Frequently Asked Questions (FAQ)
Can the mat2corr toolbox account for unevenly spaced sampling of spectra?
Unfortunately, the current version of the mat2dcorr toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.
In the literature, there are basically three different ways to deal with a perturbation that does not fulfil the equidistance condition:
- 1. Ignore the requirement for equidistant perturbation values and use the data as they are. This is what the mat2dcorr toolbox v.1.05 does.
- 2. Use modified correlation equations as described in Noda (2003) to account for uneven sampling of the perturbation variable.
- 3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.
Option 3 would be fairly easy to implement programmatically. It is already on the to-do list.
I have loaded spectral data, but the buttons are still grayed out?
This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox. For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as x- and a second time as y-data set. Only then will the buttons be available for analysis.
Links to other non-commercial 2D-COS software solutions
- 2DShige, free software for two-dimensional correlation analysis developed by Prof. Shigeaki Morita (Osaka Electro-Communication University).
- MIDAS 2010, Matlab-based software tools developed in the Canadian Light Source for 2D spectroscopic analysis and data exploration of time resolved infrared spectra
- corr2D (R), - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.
(to be continued)