  The spectral region around 10 micrometer, showing prominent dust emission
bands, is commonly used to derive the chemical composition of protoplanetary
dust. Different analysis methods have been proposed for this purpose, but so
far, no comparative test has been performed to test their validity. We
calculated model spectra of disk models with different geometries and central
sources, using a 2D radiative transfer code. These spectra were then fitted in
a blind test using four different spectral decomposition methods. We studied
the effect of disk structure (flared vs. flat), inclination angle, size of the
inner disk hole and stellar luminosity on the fitted chemical composition. Our
results show that the derived dust compositions by all methods deviate
systematically from the real chemical composition. Out of the four tested
spectral decomposition methods, our new two-layer temperature distribution
method, differs the least from the input dust composition and the results show
the weakest systematic effects. The reason for the deviations of the results
given by other methods lies in their simplifying assumptions (e.g. single
average grain temperature or one component continuum). We also tested the
influence of different noise levels on the results of the spectral
decomposition methods. We find that, for ground-based observations (8-13
micrometer) the expected uncertainty in the value of the crystallinity is about
11% for a signal-to-noise ratio of 100, while for space-based observations
(7-17 micrometer) the uncertainty is about 5%. On the basis of our results, we
propose a recipe for the analysis and interpretation of dust spectroscopy data
in the mid-infrared which should be especially valuable for analysing Spitzer
data and ground-based infrared spectroscopy data in the 10 micrometer window.
