Journal ArticleDOI
Orthogonal projections to latent structures (O-PLS)
Johan Trygg,Svante Wold +1 more
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TLDR
In this article, a generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O-PLS), is described, which removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity).Abstract:
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.read more
Citations
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Journal ArticleDOI
Assessment of PLSDA cross validation
Johan A. Westerhuis,Huub C. J. Hoefsloot,Suzanne Smit,Daniel J. Vis,Age K. Smilde,Ewoud J. J. van Velzen,John P. M. van Duijnhoven,Ferdi A. van Dorsten +7 more
TL;DR: A strategy based on cross model validation and permutation testing to validate the classification models and advocate against the use of PLSDA score plots for inference of class differences is discussed.
Journal ArticleDOI
Chemometrics in Metabonomics
TL;DR: An overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies is provided, including the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS, and dynamic extensions thereof.
Journal ArticleDOI
OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification†
Max Bylesjö,Mattias Rantalainen,Olivier Cloarec,Jeremy K. Nicholson,Elaine Holmes,Johan Trygg +5 more
TL;DR: In this paper, class-orthogonal variation can be exploited to augment classificaiton analysis (OPLS-DA) for the purpose of discriminant analysis, and the OPLS method can be used to augment classification.
Journal ArticleDOI
Survey on data-driven industrial process monitoring and diagnosis
TL;DR: A state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades are provided to draw attention from the systems and control community and the process control community.
Journal ArticleDOI
Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models.
Susanne Wiklund,Erik Johansson,Lina Sjöström,Ewa J. Mellerowicz,Ulf Edlund,John P. Shockcor,Johan Gottfries,Thomas Moritz,Johan Trygg +8 more
TL;DR: The S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes, and an improved visualization and discrimination of interesting metabolites could be demonstrated.
References
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Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra
TL;DR: In this article, the standard normal variate (SNV) and de-trending (DT) approaches are applied to individual NIR diffuse reflectance spectra to remove the multiplicative interferences of scatter and particle size.
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Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
TL;DR: In this article, the rank estimation of the rank A of the matrix Y, i.e., the estimation of how much of the data y ik is signal and how much is noise, is considered.
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Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat
TL;DR: In this article, a multi-wavelength concept for optical correction (Multiplicative Scatter Correction, MSC) is proposed for separating the chemical light absorption from the physical light scatter.
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Orthogonal signal correction of near-infrared spectra
TL;DR: It is shown how a variant of PLS can be used to achieve a signal correction that is as close to orthogonal as possible to a given Y-vector or Y-matrix and is applied to four different data sets of multivariate calibration.