O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter

Journal of Chemometrics - Tập 17 Số 1 - Trang 53-64 - 2003
Johan Trygg1, Svante Wold2
1Institute for Molecular Bioscience, University of Queensland, Australia
2Research Group for Chemometrics, Institute of Chemistry, Umea University, Umea, Sweden.

Tóm tắt

AbstractThe O2‐PLS method is derived from the basic partial least squares projections to latent structures (PLS) prediction approach. The importance of the covariation matrix (Y TX) is pointed out in relation to both the prediction model and the structured noise in both X and Y. Structured noise in X (or Y) is defined as the systematic variation of X (or Y) not linearly correlated with Y (or X). Examples in spectroscopy include baseline, drift and scatter effects. If structured noise is present in X, the existing latent variable regression (LVR) methods, e.g. PLS, will have weakened score–loading correspondence beyond the first component. This negatively affects the interpretation of model parameters such as scores and loadings. The O2‐PLS method models and predicts both X and Y and has an integral orthogonal signal correction (OSC) filter that separates the structured noise in X and Y from their joint X–Y covariation used in the prediction model. This leads to a minimal number of predictive components with full score–loading correspondence and also an opportunity to interpret the structured noise. In both a real and a simulated example, O2‐PLS and PLS gave very similar predictions of Y. However, the interpretation of the prediction models was clearly improved with O2‐PLS, because structured noise was present. In the NIR example, O2‐PLS revealed a strong water peak and baseline offset in the structured noise components. In the simulated example the O2‐PLS plot of observed versus predicted Y‐scores (u vs u hat) showed good predictions. The corresponding loading vectors provided good interpretation of the covarying analytes in X and Y. Copyright © 2003 John Wiley & Sons, Ltd.

Từ khóa


Tài liệu tham khảo

Martens H, 1994, Multivariate Calibration

10.1002/cem.695

10.1016/S0169-7439(01)00156-3

10.1366/0003702854248656

10.1016/S0169-7439(98)00109-9

10.1016/S0169-7439(99)00045-3

10.1016/S0169-7439(98)00112-9

10.1016/S0169-7439(01)00102-2

10.1016/S0003-2670(00)00890-4

10.1016/S0169-7439(00)00113-1

Wold H, 1982, Systems under Indirect Observation, 1

10.1016/0169-7439(92)80088-L

10.1002/cem.1180020306

Wold H, 1966, Research Papers in Statistics, 441

Höskuldsson A, 1996, Prediction Methods in Science and Technology

10.1002/cem.736

Box GEP, 1978, Statistics for Experimenters

10.1002/cem.700

10.1016/S0169-7439(01)00110-1

TryggJ.Parsimonious multivariate models.PhD Thesis Umeå University 2001;19–54(http://www.chem.umu.se/dep/orgchem/forskning/thesis/johantrygg/jtabstract.stm).

Rao CR, 1964, The use and interpretation of principal component analysis in applied research, Sankhya A, 26, 329

10.1016/0169-7439(93)85002-X

10.1002/cem.724

10.1002/0471725331

10.1080/00401706.1978.10489693

10.1002/aic.690440509

10.1016/S0169-7439(99)00058-1

Thông tin
Thông tin xuất bản

Journal of Chemometrics

Tập 17 Số 1

53-64

Thông tin tác giả