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. 85Email:edmund.wel...@desy.de
D-22607 Hamburg Phone: +49 40 8998 4510
Germany Fax : +49 40 8998 2787
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