title: Boosting partial least squares.
abstract: A difficulty when applying partial least squares (PLS) in multivariate calibration is that overfitting may occur. This study proposes a novel approach by combining PLS and boosting. The latter is said to be resistant to overfitting. The proposed method, called boosting PLS (BPLS), combines a set of shrunken PLS models, each with only one PLS component. The method is iterative: the models are constructed on the basis of the residuals of the responses that are not explained by previous models. Unlike classical PLS, BPLS does not need to select an adequate number of PLS components to be included in the model. On the other hand, two parameters must be determined: the shrinkage value and the iteration number. Criteria are proposed for these two purposes. BPLS was applied to seven real data sets, and the results demonstrate that it is more resistant than classical PLS to overfitting without loosing accuracy.
authors.affiliation: ChemoAC, Department of Pharmaceutical and Biomedical Analysis, Pharmaceutical Institute, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium.
authors.forename: M H
authors.initials: MH
authors.lastname: Zhang
authors.forename: Q S
authors.initials: QS
authors.lastname: Xu
authors.forename: D L
authors.initials: DL
authors.lastname: Massart
mesh_terms: D000465:Algorithms; D002138:Calibration; D002623:Chemistry Techniques, Analytical; D016018:Least-Squares Analysis; D008460:Meat / Q000032:analysis; D008899:Mineral Oil / Q000737:chemistry; D015233:Models, Statistical; D009010:Monte Carlo Method; D009824:Ointments / Q000737:chemistry; D018894:Reverse Transcriptase Inhibitors / Q000737:chemistry; D019265:Spectroscopy, Near-Infrared; D013662:Tea / Q000737:chemistry; D014908:Triticum / Q000737:chemistry
chemical_list: D009824:Ointments; D018894:Reverse Transcriptase Inhibitors; D013662:Tea; D008899:Mineral Oil