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On the normalization method in two-dimensional correlation spectra when concentration is used as a perturbation parameter.

Z W Yu·I Noda
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Abstract

Data pretreatment is of importance in two-dimensional (2D) correlation analysis when composition is used as a perturbation parameter. For composition-oriented studies, different normalization methods based on both external parameters (i.e., concentration) and internal parameters (i.e., absorbance from individual components) have been compared. It was found that when there is no overlapping between absorption bands of interest, no normalization is needed for data pretreatment. When overlapped bands must be used for 2D correlation analysis, the mean-centered normalization method could be used to obtain correct signs in synchronous spectra for a transformation process in the specific form of A-->kC. The intensity of the 2D spectrum, however, may not accurately reflect quantitative information of the overall extent of spectral intensity variation observed during experiments.

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AlgorithmsComputer SimulationModels, ChemicalModels, MolecularSpectroscopy, Fourier Transform InfraredStatistics as TopicComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Z W Yu, I Noda. 2003. On the normalization method in two-dimensional correlation spectra when concentration is used as a perturbation parameter.. https://doi.org/10.1366/000370203321535079

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