Raman microspectroscopic mapping as a tool for detection of gunshot residue on adhesive tape View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-11

AUTHORS

Justin Bueno, Lenka Halámková, Alexander Rzhevskii, Igor K. Lednev

ABSTRACT

Our research group previously reported a novel method for the detection of gunshot residue (GSR) via tape lifting combined with Raman microspectroscopic mapping and multivariate analysis. This initial study achieved proof of concept for this approach. Here, we report validation studies which investigate the reproducibility/ruggedness and specificity of the approach. Raman mapping for GSR detection on adhesive tape was performed on an independent Raman microscope, not used to generate the training set. These independent spectra were classified against the original training dataset using support vector machine discriminant analysis (SVM-DA). The resulting classification rates of 100% illustrate the reproducibility of the technique, its independence upon a specific instrument and provide an external validation for the approach. Additionally, the same procedure for GSR collection (tape lifting) was performed to collect samples from environmental sources, which could potentially provide false-positive assignments for current GSR analysis techniques. Thus, particles associated with automotive mechanics were collected. Automotive brake and tire materials are often composed of the heavy metals lead, barium, and antimony, which are the key elements targeted by current GSR detection technique. It was determined that Raman spectroscopic analysis was not susceptible to misclassifications from these samples. Results from these validation experiments illustrate the great potential of Raman microspectroscopic mapping used with tape lifting as a viable complimentary tool to current methodologies for GSR detection. Furthermore, current methodologies are not well-developed for automated organic GSR detection. Illustrated here, Raman microscoptrosocpic mapping has the potential for the automatic identification of organic GSR. Graphical abstract ᅟ. More... »

PAGES

7295-7303

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00216-018-1359-1

DOI

http://dx.doi.org/10.1007/s00216-018-1359-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107228329

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30255324


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