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This review is focused on the recent approaches to generalized 2D correlation spectroscopy, a technique widely used for the analysis of spectral data. A brief introduction of generalized 2D correlation spectroscopy is described first. Then the powerful combination of generalized 2D correlation spectroscopy and multivariate chemometircs techniques, such as the data reconstruction by principal component analysis (PCA), eigenvalue manipulation transformation (EMT), and self‐modeling curve resolution (SMCR) analysis are explored. Examples of successful applications of new approaches to generalized 2D correlation spectroscopy are highlighted.
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Jung et al. (2006) studied this question.
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