Interpretation of two-dimensional correlation spectra: Science or art?

It has been shown that the most common perturbations of conventional tone-d imensional) spectra such as random noise, baseline fluctuations, band posit ion, and width changes may complicate two-dimensional (2D) correlation spec tra, sometimes making them completely useless. In addition, two different p hysical causes may generate similar patterns for the synchronous and asynch ronous spectra. Some of these effects, such as random noise and baseline fl uctuations, can be eliminated from the input data, and one can recover the original appearance of 2D correlation spectra. The other effects, such as t he frequency shift and bandwidth variation, cannot be removed from the expe rimental spectra. In this instance, the number and position of the correlat ion peaks can be elucidated by simulation studies. This report presents a f ew examples of typical patterns found in the synchronous and asynchronous s pectra affected by those perturbations. Long streaks in 2D correlation spec tra reveal extensive baseline fluctuations in the original data set. A simp le offset often significantly reduces the extent of this effect. When no re asonable baseline correlations can be performed, the second derivative may solve this problem. In most cases, the perturbation-average spectrum is rec ommended as a reference. However, it has been proved that the calculation o f 2D correlation spectra without any reference spectrum may also provide us eful information, especially for data heavily influenced by noise or baseli ne fluctuations. In the majority of real-world systems, the spectral change s are a continuous function of applied perturbation. Thus, 2D correlation s pectra yield information about the relative rate of intensity variations ra ther than the sequence of spectral events.