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

Deena Francis, Milan Laustsen, Eleftheria Dossi, Tuule Treiberg, Iona Hardy, Shai Hvid Shiv, Bo Svarrer Hansen, Jesper Mogensen, Mogens H. Jakobsen, Tommy Sonne Alstrøm

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 such as HMTD, TATP, and 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.