Application of latent projective graph in variable selection for near infrared spectral analysis

Chemometrics and Intelligent Laboratory Systems

Volumn 114, Issue , 2012, Pages 44-49

  Shao, Xueguang ^a   Du, Guorong ^a   Jing, Ming ^a   Cai, Wensheng ^a  

^a NANKAI UNIVERSITY   (China)

Author keywords

Latent projective graph; Multivariate calibration; Near infrared spectroscopy; Variable selection

Indexed keywords

ARTICLE; BLOOD SAMPLING; CALIBRATION; CONTROLLED STUDY; INTERMETHOD COMPARISON; LATENT PROJECTIVE GRAPH; NEAR INFRARED SPECTROSCOPY; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SIMULATION; STATISTICAL ANALYSIS; TABLET; TOBACCO;

EID: 84860487253     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2012.03.003     Document Type: Article

References (39)

1. Rambla F.J., Garrigues S., De La Guardia M. PLS-NIR determination of total sugar, glucose, fructose and sucrose in aqueous solutions of fruit juices. Analytica Chimica Acta 1997, 344:41-53.
2. Trygg J., Wold S. PLS regression on wavelet compressed NIR spectra. Chemometrics and Intelligent Laboratory Systems 1998, 42:209-220.
3. Shao X.G., Bian X.H., Liu J.J., Zhang M., Cai W.S. Multivariate calibration methods in near infrared spectroscopic analysis. Analytical Methods 2010, 2:1662-1666.
4. Chauchard F., Cogdill R., Roussel S., Roger J.M., Bellon-Maurel V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems 2004, 71:141-150.
5. Borin A., Ferrao M.F., Mello C., Maretto D.A., Poppi R.J. Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Analytica Chimica Acta 2006, 579:25-32.
6. Leardi R., Gonzalez A.L. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemometrics and Intelligent Laboratory Systems 1998, 41:195-207.
7. Koljonen J., Nordling T.E.M., Alander J.T. A review of genetic algorithms in near infrared spectroscopy and chemometrics: past and future. Journal of Near Infrared Spectroscopy 2008, 16:189-197.
8. Lindgren F., Geladi P., Rannar S., Wold S. Interactive variable selection (IVS) for PLS. Part 1: theory and algorithms. Journal of Chemometrics 1994, 8:349-363.
9. Lindgren F., Geladi P., Berglund A., Sjostrom M., Wold S. Interactive variable selection (IVS) for PLS. Part II: chemical applications. Journal of Chemometrics 1995, 9:331-342.
10. Centner V., Massart D.L., de Noord O.E., de Jong S., Vandeginste B.M., Sterna C. Elimination of uninformative variables for multivariate calibration. Analytical Chemistry 1996, 68:3851-3858.
11. Ye S.F., Wang D., Min S.G. Successive projections algorithm combined with uninformative variable elimination for spectral variable selection. Chemometrics and Intelligent Laboratory Systems 2008, 91:194-199.
12. Li H.D., Liang Y.Z., Xu Q.S., Cao D.S. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta 2009, 648:77-84.
13. Araujo M.C.U., Saldanha T.C.B., Galvao R.K.H., Yoneyama T., Chame H.C., Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems 2001, 57:65-73.
14. Galvao R.K.H., Araujo M.C.U., Fragoso W.D., Silva E.C., Jose G.E., Soares S.F.C., Paiva H.M. A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemometrics and Intelligent Laboratory Systems 2008, 92:83-91.
15. Fu G.H., Xu Q.S., Li H.D., Cao D.S., Liang Y.Z. Elastic net grouping variable selection combined with partial least squares regression (EN-PLSR) for the analysis of strongly multi-collinear spectroscopic data. Applied Spectroscopy 2011, 65:402-408.
16. Chun H., Keles S. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society - Series B 2010, 72:3-25.
17. Lee D., Lee W., Lee Y., Pawitan Y. Sparse partial least-squares regression and its applications to high-throughput data analysis. Chemometrics and Intelligent Laboratory Systems 2011, 109:1-8.
18. Zou H.Y., Wu H.L., Fu H.Y., Tang L.J., Xu L., Nie J.F., Yu R.Q. Variable-weighted least-squares support vector machine for multivariate spectral analysis. Talanta 2010, 80:1698-1701.
