Examination of Firearms Review: 2007 to 2010 - Interpol

arium - no antimony. This precludes true three-component characteristic
particles from originating from the small-calibre cartridges used in this type of Hilti
nail-guns.
Berk (119) addresses yet another non-ballistic source of GSR-like particles: car
airbag inflation systems. Airbags may use primer materials in the deployment
system, which may give rise to characteristic three-component GSR particles
being emitted when the bag is ruptured. However, because materials are used in
the airbag mechanisms that are normally not found in a firearm, the "GSR"
particles will most of the time contain from trace amounts to large quantities of
elements normally not encountered in true GSR particles. Moreover, because of
the other materials present in an airbag, the overall population of particles on the
samples will to a large extent be composed of GSR-inconsistent particles, giving
away the non-ballistic origin of the particle deposits. The author concludes that
the peculiar composition of these particles, both individually and as a population,
ensures that it is normally possible to distinguish this population from true GSR -
especially if the analyst is made aware that airbag material may have been
involved in the case.
3.1.4 Interpretation of analysis results and the application of Bayesian principles
In a number of forensic disciplines, like DNA, fibres and mark-evidence, the
application of Bayes’ Law for interpreting the analysis results in the context of the
case at hand is already well established. Now, also in the GSR field voices are
speaking up to implement these principles to give an added value to the purely
analytical report that is often emitted after the analysis of GSR samples is
completed. Biedermann et al. (120) offer a rigorous discussion of the principles
involved and their implementation in the GSR field, based on statistical models
developed by Cardinetti et al. (121). They also show how Bayesian Networks, a
mathematical modelling technique originating from artificial intelligence research,
may be used to model the several factors involved in the calculation of the
likelihood ratio. The authors conclude that the Bayesian Law and Bayesian
Networks serve as powerful tools to put the GSR analysis results in the
perspective of the case context. The problem remains, however, that the
numerical values to be assigned to the different parameters in the model need to
be estimated and extracted from experimental data – an area of research that is
to be further developed before these models can be reliably put to practical use.
In this context a number of publications have appeared that further this part of
development.
Brożek-Mucha compared the difference in composition and morphology between
airborne GSR and particles inside the cartridge case (122). In this study the
composition and morphology of GSR particles originating from four brands of
ammunition was compared. They represent three types of primer mixtures
containing either mercury fulminate, lead styphnate or azide and an all-organic
detonator. It is observed that GSR particles formed inside the case have not only
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