期刊
ANALYTICA CHIMICA ACTA
卷 699, 期 1, 页码 18-25出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2011.04.061
关键词
Ant colony optimization; Variable selection; Near infrared spectroscopy; Partial least-squares regression
资金
- University of Rosario
- CONICET [PIP 1950]
A new variable selection algorithm is described, based on ant colony optimization (ACO). The algorithm aim is to choose, from a large number of available spectral wavelengths, those relevant to the estimation of analyte concentrations or sample properties when spectroscopic analysis is combined with multivariate calibration techniques such as partial least-squares (PLS) regression. The new algorithm employs the concept of cooperative pheromone accumulation, which is typical of ACO selection methods, and optimizes PLS models using a pre-defined number of variables, employing a Monte Carlo approach to discard irrelevant sensors. The performance has been tested on a simulated system, where it shows a significant superiority over other commonly employed selection methods, such as genetic algorithms. Several near infrared spectroscopic experimental data sets have been subjected to the present ACO algorithm, with PLS leading to improved analytical figures of merit upon wavelength selection. The method could be helpful in other chemometric activities such as classification or quantitative structure-activity relationship (QSAR) problems. (C) 2011 Elsevier B.V. All rights reserved.
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