Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1168475 | Analytica Chimica Acta | 2009 | 5 Pages |
Abstract
In the traditional framework of multivariate spectroscopic calibration, the most popular method, partial least squares (PLS), shrinks the regression coefficients based on the information of training sample concentrations. Motivated by the concept of parallel calibration, the second direction for shrinkage of regression coefficients, the direction towards unknown sample spectra is investigated in this paper. A different multivariate calibration method, parallel calibration model based on partial least squares, PCPLS is proposed. With both theoretical support and analysis of some real data sets, it is demonstrated that the second shrinkage direction is at least as natural and necessary as the traditional one. An interesting difference of the proposed method from traditional methods is the involvement of unknown sample spectra and consideration of their error in the training process. Some new related problems and potential applications of this method are also briefly discussed.
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Lu Xu, Xiao-Ping Yu, Xiu-Lian Lu, Yi-Hang Wu, Hai-Long Wu, Jian-Hui Jiang, Guo-Li Shen, Ru-Qin Yu,