The Partial Least-Squares Model

  • Alejandro C. Olivieri


The most popular first-order model based on partial least-squares is presented, and a range of applications are shown, from single and multiple analyte determinations to sample discrimination.


Partial least-squares regression Calibration and validation Regression coefficients First-order advantage Discriminant partial least-squares 


  1. Fearn, T.: On orthogonal signal correction. Chemom. Intell. Lab. Syst. 50, 47–52 (2000)CrossRefGoogle Scholar
  2. Fernández Pierna, J.A., Abbas, O., Lecler, B., Hogrel, P., Dardenne, P., Baeten, V.: NIR fingerprint screening for early control of non-conformity at feed mills. Food Chem. 189, 2–12 (2015)CrossRefPubMedGoogle Scholar
  3. Goicoechea, H.C., Olivieri, A.C.: Simultaneous determination of rifampicin, isoniazid and pyrazinamide in tablet preparations by multivariate spectrophotometric calibration. J. Pharm. Biomed. Anal. 20, 681–686 (1999)CrossRefPubMedGoogle Scholar
  4. Goicoechea, H.C., Olivieri, A.C.: A comparison of orthogonal signal correction and net analyte preprocessing methods. Theoretical and experimental study. Chemom. Intell. Lab. Syst. 56, 73–81 (2001)CrossRefGoogle Scholar
  5. Ni, W., Nørgaard, L., Mørupc, M.: Non-linear calibration models for near infrared spectroscopy. Anal. Chim. Acta. 813, 1–14 (2014)CrossRefPubMedGoogle Scholar
  6. Reis, M.S., Saraiva, P.M.: A comparative study of linear regression methods in noisy environments. J. Chemom. 18, 526–536 (2004)CrossRefGoogle Scholar
  7. Schreyer, S.K., Bidinosti, M., Wentzell, P.D.: Application of maximum likelihood principal components regression to fluorescence emission spectra. Appl. Spectrosc. 56, 789–796 (2002)CrossRefGoogle Scholar
  8. Soares, L.F., da Silva, D.C., Bergoa, M.C.J., Coradina, V.T.R., Braga, J.W.B., Pastore, T.C.M.: Avaliação de espectrômetro NIR portátil e PLS-DA para a discriminação de seis espécies similares de madeiras amazônicas. Quim Nova. 40, 418–426 (2017)Google Scholar
  9. Svensson, O., Kourti, T., MacGregor, J.F.: An investigation of orthogonal signal correction algorithms and their characteristics. J. Chemom. 16, 176–188 (2002)CrossRefGoogle Scholar
  10. Thomas, E.V., Haaland, D.M.: Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information. Anal. Chem. 60, 1193–1202 (1988)CrossRefGoogle Scholar
  11. Wentzell, P.D.: Measurement errors in multivariate chemical data. J. Braz. Chem. Soc. 25, 183–196 (2014)Google Scholar
  12. Wold, H.: Estimation of principal components and related models by iterative least squares. In: Krishnaiah, P.R. (ed.) Multivariate Analysis, pp. 391–420. Academic Press, New York (1966)Google Scholar
  13. Wold, S., Antti, H., Lindgren, F., Öhman, J.: Orthogonal signal correction of near-infrared spectra. Chemom. Intell. Lab. Syst. 44, 175–185 (1998)CrossRefGoogle Scholar
  14. Wold, S., Sjöström, M., Eriksson, L.: PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58, 109–130 (2001)CrossRefGoogle Scholar
  15. Xu, L., Schechter, I.: A calibration method free of optimum factor number selection for automated multivariate analysis. Experimental and theoretical study. Anal. Chem. 69, 3722–3730 (1997)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Alejandro C. Olivieri
    • 1
  1. 1.Universidad Nacional de Rosario, Instituto de Química Rosario - CONICETRosarioArgentina

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