Two-dimensional correlation analysis (2DCOR) is a unique chemometric method introduced in spectroscopy which became successful in a wide range of analytical fields. It was applied in chromatography as well but has not gained wide-spread popularity. In our previous work, we introduced an alternative method, Alteration Analysis (ALA), which is built upon the basic properties of 2DCOR, but it is fine-tuned for chromatographic applications and can be used on higher dimensional data sets as well. We explored its merits through computer generated examples. In this study, we present the application of ALA to two various data-sets in chromatography. First, we used a series of samples where the concentrations of the compounds were adjusted according to the changes we studied in our previous in-silico experiments. We compared the alteration maps from the computer generated and measured sources. The results demonstrated that ALA can provide the same properties from measured data as laid down in theory. The second one is a test concerning the effect of sample solvent composition in supercritical fluid chromatography (SFC). ALA maps show the influence of increasing methanol concentration on the peak location and shape of compounds in the chromatogram. With these two examples, we demonstrate that ALA can be used not only in theory, but it has also practical potentials and importance.
Keywords: Alteration analysis; Chromatography; R-project; Two-dimensional correlation.
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