It has been shown that the most common perturbations of conventional (one-dimensional) spectra such as random noise, baseline fluctuations, band position, and width changes may complicate two-dimensional (2D) correlation spectra, sometimes making them completely useless. In addition, two different physical causes may generate similar patterns for the synchronous and asynchronous spectra. Some of these effects, such as random noise and baseline fluctuations, can be eliminated from the input data, and one can recover the original appearance of 2D correlation spectra. The other effects, such as the frequency shift and bandwidth variation, cannot be removed from the experimental spectra. In this instance, the number and position of the correlation peaks can be elucidated by simulation studies. This report presents a few examples of typical patterns found in the synchronous and asynchronous spectra affected by those perturbations. Long streaks in 2D correlation spectra reveal extensive baseline fluctuations in the original data set. A simple offset often significantly reduces the extent of this effect. When no reasonable baseline correlations can be performed, the second derivative may solve this problem. In most cases, the perturbation-averaged spectrum is recommended as a reference. However, it has been proved that the calculation of 2D correlation spectra without any reference spectrum may also provide useful information, especially for data heavily influenced by noise or baseline fluctuations. In the majority of real-world systems, the spectral changes are a continuous function of applied perturbation. Thus, 2D correlation spectra yield information about the relative rate of intensity variations rather than the sequence of spectral events.

Simulation studies have demonstrated that the effect of a band shift may be completely removed from two-dimensional (2D) synchronous spectra if a shifting band simultaneously changes its intensity. In contrast, the corresponding asynchronous spectrum develops at least two peaks, even for a small shift coupled with an appreciable intensity variation. The separation between these peaks increases upon an increase in the bandwidth. If the spectral data are changing monotonically, the number and positions of the synchronous features can be readily determined from the difference between the first and the last spectrum in the series. The correlation spectrum calculated without the subtraction of reference spectrum, for a single band that shifts with constant intensity, is similar to that calculated without the subtraction of reference, for a band undergoing shift combined with significant intensity variations. The synchronous peaks resulting from the exponentially decaying intensity changes alone are at least 10 times more intense than the corresponding asynchronous peaks, whereas the analogous intensity ratio due to a moderate band shift is discernibly lower. This result proves that the asynchronous spectra are more sensitive to the band shift than the synchronous spectra. Also, the effect of noise is more apparent in the case of the asynchronous spectrum. The bandwidth variation alone generates noticeably weaker correlation intensity than that due to the band position or intensity changes. It has been shown that the asynchronous intensity strongly depends on the overall extent of the intensity changes at particular wavenumbers. As a result, the bands changing their intensities extensively but at similar rates may develop more intense asynchronicity than the bands with distinct difference in the rates of the intensity changes but smaller magnitude of these changes.