Use este identificador para citar ou linkar para este item: http://repositorio.bc.ufg.br/handle/ri/14867
Tipo do documento: Artigo
Título: Spectroscopic multicomponent analysis using multi-objective optimization for variable selection
Autor: Soares, Anderson da Silva
Lima, Telma Woerle de
Lucena, Daniel Vitor de
Salvini, Rogerio Lopes
Laureano, Gustavo Teodoro
Coelho, Clarimar José
Abstract: The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicochemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II (strength Pareto evolutionary algorithm II). We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR (near-infrared spectroscopy) spectrometry where the objective is the determination of a variable subgroup with information about E protein content (%), test weight (Kg/Hl), WKT (wheat kernel texture) (%) and farinograph water absorption (%). The results of traditional techniques of multivariate calibration as the SPA (successive projections algorithm), PLS (partial least square) and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.
Palavras-chave: Multi-objective algorithms
Variable selection
Linear regression
País: Estados unidos
Unidade acadêmica: Instituto de Informática - INF (RG)
Citação: SOARES, Anderson da Silva; LIMA, Telma Woerle de; LUCENA, Daniel Vitor de; SALVINI, Rogerio Lopes; LAUREANO, Gustavo Teodoro; COELHO, Clarimar José. Spectroscopic multicomponent analysis using multi-objective optimization for variable selection. Computer Technology and Application, New York, v. 4, n. 9, p. 465-474, 2013.
Tipo de acesso: Acesso Aberto
Identificador do documento: 10.17265/1934-7332/2013.09.004
Identificador do documento: 10.17265/1934-7332/2013.09.004
URI: http://repositorio.bc.ufg.br/handle/ri/14867
Data de publicação: 2014
Aparece nas coleções:INF - Artigos publicados em periódicos

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