Stacked partial least squares regression analysis for spectral calibration and prediction

Journal of Chemometrics

Volumn 23, Issue 10, 2009, Pages 505-517

  Ni, Wangdong ^a   Brown, Steven D ^b   Man, Ruilin ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b UNIVERSITY OF DELAWARE   (United States)

Author keywords

Data fusion; Interval partial least squares (iPLS); Partial least squares; Stacked partial least squares; Stacking; Wavelength selection

Indexed keywords

FORECASTING; LEAST SQUARES APPROXIMATIONS; REGRESSION ANALYSIS; STATISTICS;

INTERVAL PARTIAL LEAST SQUARE; PARTIAL LEAST-SQUARES; PLS MODELLING; SPECTRA'S; STACKED PARTIAL LEAST SQUARE; STACKED REGRESSION; STACKINGS; WAVELENGTH SELECTION;

DATA FUSION;

EID: 70350364485     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1246     Document Type: Article

References (19)

1. Xu L, Schechter I. Wavelength selection for simultaneous spectroscopic analysis: experimental and theoretical study. Anal. Chem. 1996; 68: 2392-2400.
2. Hoskuldsson A. Variable and subset selection in PLS regression. Chemom. Intell. Lab. Syst., Lab. Inf. Manag. 2001; 55: 23-38.
3. Nørgaard L, Saudaland A, Wagner J, Nielsen JP, Munck L, Engelsen SB.Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Appl. Spectrosc. 2000; 54: 413-419.
4. Jiang JH, Berry RJ, Siesler HW, Ozaki Y. Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data. Anal. Chem. 2002; 74: 3555-3565.
5. Centner V, Massart DL, Noord OE, Jong SD, Vandeginste BM, Sterna C. Elimination of uninformative variables for multivariate calibration. Anal. Chem. 1996; 68: 3851-3858.
6. Shane MC, Cameron BD, Cote GL, Spiegelman CH. A novel peak-hopping stepwise feature selection method with application to Raman spectroscopy. Anal. Chim. Acta 1999; 338: 251-264.
7. Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. J. Chemometrics 2000; 14: 643-655.
8. Leardi R, Nørgaard L. Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. J. Chemometrics 2004; 18: 486-497.
9. Tan HW, Brown SD. Multivariate calibration of spectral data using dual-domain regression analysis. Anal. Chim. Acta 2003; 490: 291-301.
10. Tan HW, Brown SD. Dual domain regression analysis for spectral calibration models. J. Chemometrics 2003; 17: 111-122.
11. Breiman L. Stacked regressions. Mach. Learn. 1996; 24: 49-64.
12. Pottmann M, Ogunnaike BA, Schwaber JS. Development and implementation of a high-performance sensor system for an industrial polymer reactor. Ind. Eng. Chem. Res. 2005; 44: 2606-2630.
13. Fortuna L, Graziani S, Napoli G, Xibilia MG. Stacking approaches for the design of a soft sensor for sulfur recovery unit. 32nd Annual Conference of the IEEE Industrial Electronics, 2006; 229-234.
14. Hibon M, Evgeniou T. To combine or not to combine, selecting among forecasts and their combinations. Int. J. Forecast. 2005; 21: 15-24.
15. Xu L, Zhou YP, Tang LJ, Wu HL, Jiang JH, Shen GL, Yu RQ. Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration. Anal. Chim. Acta 2008; 616: 138-143.
16. Xu L, Jiang JH, Zhou YP, Wu HL, Shem GL, Yu RQ. MCCV stacked regression for model combination and fast spectral interval selection in multivariate calibration. Chemom. Intell. Lab. Syst. 2007; 87: 226-230.
17. Xu QS, Liang YZ. Monte Carlo cross validation. Chemom. Intell. Lab. Syst. 2001; 56: 1-11.
18. Feudale RN, Tan HW, Brown SD. Piecewise orthogonal signal correction. Chemom. Intell. Lab. Syst. 2002; 63: 129-138.
19. Feudale RN, Tan HW, Brown SD. Improved piecewise orthogonal signal correction algorithms. Appl. Spectrosc. 2003; 57: 1201-1206.