Crossmark

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

Crossref DOI link: https://doi.org/10.1039/C8AN01841C

Published Online: 2019

Update policy: https://doi.org/10.1039/rsc_crossmark_policy

Authors

Gallidabino, Matteo D. https://orcid.org/0000-0001-7871-9793
Barron, Leon P. https://orcid.org/0000-0001-5986-3853
Weyermann, Céline https://orcid.org/0000-0002-3533-3919
Romolo, Francesco S. https://orcid.org/0000-0002-5334-0960
Funding

Funding for this research was provided by:

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (P2LAP1_164912)
License Information

Version of Record valid from 2018-11-02
More Information

SIMILARITY CHECK

: This document is Similarity Check deposited

RELATED DATA

: Supplementary Information

IDENTIFIERS

: Matteo D. Gallidabino (ORCID)

: Matteo D. Gallidabino (ResearcherID)

: Leon P. Barron (ORCID)

: Céline Weyermann (ORCID)

: Céline Weyermann (ResearcherID)

: Francesco S. Romolo (ORCID)

: Francesco S. Romolo (ResearcherID)

PEER REVIEW METHOD

: Single-blind

PUBLICATION HISTORY

: Received 23 September 2018; Accepted 2 November 2018; Accepted Manuscript published 2 November 2018; Advance Article published 26 November 2018; Version of Record published 11 February 2019

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