The successive projections algorithm for interval selection in PLS

Microchemical Journal

Volumn 110, Issue , 2013, Pages 202-208

  De Araújo Gomes, Adriano ^a   Galvão, Roberto Kawakami Harrop ^b   De Araújo, Mário Cesar Ugulino ^a   Véras, Germano ^c   Da Silva, Edvan Cirino ^a  

^a FEDERAL UNIVERSITY OF PARAÍBA   (Brazil)

^b INSTITUTO TECNOLÓGICO DE AERONÁUTICA   (Brazil)

^c STATE UNIVERSITY OF PARAÍBA   (Brazil)

Author keywords

IPLS; NIR spectrometry; Partial Least Squares; Successive projections algorithm; Variable selection

Indexed keywords

EID: 84876726373     PISSN: 0026265X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.microc.2013.03.015     Document Type: Article

References (43)

1. Andersen C.M., Bro R. Variable selection in regression: a tutorial. J. Chemom. 2010, 24:728-737.
2. Forina M., Lanteri S., Oliveros M.C.C., Millan C.P. Selection of useful predictors in multivariate calibration. Anal. Bioanal. Chem. 2004, 380:397-418.
3. Leardi R., Nørgaard L. Sequential application of backward interval partial least squares and genetic. J. Chemom. 2004, 18:486-497.
4. Xiaobo Z., Jiewen Z., Povey M.J.W., Holmes M., Hanpin M. Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta 2010, 667:14-32.
5. Shamsipur M., Zare-Shahabadi V., Hemmateenejad B., Akhond M. Ant colony optimisation: a powerful tool for wavelength selection. J. Chemom. 2006, 20:146-147.
6. Mehmood T., Liland K.H., Snipen L., Sæbø S. A review of variable selection methods in Partial Least Squares Regression. Chemom. Intell. Lab. Syst. 2012, 118:62-69.
7. Fujiwara K., Sawada H., Kano M. Input variable selection for PLS modeling using nearest correlation spectral clustering. Chemom. Intell. Lab. Syst. 2012, 118:109-119.
8. Galvão R.K.H., Araújo M.C.U. Comprehensive Chemometrics 2009, vol. 3:233-283. Elsevier, Oxford. B. Walczak, R. Tauler, S. Brown (Eds.).
9. Martens H., Martens M. Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Qual. Prefer. 2000, 11:5-16.
10. Balabin R.M., Smirnov S.V. Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data. Anal. Chim. Acta 2011, 692:63-72.
11. Liao Y., Fan Y., Cheng F. On-line prediction of pH values in fresh pork using visible/near-infrared spectroscopy with wavelet de-noising and variable selection methods. J. Food Eng. 2012, 109:668-675.
12. Goudarzi N., Goodarzi M. Application of successive projections algorithm (SPA) as a variable selection in a QSPR study to predict the octanol/water partition coefficients (Kow) of some halogenated organic compounds. Anal. Methods 2010, 2:758-764.
13. Araújo M.C.U., Saldanha T.C.B., Galvão R.K.H., Yoneyama T., Chame H.C., Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom. Intell. Lab. Syst. 2001, 57:65-73.
14. Galvão R.K.H., Pimentel M.F., Araújo M.C.U., Yoneyama T., Visani V. Aspects of the successive projections algorithm for variable selection in multivariate calibration applied to plasma emission spectrometry. Anal. Chim. Acta 2001, 443:107-115.
15. Soares S.F.C., Gomes A.A., Galvão Filho A.R., Galvão R.K.H., Araújo M.C.U. The successive projections algorithm. Trends Anal. Chem. 2013, 42:84-98.
16. Honorato F.A., Galvão R.K.H., Pimentel M.F., Barros Neto B., Araújo M.C.U., Carvalho F.R. Robust modeling for multivariate calibration transfer by the Successive Projections Algorithm. Chemom. Intell. Lab. Syst. 2005, 76:65-72.
17. Pontes M.J.C., Galvão R.K.H., Araújo M.C.U., Moreira P.N.T., Pessoa Neto O.D., José G.E., Saldanha T.C.B. The successive projections algorithm for spectral variable selection in classification problems. Chemom. Intell. Lab. Syst. 2005, 78:11-18.
18. Moreira E.D., Pontes M.J.C., Galvão R.K.H., Araújo M.C.U. Near infrared reflectance spectrometry classification of cigarettes using the successive projections algorithm for variable selection. Talanta 2009, 79:1260-1264.
19. Ghasemi-Varnamkhasti M., Mohtasebi S.S., Rodriguez-Mendez M.L., Gomes A.A., Araújo M.C.U., Galvão R.K.H. Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection. Talanta 2012, 89:286-291.
20. Dantas Filho H.A., Galvão R.K.H., Araújo M.C.U., Silva E.C., Saldanha T.C.B., José G.E., Pasquini C., Raimundo I.M., Rohwedder J.J.R. A strategy for selecting calibration samples for multivariate modelling. Chemom. Intell. Lab. Syst. 2004, 72:83-91.