19. Andries J.P.M., Vander Heyden Y., Buydens L.M.C. Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity. Analytica Chimica Acta 2011, 705:292-305.
20. Chen D., Hu X.G., Shao X.G., Su Q.D. Variable selection by modified IPW (iterative predictor weighting)-PLS (partial least squares) in continuous wavelet regression models. Analyst 2004, 129:664-669.
21. Chen D., Shao X.G., Hu B., Su Q.D. Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra. Analytical Sciences 2005, 21:161-166.
22. Shao X.G., Wang F., Chen D., Su Q.D. A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables. Analytical and Bioanalytical Chemistry 2004, 378:1382-1387.
23. Cai W.S., Li Y.K., Shao X.G. A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems 2008, 90:188-194.
24. Xu H., Liu Z.C., Cai W.S., Shao X.G. A wavelength selection method based on randomization test for near-infrared spectral analysis. Chemometrics and Intelligent Laboratory Systems 2009, 97:189-193.
25. Malinowski E.R. Theory of error in factor analysis. Analytical Chemistry 1977, 49:606-612.
26. Kvalheim O.M. Interpretation of direct latent-variable projection methods and their aims and use in the analysis of multicomponent spectroscopic and chromatographic data. Chemometrics and Intelligent Laboratory Systems 1988, 4:11-25.
27. Kvalheim O.M., Liang Y.Z. Heuristic evolving latent projections: resolving two-way multicomponent data. 1. Selectivity, latent-projective graph, datascope, local rank, and unique resolution. Analytical Chemistry 1992, 64:936-946.
28. Liang Y.Z., Kvalheim O.M. Resolution of two-way data: theoretical background and practical problem-solving Part 1: theoretical background and methodology. Fresenius' Journal of Analytical Chemistry 2001, 370:694-704.
29. Shao X.G., Leung A.K.M., Chau F.T. Wavelet: a new trend in chemistry. Accounts Chemistry Research 2003, 36:276-283.
30. Tan H.W., Brown S.D. Wavelet analysis applied to removing non-constant, varying spectroscopic background in multivariate calibration. Journal of Chemometrics 2002, 16:228-240.
31. Ma C.X., Shao X.G. Continuous wavelet transform applied to removing the fluctuating background in near-infrared spectra. Journal of Chemical Information and Computer Science 2004, 44:907-911.
32. Chen D., Shao X.G., Hu B., Su Q.D. A background and noise elimination method for quantitative calibration of near infrared spectra. Analytica Chimica Acta 2004, 511:37-45.
33. Liu J.J., Ma X., Wen Y.D., Wang Y., Cai W.S., Shao X.G. Online near-infrared spectroscopy combined with alternating trilinear decomposition for process analysis of industrial production and quality assurance. Industrial and Engineering Chemistry Research 2011, 50:7677-7681.
34. Zhang M., Cai W.S., Shao X.G. Wavelet unfolded partial least squares for near-infrared spectral quantitative analysis of blood and tobacco powder samples. Analyst 2011, 136:4217-4221.
35. Norris K.H., Ritchie G.E. Assuring specificity for a multivariate near-infrared (NIR) calibration: the example of the Chambersburg Shoot-out 2002 data set. Journal of Pharmaceutical and Biomedical Analysis 2008, 48:1037-1041.
36. Chen Z.P., Li L.M., Yu R.Q., Littlejohn D., Nordon A., Morris J., Dann A.S., Jeffkins P.A., Richardson M.D., Stimpson S.L. Systematic prediction error correction: a novel strategy for maintaining the predictive abilities of multivariate calibration models. Analyst 2011, 136:98-106.
37. Kuenstner J.T., Norris K.H. Spectrophotometry of human hemoglobin in the near infrared region from 1000 to 2500nm. Journal of Near Infrared Spectroscopy 1994, 2:59-65.
38. Kuenstner J.T., Norris K.H., McCarthy W.F. Measurement of hemoglobin in unlysed blood by near-infrared spectroscopy. Applied Spectroscopy 1994, 48.
39. Kuenstner J.T., Norris K.H. Near infrared hemoglobinometry. Journal of Near Infrared Spectroscopy 1995, 3:11-18.