The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter
Abstract
The 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Addelman, S. (1979) · Technometrics · doi:10.1080/00401706.1979.10489788 · Cited by 6,750
Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
Wold, S. (1978) · Technometrics · doi:10.1080/00401706.1978.10489693 · Cited by 2,601
Self-citationA Use's Guide to Principal Components
Jackson, J. E. (1991) · Wiley Series in Probability and Statistics · doi:10.1002/0471725331 · Cited by 2,516
Orthogonal projections to latent structures (O‐PLS)
Trygg, J., & Wold, S. (2002) · Journal of Chemometrics · doi:10.1002/cem.695 · Cited by 2,447
Self-citationSIMPLS: An alternative approach to partial least squares regression
de Jong, S. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)85002-x · Cited by 1,905
Höskuldsson, A. (1988) · Journal of Chemometrics · doi:10.1002/cem.1180020306 · Cited by 1,881
Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat
Geladi, P., MacDougall, D., & Martens, H. (1985) · Applied Spectroscopy · doi:10.1366/0003702854248656 · Cited by 1,513
The use and interpretation of principal component analysis in applied research
Rao, C. R. (1964) · Cited by 1,139
Orthogonal signal correction of near-infrared spectra
Wold, S., Antti, H., Lindgren, F., & Öhman, J. (1998) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(98)00109-9 · Cited by 1,089
Self-citationMartens, H., & Næs, T. (1984) · Chemometrics · doi:10.1007/978-94-017-1026-8_5 · Cited by 597
Some recent developments in PLS modeling
Wold, S., Trygg, J., Berglund, A., & Antti, H. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00156-3 · Cited by 559
Self-citationDirect orthogonal signal correction
Westerhuis, J. A., de Jong, S., & Smilde, A. K. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00102-2 · Cited by 317
Sjöblom, J., Svensson, O., Josefson, M., Kullberg, H., & Wold, S. (1998) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(98)00112-9 · Cited by 296
Self-citationVariable and subset selection in PLS regression
Höskuldsson, A. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(00)00113-1 · Cited by 281
O2‐PLS for qualitative and quantitative analysis in multivariate calibration
Trygg, J. (2002) · Journal of Chemometrics · doi:10.1002/cem.724 · Cited by 238
Self-citationAn investigation of orthogonal signal correction algorithms and their characteristics
Svensson, O., Kourti, T., & MacGregor, J. F. (2002) · Journal of Chemometrics · doi:10.1002/cem.700 · Cited by 166
Product design through multivariate statistical analysis of process data
Jaeckle, C. M., & MacGregor, J. F. (1998) · AIChE Journal · doi:10.1002/aic.690440509 · Cited by 140
Kvalheim, O. M. (1992) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(92)80088-l · Cited by 32
Research works printed but not linked: 9
- 10.1016/S0169-7439(99)00045-3 · doi:10.1016/s0169-7439(99)00045-3
- 10.1016/S0003-2670(00)00890-4 · doi:10.1016/s0003-2670(00)00890-4
- Wold H, 1982, Systems under Indirect Observation, 1
- Wold H, 1966, Research Papers in Statistics, 441
- Höskuldsson A, 1996, Prediction Methods in Science and Technology
- 10.1002/cem.736 · doi:10.1002/cem.736
- 10.1016/S0169-7439(01)00110-1 · doi: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).
- 10.1016/S0169-7439(99)00058-1 · doi:10.1016/s0169-7439(99)00058-1
Cited by
Dumas, M.-E., Barton, R. H., Toye, A., Cloarec, O., Blancher, C., Rothwell, A., Fearnside, J., Tatoud, R., Blanc, V., Lindon, J. C., et al. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601056103 · Cited by 1,042
NMR-based metabolomic analysis of plants
Kim, H. K., Choi, Y. H., & Verpoorte, R. (2010) · Nature Protocols · doi:10.1038/nprot.2009.237 · Cited by 934
Cloarec, O., Dumas, M.-E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E., et al. (2005) · Analytical Chemistry · doi:10.1021/ac048630x · Cited by 912
Self-citationPinto-Sanchez, M. I., Hall, G. B., Ghajar, K., Nardelli, A., Bolino, C., Lau, J. T., Martin, F.-P., Cominetti, O., Welsh, C., Rieder, A., et al. (2017) · Gastroenterology · doi:10.1053/j.gastro.2017.05.003 · Cited by 821
Systems biology and multi-omics integration: viewpoints from the metabolomics research community
Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723
Metadata
- Authors
- Trygg, J., & Wold, S.
- Venue
- Journal of Chemometrics
- Publisher
- Wiley
- Published
- 2003-01
- Volume
- 17
- Issue
- 1
- Pages
- 53-64
- ISSN
- 0886-9383 (ISSN-L)
- 1099-128X
- OpenAlex
- W2123727902
- Cited by
- 339
- References
- 18
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
