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Forensic Sci Int. 2001 Jun 15;119(2):195-211.

Identification of gunshot residue: a critical review.

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  • 1Head, Gunshot Residue Analysis Laboratory, Direzione Centrale Polizia Criminale, Servizio Polizia Scientifica, Viale dell'Aeronautica 7, 00144 Rome, Italy.


A review of the scientific papers published on inorganic gunshot residue (GSR) analysis permits to study how the particle analysis has shown its capability in detection and identification of gunshot residue. The scanning electron microscope can be the most powerful tool for forensic scientists to determine the proximity to a discharging firearm and/or the contact with a surface exposed to GSR. Particle analysis can identify individual gunshot residue particles through both morphological and elemental characteristics. When particles are detected on the collected sample, the analytical results can be interpreted following rules of a formal general interpretative system, to determine whether they come from the explosion of a primer or from other possible sources. The particles on the sample are compared with an abstract idea of "unique" GSR particle produced by the sole source of the explosion of a primer. "Uniqueness" is not the only problem related to GSR detection and identification for a forensic scientist. With "not-unique" particles interpretation of results is extremely important. The evidential strength of "not-unique" particles can increase with a more fruitful interpretative framework based on Bayes rule. For the assessment of the value of a GSR in linking a suspect and a crime, it is important to compare two hypothesis: the first can be that of the evidence if the suspect has been shooting in a specific situation, the second that of the evidence if the suspect was not involved in this shooting. This case specific or case-by-case approach is closer to what the court is interested in. The authors consider that a "case-by-case" approach should be followed whenever possible. Research of models and data such as those developed in other trace evidence material (fibres, glass, etc.) using a Bayesian approach is suggested in the interpretation of GSR.

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