Issue 19, 2023

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

Abstract

Colorimetric 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.

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

Supplementary files

Article information

Article type
Paper
Submitted
16 فرؤری 2023
Accepted
17 اپریل 2023
First published
18 اپریل 2023

Anal. Methods, 2023,15, 2343-2354

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

D. P. Francis, M. Laustsen, E. Dossi, T. Treiberg, I. Hardy, S. H. Shiv, B. S. Hansen, J. Mogensen, M. H. Jakobsen and T. S. Alstrøm, Anal. Methods, 2023, 15, 2343 DOI: 10.1039/D3AY00247K

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