21. Di Nezio M.S., Pistonesi M.F., Fragoso W.D., Pontes M.J.C., Goicoechea H.C., Araujo M.C.U., Band B.S.F. Successive projections algorithm improving the multivariate simultaneous direct spectrophotometric determination of five phenolic compounds in sea water. Microchem. J. 2007, 85:194-200.
22. Breitkreitz M.C., Raimundo I.M., Rohwedder J.J.R., Pasquini C., Dantas Filho H.A., José G.E., Araújo M.C.U. Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration. Analyst 2003, 128:1204-1207.
23. Lira L.F.B., Albuquerque M.S., Pacheco J.G.A., Fonseca T.M., Cavalcanti E.H.S., Stragevitch L., Pimentel M.F. Infrared spectroscopy and multivariate calibration to monitor stability quality parameters of biodiesel. Microchem. J. 2010, 96:126-131.
24. Brereton R.G. Introduction to multivariate calibration in analytical Chemistry. Analyst 2000, 15:2125-2154.
25. Leardi R.J. Application of genetic algorithm-PLS for feature selection in spectral data sets. J. Chemom. 2000, 14:643-655.
26. Pierna J.A.F., Abbas O., Baeten V., Dardenne P. A Backward Variable Selection method for PLS regression (BVSPLS). Anal. Chim. Acta 2009, 642:89-93.
27. Forina M., Casolino C., Millan C.P. Iterative predictor weighting (IPW) PLS: a technique for the elimination of useless predictors in regression problems. J. Chemom. 1999, 13:165-184.
28. Centner V., Massart D.L., Noord O.E., Jong S., Vandeginste B.M., Sterna C. Elimination of uninformative variables for multivariate calibration. Anal. Chem. 1996, 68:3851-3858.
29. Allegrini F., Olivieri A.C. A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis. Anal. Chim. Acta 2011, 699:18-25.
30. Hageman J.A., Streppel M., Wehrens R., Buydens L.M.C. Wavelength selection with Tabu Search. J. Chemom. 2003, 17:427-437.
31. Du Y.P., Liang Y.Z., Jiang J.H., Berry R.J., Ozaki Y. 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. Anal. Chim. Acta 2004, 501:183-191.
32. Müller A.L.H., Flores E.M.M., Müller E.I., Silva F.E.B., Ferrão M.F. Attenuated total reflectance with Fourier transform infrared spectroscopy (ATR/FTIR) and different PLS algorithms for simultaneous determination of clavulanic acid and amoxicillin in powder pharmaceutical formulation. J. Braz. Chem. Soc. 2011, 22:1903-1912.
33. Teófilo R.F., Martins J.P.A., Ferreira M.M.C. Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression. J. Chemom. 2009, 23:32-48.
34. Paiva H.M., Soares S.F.C., Galvão R.K.H., Araújo M.C.U. A graphical user interface for variable selection employing the Successive Projections Algorithm. Chemom. Intell. Lab. Syst. 2012, 118:260-266.
35. Galvão R.K.H., Araújo M.C.U., Fragoso W.D., Silva E.C., José 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. Chemom. Intell. Lab. Syst. 2008, 92:83-91.
36. Galvão R.K.H., Araújo M.C.U., Silva E.C., José G.E., Soares S.F.C., Paiva H.M. Cross-validation for the selection of spectral variables using the successive projections algorithm. J. Braz. Chem. Soc. 2007, 18:1580-1584.
37. Westad F., Martens H. Variable selection in near infrared spectroscopy based on significance testing in partial least squares regression. J. Near Infrared Spectrosc. 2000, 8:117-124.
38. Fernandes D.D.S., Gomes A.A., Costa G.B., Silva G.W.B., Véras G. Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection. Talanta 2011, 87:30-34.
39. Galvão R.K.H., Araújo M.C.U., José G.E., Pontes M.J.C., Silva E.C., Saldanha T.C.B. A method for calibration and validation subset partitioning. Talanta 2005, 67:736-740.
40. Tsuchikawa S.A. A review of recent near infrared research for wood and paper. Appl. Spectrosc. Rev. 2007, 42:42-71.
41. Annual Book of ASTM Standards Standards Practices for Infrared, Multivariate, Quantitative Analysis, E1655 2012, vol. 03.06. ASTM International, West Conshohocken, Pennsylvania, USA.
42. Li Ba., Wang D., Xu C., Zhang Z. Flow-injection simultaneous chemiluminescence determination of ascorbic acid and l-cysteine with partial least squares calibration. Microchim. Acta 2005, 149:205-212.
43. Haaland D.M., Thomas E.V. Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information. Anal. Chem. 1988, 60:1193-1202.