Quantitative profile–profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR)

Author:

Gallidabino Matteo D.12345ORCID,Barron Leon P.678910ORCID,Weyermann Céline1112131415ORCID,Romolo Francesco S.1617181920ORCID

Affiliation:

1. Centre for Forensic Science

2. Department of Applied Sciences

3. Faculty of Health & Life Sciences

4. Northumbria University Newcastle

5. NE1 8ST Newcastle Upon Tyne

6. King's Forensics

7. Division of Analytical

8. Environmental & Forensic Sciences

9. Faculty of Life Sciences and Medicine

10. King's College London

11. École des Sciences Criminelles

12. Faculté de Droit

13. des Sciences Criminelles et d'Administration Publique

14. Université de Lausanne

15. 1015 Lausanne-Dorigny

16. Sezione di Medicina Legale

17. Dipartimento SAIMLAL

18. Sapienza Università di Roma

19. 00161 Rome

20. Italy

Abstract

Possibility to associate gunshot residues with other residues or their original ammunition has been demonstrated using a novel predictive approach.

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrochemistry,Spectroscopy,Environmental Chemistry,Biochemistry,Analytical Chemistry

Reference44 articles.

1. K. Inman and N.Rudin , Principles and practices of criminalistics , CRC Press , Boca Raton, USA , 2001

2. M. G. Haag and L. C.Haag , Shooting incident reconstruction , Academic Press , San Diego, USA , 2nd edn, 2011

3. B. J. Heard , Handbook of firearms and ballistics , John Wiley & Sons , Hoboken, USA , 2nd edn, 2008

4. J. S. Wallace , Chemical analysis of firearms, ammunition, and gunshot residue , CRC Press , Boca Raton, USA , 2008

5. Identification of gunshot residue: a critical review

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