Journal of Automated Methods and Management in Chemistry
Volume 2009 (2009), Article ID 291820, 6 pages
doi:10.1155/2009/291820
Research Article
An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration
1College of Life Sciences, China Jiliang University, Hangzhou 310018, China
2State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
Received 1 March 2009; Accepted 2 April 2009
Academic Editor: Peter Stockwell
Copyright © 2009 Xiao-Ping Yu et al. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Linked References
- H. Martens and T. Næs, Multivariate Calibration, John Wiley & Sons, Chichester, UK, 1989.
- C. W. Brown, P. F. Lynch, R. J. Obremski, and D. S. Lavery, “Matrix representations and criteria for selecting analytical wavelengths for multicomponent spectroscopic analysis,” Analytical Chemistry, vol. 54, no. 9, pp. 1472–1479, 1982. View at Publisher · View at Google Scholar
- S. D. Frans and J. M. Harris, “Selection of analytical wavelengths for multicomponent spectrophotometric determinations,” Analytical Chemistry, vol. 57, no. 13, pp. 2680–2684, 1985. View at Publisher · View at Google Scholar
- J. H. Kalivas, N. Roberts, and J. M. Sutter, “Global optimization by simulated annealing with wavelength selection for ultraviolet-visible spectrophotometry,” Analytical Chemistry, vol. 61, no. 18, pp. 2024–2030, 1989. View at Publisher · View at Google Scholar
- D. Jouan-Rimbaud, B. Walczak, D. L. Massart, I. R. Last, and K. A. Prebble, “Comparison of multivariate methods based on latent vectors and methods based on wavelength selection for the analysis of near-infrared spectroscopic data,” Analytica Chimica Acta, vol. 304, no. 3, pp. 285–295, 1995. View at Publisher · View at Google Scholar
- C. H. Spiegelman, M. J. McShane, M. J. Goetz, M. Motamedi, Q. L. Yue, and G. L. Coté, “Theoretical justification of wavelength selection in PLS calibration: development of a new algorithm,” Analytical Chemistry, vol. 70, no. 1, pp. 35–44, 1998. View at Publisher · View at Google Scholar
- B. Nadler and R. R. Coifman, “The prediction error in CLS and PLS: the importance of feature selection prior to multivariate calibration,” Journal of Chemometrics, vol. 19, no. 2, pp. 107–118, 2005. View at Publisher · View at Google Scholar
- A. Höskuldsson, “Variable and subset selection in PLS regression,” Chemometrics and Intelligent Laboratory Systems, vol. 55, no. 1-2, pp. 23–38, 2001. View at Publisher · View at Google Scholar
- L. Xu and W.-J. Zhang, “Comparison of different methods for variable selection,” Analytica Chimica Acta, vol. 446, no. 1-2, pp. 477–483, 2001. View at Publisher · View at Google Scholar
- L. Nørgaard, A. Saudland, J. Wagner, J. P. Nielsen, L. Munck, and S. B. Engelsen, “Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy,” Applied Spectroscopy, vol. 54, no. 3, pp. 413–419, 2000. View at Publisher · View at Google Scholar
- L. Munck, J. Pram Nielsen, B. Møller, et al., “Exploring the phenotypic expression of a regulatory proteome-altering gene by spectroscopy and chemometrics,” Analytica Chimica Acta, vol. 446, no. 1-2, pp. 171–186, 2001. View at Publisher · View at Google Scholar
- J.-H. Jiang, R. James Berry, H. W. Siesler, and Y. Ozaki, “Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data,” Analytical Chemistry, vol. 74, no. 14, pp. 3555–3565, 2002. View at Publisher · View at Google Scholar
- R. Leardi and L. Nørgaard, “Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions,” Journal of Chemometrics, vol. 18, no. 11, pp. 486–497, 2004. View at Publisher · View at Google Scholar
- Y. P. Du, Y. Z. Liang, J. H. Jiang, R. J. Berry, and Y. Ozaki, “Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares,” Analytica Chimica Acta, vol. 501, no. 2, pp. 183–191, 2004. View at Publisher · View at Google Scholar
- J. A. Cramer, K. E. Kramer, K. J. Johnson, R. E. Morris, and S. L. Rose-Pehrsson, “Automated wavelength selection for spectroscopic fuel models by symmetrically contracting repeated unmoving window partial least squares,” Chemometrics and Intelligent Laboratory Systems, vol. 92, no. 1, pp. 13–21, 2008. View at Publisher · View at Google Scholar
- Q.-S. Xu and Y.-Z. Liang, “Monte Carlo cross validation,” Chemometrics and Intelligent Laboratory Systems, vol. 56, no. 1, pp. 1–11, 2001. View at Publisher · View at Google Scholar
- L. Xu, J.-H. Jiang, Y.-P. Zhou, H.-L. Wu, G.-L. Shen, and R.-Q. Yu, “MCCV stacked regression for model combination and fast spectral interval selection in multivariate calibration,” Chemometrics and Intelligent Laboratory Systems, vol. 87, no. 2, pp. 226–230, 2007. View at Publisher · View at Google Scholar
- D. Wolpert, “A mathematical theory of generalization: part I, part II,” Complex Systems, vol. 4, pp. 151–200, 1990.
- L. Breiman, “Stacked regressions,” Machine Learning, vol. 24, no. 1, pp. 49–64, 1996.
- R. A. Fisher, “Frequency distributions of the values of the correlation coefficient in samples from an indefinitely large population,” Biometrika, vol. 10, no. 4, pp. 507–521, 1915.
- D. L. Hawkins, “Using statistics to derive the asymptotic distribution of Fisher's statistic,” The American Statistician, vol. 43, no. 4, pp. 235–237, 1989. View at Publisher · View at Google Scholar
- F. Wülfert, W. Th. Kok, and A. K. Smilde, “Influence of temperature on vibrational spectra and consequences for the predictive ability of multivariate models,” Analytical Chemistry, vol. 70, no. 9, pp. 1761–1767, 1998. View at Publisher · View at Google Scholar
- R. D. Snee, “Validation of regression models: methods and examples,” Technometrics, vol. 19, no. 4, pp. 415–428, 1977. View at Publisher · View at Google Scholar
- L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996.
- M. Hubert and S. Verboven, “A robust PCR method for high-dimensional regressors,” Journal of Chemometrics, vol. 17, no. 8-9, pp. 438–452, 2003. View at Publisher · View at Google Scholar
- M. Hubert and K. Vanden Branden, “Robust methods for partial least squares regression,” Journal of Chemometrics, vol. 17, no. 10, pp. 537–549, 2003. View at Publisher · View at Google Scholar
- S. Serneels, C. Croux, P. Filzmoser, and P. J. Van Espen, “Partial robust M-regression,” Chemometrics and Intelligent Laboratory Systems, vol. 79, no. 1-2, pp. 55–64, 2005. View at Publisher · View at Google Scholar