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An investigation of orthogonal signal correction algorithms and their characteristics
Abstract
Six different algorithms for orthogonal signal correction (OSC) are studied and compared both from an algorithmic point of view and from a prediction and analysis point of view. The algorithms have appeared under the names OSC (three alternative algorithms), direct orthogonalization (DO) and orthogonal projection to latent structures (OPLS). These algorithms can be divided into two groups. The first group has the ability to reduce the number of PLS components in the calibration models significantly by removing only one orthogonal component. The second group reduces the complexity of the calibration model by one PLS component for each orthogonal component removed. The methods are evaluated and compared using both simulated and real calibration data sets. In some cases the OSC algorithms can have quite different behaviors, such as when non‐linearities are present. However, in all cases we have studied, none of the OSC algorithms provided a significant improvement in the calibration models over using PLS on the raw data. The main advantage with OSC may lie in the possibly easier interpretation and understanding from the analysis of corrected data. Analysis of the orthogonal information removed with OSC might also be beneficial. Copyright © 2002 John Wiley & Sons, Ltd.
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Cited by
Transfer of multivariate calibration models: a review
Feudale, R. N., Woody, N. A., Tan, H., Myles, A. J., Brown, S. D., & Ferré, J. (2002) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(02)00085-0 · Cited by 555
O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter
Trygg, J., & Wold, S. (2003) · Journal of Chemometrics · doi:10.1002/cem.775 · Cited by 339
Resting-state cortical connectivity predicts motor skill acquisition
Wu, J., Srinivasan, R., Kaur, A., & Cramer, S. C. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.01.026 · Cited by 150
“As with previous studies that used PLS (Esteban-Diez et al., 2004; Krishnan et al., 2013; Svensson et al., 2002; Williams et al., 2009), this step allowed for more efficient PLS models with fewer components.”
Noto, A., Fanos, V., Barberini, L., Grapov, D., Fattuoni, C., Zaffanello, M., Casanova, A., Fenu, G., De Giacomo, A., De Angelis, M., et al. (2014) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.3109/14767058.2014.954784 · Cited by 113
Tong, W., Chen, G., Yu, H., Wang, X., Xue, R., Yang, Y., Luo, H., & Huang, D. (2024) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2024.106355 · Cited by 24
Metadata
- Authors
- Svensson, O., Kourti, T., & MacGregor, J. F.
- Venue
- Journal of Chemometrics
- Publisher
- Wiley
- Published
- 2002-02-07
- Volume
- 16
- Issue
- 4
- Pages
- 176-188
- ISSN
- 0886-9383 (ISSN-L)
- 1099-128X
- OpenAlex
- W2049154504
- Cited by
- 166
- References
- 0
- FWCI
- 14.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Svensson, O., Kourti, T., & MacGregor, J. F. (2002). An investigation of orthogonal signal correction algorithms and their characteristics. Journal of Chemometrics. https://doi.org/10.1002/cem.700
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