INVESTIGADORES
GOICOECHEA Hector Casimiro
artículos
Título:
Wavelength selection for multivariate calibration using a genetic algorithm: a novel initialization strategy
Autor/es:
GOICOECHEA, HÉCTOR C; AC OLIVIERI,
Revista:
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
Editorial:
American Chemical Society
Referencias:
Año: 2002 vol. 42 p. 1146 - 1153
ISSN:
0095-2338
Resumen:
Genetic algorithms and other procedures mimicking natural processes are being increasingly used for variable
selection, to improve the predictive ability of partial least-squares multivariate calibration. Two issues are
critical for the success of genetic algorithms: initialization (setting the first candidates for solving the problem
at hand) and overfitting (the tendency to produce excellent results when training, but poor predictions toward
fresh samples). A new procedure is presented for sensor selection problems, involving iterative reinitialization
based on a statistical analysis of the included sensors. It is shown to give excellent results without the
requirement of preparing independent test data sets. Monte Carlo simulations using a theoretical threecomponent
example illustrate how partial least-squares regression greatly benefits from variable selection
when the analyte of interest is diluted, and how the new initialization method compares with other strategies.
The new genetic algorithm was applied to five experimental data sets. The target parameters were the
concentrations of diluted analytes in four pharmaceutical mixtures studied by UV-visible spectrophotometry
and the octane number in gasolines analyzed by near-infrared spectroscopy.-visible spectrophotometry
and the octane number in gasolines analyzed by near-infrared spectroscopy.