Two-Way Data Analysis: Multivariate Curve Resolution, Iterative Methods
This article describes the general modus operandi of model-free Multivariate Curve Resolution iterative methods, i.e., the recovery of pure concentration profiles and responses (spectra) from the iterative optimization of initial estimates under the action of constraints. The basic bilinear curve resolution model is expressed in two different forms, as: (1) or (2). Methods based on Eq. (1), such as iterative target transformation factor analysis (ITTFA) and multivariate curve resolution-alternating least squares (MCR-ALS), solve for the C and/or ST matrices directly, whereas methods based on Eq. (2), such as Resolving Factor Analysis (RFA) and the resolution of matrices through elementary matrix transformations (Gentle) optimize the transformation matrix R in such a way that are chemically meaningful. All these methods are described but, since MCR-ALS is the method that has evolved more in time, explanations about advances specifically linked to the use of this algorithm are explained in more detail.
| Main Authors: | , , |
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| Other Authors: | |
| Format: | capítulo de libro biblioteca |
| Language: | English |
| Published: |
Elsevier
2019
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| Subjects: | Constraints, Elementary matrix transformation, Iterative target transformation factor analysis (ITTFA), Multivariate curve resolution (MCR), Multivariate curve resolution-alternating least squares (MCR-ALS), |
| Online Access: | http://hdl.handle.net/10261/229047 |
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