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Deconvolution of dust mixtures

NCJ Number
Forensic Science International Volume: 308 Dated: 2020
Date Published

This article proposes representing dust mixtures as vectors of counts of the individual particles, which can be characterized by any appropriate analytical technique.


Although 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. Although dust is ubiquitous, the use of dust evidence is anecdotal and is limited to cases where rare and characteristic particles are observed. The dust at any given location contains many 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, such as the scene of a crime. The method proposed in the current article enables the description of a dust mixture as a mixture of multinomial distributions over a fixed number of dust particle types. Using a latent Dirichlet allocation model, the method infers (a) the contributions of sites of interest to a dust mixture, and (b) the particle profiles associated with these sites. (publisher abstract modified)

Date Published: January 1, 2020