O2-PLS for qualitative and quantitative analysis in multivariate calibration. Trygg, J. Journal of Chemometrics, 16(6):283–293, 2002. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cem.724![O2-PLS for qualitative and quantitative analysis in multivariate calibration [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In this paper the O-PLS method [1] has been modified to further improve its interpretational functionality to give (a) estimates of the pure constituent profiles in X as well as model (b) the Y-orthogonal variation in X, (c) the X-orthogonal variation in Y and (d) the joint X–Y covariation. It is also predictive in both ways, X ↔ Y. We call this the O2-PLS approach. In earlier papers we discussed the improved interpretation using O-PLS compared to the partial least squares projections to latent structures (PLS) when systematic Y-orthogonal variation in X exists, i.e. when a PLS model has more components than the number of Y variables. In this paper we show how the parameters in the PLS model are affected and to what degree the interpretational ability of the PLS components changes with the amount of Y-orthogonal variation. In both real and synthetic examples, the O2-PLS method provided improved interpretation of the model and gave a good estimate of the pure constituent profiles, and the prediction ability was similar to the standard PLS model. The method is discussed from geometric and algebraic points of view, and a detailed description of this modified O2-PLS method is given and reviewed. Copyright © 2002 John Wiley & Sons, Ltd.
@article{trygg_o2-pls_2002,
title = {O2-{PLS} for qualitative and quantitative analysis in multivariate calibration},
volume = {16},
issn = {1099-128X},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cem.724},
doi = {10/fwbqdw},
abstract = {In this paper the O-PLS method [1] has been modified to further improve its interpretational functionality to give (a) estimates of the pure constituent profiles in X as well as model (b) the Y-orthogonal variation in X, (c) the X-orthogonal variation in Y and (d) the joint X–Y covariation. It is also predictive in both ways, X ↔ Y. We call this the O2-PLS approach. In earlier papers we discussed the improved interpretation using O-PLS compared to the partial least squares projections to latent structures (PLS) when systematic Y-orthogonal variation in X exists, i.e. when a PLS model has more components than the number of Y variables. In this paper we show how the parameters in the PLS model are affected and to what degree the interpretational ability of the PLS components changes with the amount of Y-orthogonal variation. In both real and synthetic examples, the O2-PLS method provided improved interpretation of the model and gave a good estimate of the pure constituent profiles, and the prediction ability was similar to the standard PLS model. The method is discussed from geometric and algebraic points of view, and a detailed description of this modified O2-PLS method is given and reviewed. Copyright © 2002 John Wiley \& Sons, Ltd.},
language = {en},
number = {6},
urldate = {2021-10-19},
journal = {Journal of Chemometrics},
author = {Trygg, Johan},
year = {2002},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cem.724},
keywords = {O-PLS, O2-PLS, PLS, multivariate calibration, preprocessing, pure profile estimation},
pages = {283--293},
}
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