Spectroscopic multicomponent analysis using multi-objective optimization for variable selection
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Data
2014
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Resumo
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.
Descrição
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Multi-objective algorithms, Variable selection, Linear regression
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.