Noise reduction methods applied to two-dimensional correlation spectroscopy (2D-COS) reveal complementary benefits of pre- and post-treatment

Two-dimensional correlation spectroscopy (2D-COS) is a powerful spectral analysis technique widely used in many fields of spectroscopy because it can reveal spectral information in complex systems that is not readily evident in the original spectral data alone. However, noise may severely distort the information and thus limit the technique's usefulness. Consequently, noise reduction is often performed before implementing 2D-COS. In general, this is implemented using one-dimensional (1D) methods applied to the individual input spectra, but, because 2D-COS is based on sets of successive spectra and produces 2D outputs, there is also scope for the utilization of 2D noise-reduction methods. Furthermore, 2D noise reduction can be applied either to the original set of spectra before performing 2D-COS ("pretreatment") or on the 2D-COS output ("post-treatment"). Very little work has been done on post-treatment; hence, the relative advantages of these two approaches are unclear. In this work we compare the noise-reduction performance on 2D-COS of pretreatment and post-treatment using 1D (wavelets) and 2D algorithms (wavelets, matrix maximum entropy). The 2D methods generally outperformed the 1D method in pretreatment noise reduction. 2D post-treatment in some cases was superior to pretreatment and, unexpectedly, also provided correlation coefficient maps that were similar to 2D correlation spectroscopy maps but with apparent better contrast.