Resolution with constrained optimization, good

Figure 11. Good resolution with constrained optimization and addition of a paracrystalline peak. Corrected equatorial trace of a polypropylene fiber specimen cold drawn X-5.5. Total theoretical trace now comprised of realistic crystalline peaks 110, 040, 130, 111, 131, 041, and paracrystalline peak at 15.5°. Compare...

Peak resolution is usually easier if well chosen background parameters are input and if constrained optimization methods are utilised. Misleading results can be obtained if the constraints are too limited and tests with unconstrained optimization are desirable if at all possible. In particular, the possible presence of paracrystalline or intermediate phase peaks must be tested with extreme care in order to avoid ambiguity. It is not sufficient to have a good mathematical resolution alone, all peaks must be significant in crystallographic or structural terms. The incidental measurement of peak-area crystallinity is considered to be of secondary importance to the resolution of overlapping peaks. 

Exploration of a data set before resolution is a golden rule fully applicable to image analysis. In this context, there are two important domains of information in the data set the spectral domain and the spatial domain. Using a method for the selection of pure variables like SIMPLISMA [53], we can select the pixels with the most dissimilar spectra. As in the resolution of other types of data sets, these spectra are good initial estimates to start the constrained optimization of matrices C and ST. The spatial dimension of an image is what makes these types of measurement different from other chemical data sets, since it provides local information about the sample through pixel-to-pixel spectral variations. This local character can be exploited with chemometric tools based on local-rank analysis, like FSMW-EFA [30, 31], explained in Section 11.3. 

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