Orthogonal projections to latent structures (O-PLS)
2002 (English)In: Journal of Chemometrics, Vol. 16, no 3, 119-28 p.Article in journal (Refereed) Published
A generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O-PLS), is described. O-PLS removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity). In mathematical terms this is equivalent to removing systematic variation in X that is orthogonal to Y. In an earlier paper, Wold et al. (Chemometrics Intell. Lab. Syst. 1998; 44: 175-185) described orthogonal signal correction (OSC). In this paper a method with the same objective but with different means is described. The proposed O-PLS method analyzes the variation explained in each PLS component. The non-correlated systematic variation in X is removed, making interpretation of the resulting PLS model easier and with the additional benefit that the non-correlated variation itself can be analyzed further. As an example, near-infrared (NIR) reflectance spectra of wood chips were analyzed. Applying O-PLS resulted in reduced model complexity with preserved prediction ability, effective removal of non-correlated variation in X and, not least, improved interpretational ability of both correlated and non-correlated variation in the NIR spectra.
Place, publisher, year, edition, pages
2002. Vol. 16, no 3, 119-28 p.
orthogonal projections to latent structures (O-PLS), orthogonal signal correction (OSC), NIPALS PLS, multivariate data analysis, calibration, preprocessing
Computer and Information Science
IdentifiersURN: urn:nbn:se:umu:diva-9115DOI: 10.1002/cem.695OAI: oai:DiVA.org:umu-9115DiVA: diva2:148786