Machine learning improves trace explosive selectivity: Application to nitrate-based explosives

Yehuda Zeiri, Danny Fisher, Stefan R. Lukow, Gennadiy Berezutskiy, Itai Gil, Tal Levy

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Ion mobility spectrometry (IMS) is the method of choice to detect trace amounts of explosives in most airports and border crossing settings. For most explosives, the IMS detection limits are suitably low enough to meet security requirements. However, for some explosive families, the selectivity is not sufficient. One such family is nitrate-based explosives, where discrimination between various nitrate threats and ambient nitrates is challenging. Using a small database, machine learning methods were utilized to examine the extent of improvement in IMS selectivity for detection of nitrate-based explosives. Five classes were considered in this preliminary study: ammonium nitrate (AN), an ∼95:5 mixture of AN and fuel oil (ANFO), urea nitrate (UN), nitrate due to environmental pollution, and samples that did not contain any explosive (blanks). The preliminary results clearly show that the incorporation of machine learning methods can lead to a significant improvement in IMS selectivity.

Original languageEnglish
Pages (from-to)9656-9664
Number of pages9
JournalJournal of Physical Chemistry A
Volume124
Issue number46
DOIs
StatePublished - 19 Nov 2020

Fingerprint

Dive into the research topics of 'Machine learning improves trace explosive selectivity: Application to nitrate-based explosives'. Together they form a unique fingerprint.

Cite this