We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control trafficking of illegal drugs, explosive detection, or in other law enforcement applications.
Lasse L. Mølgaard, Ole T. Buus, Jan Larsen, Hamid Babamoradi, Ida L. Thygesen, Milan Laustsen, Jens Kristian Munk, Eleftheria Dossi, Caroline O'Keeffe, Lina Lässig, Sol Tatlow, Lars Sandström, and Mogens H. Jakobsen, "Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning," Proc. SPIE 10183, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XVIII, 1018307 (Presented at SPIE Defense + Security: April 11, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2262468.
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