Recent advancement in the field of two-dimensional correlation spectroscopy

Quantitative classification of two-dimensional correlation spectra

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.

An insight into sequential order in two-dimensional correlation spectroscopy

Determination of the sequential order of events is a very important feature of generalized two-dimensional correlation spectroscopy (2D-COS). Recently, queries have been put forward on the actual effectiveness of this method when changes are non-periodic, particularly in the presence of local sequential order. Consequently, it brings an urgent necessity to understand the true connotation of the sequential order parameter derived from 2D-COS analysis. This article presents an attempt to address these questions based on the analysis of simulated spectra by assuming the band intensity changes in a logarithmic, exponential, hyperbolic, or polynomial manner. It is concluded that for two events changing monotonically and without local sequential orders, one event occurring prior to the other as determined using 2D-COS means in most cases that the former has both a shorter half-time and a greater half-intensity. As a rule of thumb, intensity versus the perturbation factor should be plotted before performing the 2D-COS analysis to determine the sequential order of the involved events. In the presence of obvious local sequential order, 2D-COS analysis is unnecessary. Otherwise, sequential order can be determined quite reliably based on 2D-COS analysis.