This is where some sort of automatic classifier or dimensional reducer would come in handy. You would first have to have auto-reduction into some standard form like k^n*chi(k) (EXAFS) or pre-edge-subtracted, post-edge nornalized (XANES). After that, you might use PCA or some other such tool to express each spectrum as a point in some high-dimensional space, then find a projection in that space that allows you to see interesting features. For instance, spectra along a reaction sequence might plot out as a 1D curve twisting through a higher-dimenionsal space. I've done something like that for XANES spectra of inhomogeneous samples, identifying clusters of 'alike' spectra. Projection pursuit methods might be a way to go for finding 'interesting' projections. mam On 4/11/2012 6:51 AM, Edmund Welter wrote:
Dear XAFS users
recently I read several mails on this list which were dealing with the problem of large data sets, as they are produced by Q-EXAFS scans or by dispersive XAFS. The question was if there are tools available to handle data sets of several 1000 spectra and perform a linear combination fit or even a full EXAFS evaluation on each of them. Evaluating 3000 spectra is a heroic attempt, but I wonder if it is also economical.
In most (that means not in ALL!) cases, the vast majority of these spectra is boring, because the spectrum with the number X looks exactly like the spectrum with the number X-1 looked and how the spectrum with the number X+1 will look and so on. Evaluating all these (basically identical) spectra is in principle a waste of time and working memory. The interesting spectra are those which were measured when something was happening in the sample. Since we do not always know at which time, or temperature or reactant concentration etc. interesting things will happen it is without any doubt justified to measure x-thousand spectra, but after that we should use a more sophisticated approach than brute force.
I think that it would be much more useful to find procedures (that means develop computer programs) that search for the (usually relatively small number of) interesting spectra. The most obvious parameter is how similar is a particular spectrum to the spectra measured before and after. The next step would probably be to identify clusters of related spectra using statistical methods. This is a problem which had to be solved in other areas like the automated analysis of images before and should also be possible with our kind of data.
Anyway, how to handle thousands of XAFS spectra will become a very important problem in the future. With all these beamlines that provide 10^12 photons per second we can measure a factor 100 – 1000 faster than we did with 10^9 photons per second. So, I wonder if anything beyond the brute force approach is going on in the EXAFS software universe to make effective and economical use of the measured data.
Best regards, Edmund Welter
-- -------------------------------------------------------- Dr. Edmund Welter Deutsches Elektronen-Synchrotron DESY FS-Do
Notkestr. 85 Email:edmund.welter@desy.de D-22607 Hamburg Phone: +49 40 8998 4510 Germany Fax : +49 40 8998 2787 --------------------------------------------------------
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