Quantitative Classification of Two-Dimensional Correlation Spectra

Abstract

Two-dimensional (2D) correlation spectroscopy, which takes advantage of the apparent enhancement of spectral resolution, is known to be useful in qualitative discrimination of seemingly similar samples. The possibility of quantitative classification of 2D correlation spectra is even more desirable. Two useful parameters, namely Euclidian distance and correlation coefficient between 2D correlation spectra, are introduced for this purpose. Dry and sweet red wine samples are used to demonstrate the utility of these parameters. The distances between the 2D infrared (IR) spectra of sweet and dry red wines are roughly proportional to the differences of sugar contents in them. The result shows that the two parameters are useful measures for the quantitative evaluation of the similarity among the samples and their corresponding 2D correlation spectra.