A two-stage regression approach for spectroscopic quantitative analysis

Chemometrics and Intelligent Laboratory Systems

Volumn 109, Issue 1, 2011, Pages 34-41

  Douak, F ^a,b   Benoudjit, N ^a   Melgani, F ^b  

^a Université de Batna ^*  (Algeria)

^b UNIVERSITY OF TRENTO   (Italy)

Author keywords

Boosting; Feature selection; Partial least squares regression (PLSR); Radial basis function neural network (RBFN); Residual based correction (RBC); Spectrometry; Support vector machines (SVM)

Indexed keywords

ACCURACY; ANALYTICAL ERROR; ARTICLE; CLUSTER ANALYSIS; CONTROLLED STUDY; KERNEL METHOD; LINEAR SYSTEM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; NEAR INFRARED SPECTROSCOPY; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RADIAL BASED FUNCTION; SUPPORT VECTOR MACHINE;

EID: 80053622629     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2011.07.007     Document Type: Article

References (38)

1. Devos O., Ruckebusch C., Durand A., Duponchel L., Huvenne J.P. Support vector machines (SVM) in near infrared (NIR) spectroscopy: focus on parameters optimization and model interpretation. Chemom. Intell. Lab. Syst. 2009, 96:27-33.
2. Nicolaï B.M., Theron K.I., Lammertyn J. Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemom. Intell. Lab. Syst. 2007, 85:243-252.
3. Massart D.L., Vandeginste B.G.M., Buydens L., De Jong S., Lewi P., Smeyers-Verbeke J. Handbook of Chemometrics and Qualimetrics: Part A 1997, Elsevier, Amsterdam.
4. D. Francois, High-dimensional data analysis: optimal metrics and feature selection, Ph.D. Thesis, Université Catholique de Louvain, Belgium, 2007.
5. Rossi F., Francois D., Wertz V., Meurens M., Verleysen M. Fast selection of spectral variables with B-spline compression. Chemom. Intell. Lab. Syst. 2007, 86:208-218.
6. Benoudjit N., François D., Meurens M., Verleysen M. Spectrophotometric variable selection by mutual information. Chemom. Intell. Lab. Syst. 2004, 74:243-251.
7. Fei Q., Li M., Wang B., Huan Y., Feng G., Ren Y. Analysis of cefalexin with NIR spectrometry coupled to artificial neural networks with modified genetic algorithm for wavelength selection. Chemom. Intell. Lab. Syst. 2009, 97:127-131.
8. Hemmateenejad B., Miri R., Akhond M., Shamsipur M. QSAR study of the calcium channel antagonist activity of some recently synthesized dihydropyridine derivatives. An application of genetic algorithm for variable selection in MLR and PLS methods. Chemom. Intell. Lab. Syst. 2002, 64:91-99.
9. Benoudjit N., Cools E., Meurens M., Verleysen M. Chemometric calibration of infrared spectrometers: selection and validation of variables by non-linear models. Chemom. Intell. Lab. Syst. 2004, 70:47-53.
10. Eklöv T., Mårtensson P., Lundström I. Selection of variables for interpreting multivariate gas sensor data. Anal. Chim. Acta. 1999, 381:221-232.
11. Geladi P. Some recent trends in the calibration literature. Chemom. Intell. Lab. Syst. 2002, 60:211-224.
12. Patterson D. Artificial Neural Networks. Theory and Applications 1996, Prentice-Hall, Upper Saddle River.
13. Zupan J., Gasteiger J. Neural Networks for Chemists; An Introduction 1993, Verlag Chemie, Weinheim.
14. Vapnik V.N. The Nature of Statistical Learning Theory, Springer-Verlag 1995, New York, USA.
15. Scholkopf B., Smola A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2001, MIT Press, Cambridge, MA, USA.
16. Reiriz A.G., Damiani P.C., Culzoni M.J., Goicoechea H.C., Olivieri A.C. A versatile strategy for achieving the second-order advantage when applying different artificial neural networks to non-linear second-order data: unfolded principal component analysis/residual bilinearization. Chemom. Intell. Lab. Syst. 2008, 92:61-70.
17. Fidêncio P.H., Poppi R.J., de Andrade J.C. Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy. Anal. Chim. Acta. 2002, 453:125-134.
18. Smola A., Schölkopf B. A tutorial on support vector regression. NeuroCOLT Tech. Rep. NC-TR-98-030 1998, Royal Holloway College, Univ. London, London, U.K.
19. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, 1st ed 2000, Cambridge Univ. Press, Cambridge, U.K.
20. Park J., Sandberg I. Approximation and radial basis function networks. Neural Comput. 1993, 5:305-316.
21. Poggio T., Girosi F. Networks for approximation and learning. Proc. IEEE 1990, 78(9):1481-1497.
22. Orr M.J. Introduction to radial basis functions networks. Technical Reports 1996, April. http://www.anc.ed.ac.uk/~mjo/papers/intro.ps.
23. Freund Y., Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1997, 55:119-139.
24. Friedman J.H. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002, 38:367-378.
25. Friedman J.H. Multivariate adaptive regression splines. Ann. Stat. 1991, 19:1-141.
26. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, Wadsworth, Monterey.
27. Shao X., Bian X., Cai W. An improved boosting partial least squares method for near-infrared spectroscopic quantitative analysis. Anal. Chim. Acta. 2010, 666:32-37.
28. Zhou Y.P., Jiang J.H., Lin W.Q., Zou H.Y., Wu H.L., Shen G.L., Yu R.Q. Boosting support vector regression in QSAR studies of bioactivities of chemical compounds. Eur. Pharmacol. Sci. 2006, 28:344-353.
29. Zhang M.H., Xu Q.S., Massart D.L. Boosting partial least squares. Anal. Chem. 2005, 77:1423-1431.
30. Shinzawa H., Jiang J.H., Ritthiruangdej P., Ozaki Y. Investigations of bagged kernel partial least squares (KPLS) and boosting KPLS with applications to near-infrared (NIR) spectra. J. Chemom. 2006, 20:436-444.
31. Zhou Y.P., Jiang J.H., Wu H.L., Shen G.L., Yu R.Q., Ozaki Y. Dry film method with ytterbium as the internal standard for near infrared spectroscopic plasma glucose assay coupled with boosting support vector regression. J. Chemom. 2006, 20:13-21.
32. Drucker H. Improving regressors using boosting techniques. Proceedings of the 14th International Conference on Machine Learning, Nashville, TN, July 8-12 1997, 107-115.
33. Datasets provided by Prof. Marc Meurens, Université catholique de Louvain, BNUT meurens@bnut.ucl.ac.be. Wine and orange juice datasets available from. http://www.ucl.ac.be/mlg/.
34. Rossi F., Lendasse A., Francois D., Wertz V., Verleysen M. Mutual information for the selection of relevant variables in spectrometric nonlinear modelling. Chemom. Intell. Lab. Syst. 2006, 80:215-226.
35. Krier C., Rossi F., François D., Verleysen M. A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis. Chemom. Intell. Lab. Syst. 2008, 91:43-53.
36. Tecator meat sample dataset Available from. http://lib.stat.cmu.edu/datasets/tecator.
37. Maimon O.Z., Rokach L. Data Mining and Knowledge Discovery Handbook 2005, Springer-Verlag, New York Inc.
38. van der Voet H. Comparing the predictive accuracy of models using a simple randomization test. Chemom. Intell. Lab. Syst. 1994, 25:313-323.