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Type: Artigo de evento
Title: Classification Of Antarctic Algae By Applying Kohonen Neural Network With 14 Elements Determined By Inductively Coupled Plasma Optical Emission Spectrometry
Author: Balbinot L.
Smichowski P.
Farias S.
Arruda M.A.Z.
Vodopivez C.
Poppi R.J.
Abstract: Optical emission spectrometers can generate results, which sometimes are not easy to interpret, mainly when the analyses involve classifications. To make simultaneous data interpretation possible, the Kohonen neural network is used to classify different Antarctic algae according to their taxonomic groups from the determination of 14 analytes. The Kohonen neural network architecture used was 5×5 neurons, thus reducing 14-dimension input data to two-dimensional space. The input data were 14 analytes (As, Co, Cu, Fe, Mn, Sr, Zn, Cd, Cr, Mo, Ni, Pb, Se, V) with their concentrations, determined by inductively coupled plasma optical emission spectrometry in 11 different species of algae. Three taxonomic groups (Rhodophyta, Phaeophyta and Cholorophyta) can be differentiated and classified through only their Cu content. © 2005 Elsevier B.V. All rights reserved.
Rights: fechado
Identifier DOI: 10.1016/j.sab.2005.03.005
Date Issue: 2005
Appears in Collections:Unicamp - Artigos e Outros Documentos

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