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Deconvolution of dust mixtures
تجزیه ترکیبات گرد و غبار-2020 While most evidence types considered by forensic scientists result from the interactions between
criminals, objects or victims at crime scenes, dust evidence arises from the mere presence of individuals
and objects at locations of interest. Dust is ubiquitous. Yet, the use of dust evidence is anecdotical and is
limited to cases where rare and characteristic particles are observed. The dust at any given location
contains a large number of particles from different types and the dust present on an object or individual
traveling across locations may be indicative of the locations recently visited by an individual, and, in
particular, of the presence of an individual at a particular site of interest, e.g., the scene of a crime.
In this paper, we propose to represent dust mixtures as vectors of counts of the individual particles,
which can be characterised by any appropriate analytical technique. This strategy enables us to describe a
dust mixture as a mixture of multinomial distributions over a fixed number of dust particle types. Using
a latent Dirichlet allocation model, we make inference on (a) the contributions of sites of interest to a dust
mixture, and (b) the particle profiles associated with these sites. Keywords: Dust particles | Chemical mixtures | Chemometrics | Latent Dirichlet allocation | Topic models | Trace evidence |
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