EANN'03) 
Abstract: Polychlorinated Biphenyls (PCBs) and Polychlorinated Dibenzofurans (PCDFs) are chlorinated aromatic compounds and are emitted into the environment. It has been shown lo have toxicity and carcinogenic potential characteristics. Because of this their identification and quantification is a matter of great concern. However. the similar structure of PCBs and PCDFs can produce overlapping in fluorescence spectra, which add difficult to their determination. We present in this paper. an HUMANN-based computational neural systeni [1][2] for the identification of these compounds. HUMANN is a multilayer neural net with high biologicnl plausibility. Its adaptive character is essentially embodiment in the labelling module, because of its dynamic dimension The determination of the different analytes will be indicated by the firing neurons in the labelling layer and by the activation level of these neurons. In this work its have also been developed a model for spectral data. íiuorescence spectrurn of single compounds and complex niixture. via Gaussian distribution. Our final proposal consists in putting to work together fluorescence spectrometry and neural computation approach, and to analyse the good results and the troubles found in this new method using three type of spectra: excitation, emission and synchronous.
URI: http://hdl.handle.net/10553/74613
Source: Neural network engineering experiences: Proceedings of the Eighy International Conference o Engineering Applications of Neural Networks (EANN'03), Universidad de Malaga, 8-10 Septiembre, p. 290-297
Appears in Collections:Actas de congresos
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