TY - JOUR
T1 - Machine learning improves trace explosive selectivity
T2 - Application to nitrate-based explosives
AU - Zeiri, Yehuda
AU - Fisher, Danny
AU - Lukow, Stefan R.
AU - Berezutskiy, Gennadiy
AU - Gil, Itai
AU - Levy, Tal
N1 - Publisher Copyright:
© 2020 American Chemical Society
PY - 2020/11/19
Y1 - 2020/11/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85096456860&partnerID=8YFLogxK
U2 - 10.1021/acs.jpca.0c05909
DO - 10.1021/acs.jpca.0c05909
M3 - Article
C2 - 33156629
AN - SCOPUS:85096456860
SN - 1089-5639
VL - 124
SP - 9656
EP - 9664
JO - Journal of Physical Chemistry A
JF - Journal of Physical Chemistry A
IS - 46
ER -