Selection of discriminant wavelength intervals in NIR spectrometry with genetic algorithms

Journal of Chemometrics

Volumn 20, Issue 3-4, 2006, Pages 136-145

  Reynès, Christelle ^a   De Souza, Sabrina ^a   Sabatier, Robert ^a   Figuères, Gilles ^b   Vidal, Bernard ^b  

^a UNIV MONTPELLIER   (France)

^b Altadis   (France)

Author keywords

Feature selection; Genetic algorithms; Near infrared spectrometry

Indexed keywords

DISCRIMINANT ANALYSIS; FEATURE SELECTION; INFRARED DEVICES; INFRARED SPECTROSCOPY; SPECTROMETRY; TOBACCO;

FACTORIAL DISCRIMINANT ANALYSIS; FEATURES SELECTION; FISHERS'S LINEAR DISCRIMINANT ANALYSIS; NEAR INFRARED SPECTROMETRY; NEAR-INFRARED SPECTRAL REGIONS; NIR SPECTROMETRY; SPECTRA'S; VISIBLE AND NEAR INFRARED; WAVELENGTH SELECTION;

GENETIC ALGORITHMS;

EID: 34247272512     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1000     Document Type: Article

References (34)

1. Bertrand D. La spectrométrie proche infra-rouge et ses applications dans les industries de l'alimentation animate. INRA Prod. Anim. 2002; 15: 209-219.
2. Davies T, Fearn T. Sorting the wheat from the chaff. Rapport technique, Spectroscopy Europe, 2003.
3. Figueres G, Letourneux S. Quantitative and qualitative applications of near infrared spectroscopy in the tobacco industry. 6th Tobacco Scientific Meeting, Bergerac, France, 1-2 September 2005. (Proceedings In Press).
4. Martens H, Naes T. Multivariate Calibration. Wiley: Chichester, UK, 1989.
5. Verron T. Traitement du signal et généralisation de la régression PLS pour la modélisation de spectres PIR. Thèse de doctorat, Université Montpellier 1, Montpellier, 2005.
6. Thomas EV. A primer on mutivariate calibration. Anal. Chem. 1994; 66: 795-804.
7. Goldberg DE. Genetic algorithms: Search, optimization and machine learning. Addison-Wesley: New York, 1989.
8. Holland J. Adaptation in Natural and Artificial Systems. University of Michigan Press: Ann Arbor, MI, 1975.
9. Bangalore AS, Shaffer RE, Small GW. Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy. Anal. Chem. 1996; 68: 4200-4212.
10. Broadhurst D, Goodacre R, Jones A, Rowland JJ, Kell DB. Generic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry. Anal. Chim. Acta 1997; 348: 71-86.
11. Chiang LH, Pell RJ. Genetic algorithms applied to the near infrared analysis of gasolines. J. Process Control 2003; 14: 143-155.
12. Jouan-Rimbaud D, Massart DL, Leardi R, De Noord O. Genetic algorithms as a tool for wavelength selection in mutivariate calibration. Anal. Chem. 1995; 67: 4295-4301.
13. Lavine BK, Davidson CE, Moores AJ. Innovative genetic algorithms for chemoinformatics. Chemometr. Intell. Lab. Syst. 2002; 661: 51-62.
14. Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. J. Chemometr. 2000; 14: 643-655.
15. Leardi R, Norgaard L. Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. J. Chemometr. 2004; 18: 486-497.
16. Lestander TA, Leardi R, Geladi P. Selection of near infrared wave-lengths using genetic algorithms for the determination of seed moisture content. J. Near Infrared Spectrosc. 2003; 11: 433-446.
17. Lucasius CB, Beckers MLM, Kateman G. Genetic algorithms in wavelengths selection: a comparative study.
18. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning, Data Mining, Inference and Prediction. Springer-Verlag: New York, 2001; 533p.
19. Mardia KV, Kent JT, Bibby JM. Multivariate analysis-Probability and Mathematical Statistics. Academic Press: London, 1979.
20. Figuères G, Andrieu E, Letourneux S, Biesse JP, Vidal B. Near infrared spectroscopy and pattern recognition as screening methods for classification of commercial tobacco blends. Congrès de Chimiométrie, Paris, France, 30 November & 1st December 2004.
21. Salomon R. The influence of different coding schemes on the computational complexity of genetic algorithms in function optimization. Proceedings of the fourth international conference on parallel problem solving from nature. Springer-Verlag: Berlin, 1996; 227-235.
22. Reeves C, Rowe J. Genetic algorithms: principles and perspectives: a guide to GA theory. Kluwer Academic: Boston, 2003.
23. Bhandari D, Murthy CA, Pasl SK. Genetic algorithm with elitist model and its convergence. Int. J. Pattern Recognit. Artifif. Intell. 1996; 10(6): 731-747.
24. Jeffries NO. Performance of a genetic algorithm for mass spectrometry proteomics. BMC Bioinformatics 2004; 5: 180.
25. Green PJ. Reversible Jump Markov Chain Monte Carlo computation and Bayesian model determination. Biome trika 1995; 82: 711-732.
26. DiMatteo I, Genovese CR, Kass RE. Bayesian curve-fitting with free-knot splines. Biometrika Trust 2001; 88: 1055-1071.
27. Pittman J. Adaptive Splines and Genetic Algorithms. J. Comput. Graph. Stat. 2002; 11(3): 615-638.
28. Naes T, Mevik B-H. Understanding the collinearity problem in regression and discriminant analysis. J. Chemometr. 2001; 15: 413-426.
29. Barker M, Rayens W. Partial Least Squares for discrimination. J. Chemometr. 2003; 17: 166-173.
30. R Development Core Team. 2004; R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0 URL http://www.R-project.org.
31. Tenenhaus M. La Regression PLS, Théorie et Pratique. Technip: Paris, 1998; 254p.
32. Osborne BG, Fearn T, Hindle PH. Practical NIR spectro scopy with Applications in Food and Beverage Analysis, 2nd edn. Longman Scientific & Technical, Harlow: Essex, UK, 1993.
33. Siriex A, Downey G. Commercial wheatflour authentification by discriminant analysis of near infrared reflectance spectra. J. Near Infrared Spectrosc. 1993; 1: 187-197.
34. William P, Norris K. Near Infrared Technology in the Agricultural and Food Industries. American Association of Cereal Chemists, Inc.: St Paul, MN, USA, 1987.