Machine learning methods for the detection of explosives, drugs and precursor chemicals gathered using a colorimetric sniffer sensor

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dc.contributor.authorFrancis, Deena P.
dc.contributor.authorLaustsen, Milan
dc.contributor.authorDossi, Eleftheria
dc.contributor.authorTreiberg, Tuule
dc.contributor.authorHardy, Iona
dc.contributor.authorShiv, Shai Hvid
dc.contributor.authorHansen, Bo Svarrer
dc.contributor.authorMogensen, Jesper
dc.contributor.authorJakobsen, Mogens H.
dc.contributor.authorAlstrøm, Tommy S.
dc.date.accessioned2023-05-10T09:34:00Z
dc.date.available2023-05-10T09:34:00Z
dc.date.issued2023-04-18
dc.description.abstractColorimetric sensing technology for the detection of explosives, drugs, and their precursor chemicals is an important and effective approach. In this work, we use various machine learning models to detect these substances from colorimetric sensing experiments conducted in controlled environments. The detection experiments based on the response of a colorimetric chip containing 26 chemo-responsive dyes indicate that homemade explosives (HMEs) such as hexamethylene triperoxide diamine (HMTD), triacetone triperoxide (TATP), and methyl ethyl ketone peroxide (MEKP) used in improvised explosives devices are detected with true positive rate (TPR) of 70–75%, 73–90% and 60–82% respectively. Time series classifiers such as Convolutional Neural Networks (CNN) are explored, and the results indicate that improvements can be achieved with the use of kinetics of the chemical responses. The use of CNNs is limited, however, to scenarios where a large number of measurements, typically in the range of a few hundred, of each analyte are available. Feature selection of important dyes using the Group Lasso (GPLASSO) algorithm indicated that certain dyes are more important in discrimination of an analyte from ambient air. This information could be used for optimizing the colorimetric sensor and extend the detection to more analytes.en_UK
dc.identifier.citationFrancis DP, Laustsen M, Dossi E, et al., (2023) Machine learning methods for the detection of explosives, drugs and precursor chemicals gathered using a colorimetric sniffer sensor. Analytical Methods, Volume 15, Issue 19, May 2023, pp. 2343-2354en_UK
dc.identifier.issn1759-9660
dc.identifier.urihttps://doi.org/10.1039/D3AY00247K
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19618
dc.language.isoenen_UK
dc.publisherRoyal Society of Chemistryen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleMachine learning methods for the detection of explosives, drugs and precursor chemicals gathered using a colorimetric sniffer sensoren_UK
dc.typeArticleen_UK

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