Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning
dc.contributor.author | Mølgaard, Lasse L. | |
dc.contributor.author | Buus, Ole T. | |
dc.contributor.author | Larsen, Jan | |
dc.contributor.author | Babamoradi, Hamid | |
dc.contributor.author | Thygesen, Ida L. | |
dc.contributor.author | Laustsen, Milan | |
dc.contributor.author | Munk, Jens Kristian | |
dc.contributor.author | Dossi, Eleftheria | |
dc.contributor.author | O'Keeffe, Caroline | |
dc.contributor.author | Lässig, Lina | |
dc.contributor.author | Tatlow, Sol | |
dc.contributor.author | Sandström, Lars | |
dc.contributor.author | Jakobsen, Mogens H. | |
dc.date.accessioned | 2017-05-23T10:10:10Z | |
dc.date.available | 2017-05-23T10:10:10Z | |
dc.date.issued | 2017-05 | |
dc.description.abstract | 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 fullyintegrated 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 traf- ficking of illegal drugs, explosive detection, or in other law enforcement applications. | en_UK |
dc.description.sponsorship | EU FP7 Grant Agreement Number 313202 | en_UK |
dc.identifier.citation | Molgaard LL, Buus OT, Larsen J, et al., (2017) Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning. In: Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XVIII, SPIE Defense + Security, 2017, 9-13 April 2017, Anaheim, California, USA | en_UK |
dc.identifier.uri | ||
dc.identifier.uri | https://doi.org/10.1117/12.2262468 | |
dc.identifier.uri | http://dspace.lib.cranfield.ac.uk/handle/1826/11918 | |
dc.publisher | SPIE | en_UK |
dc.rights | Attribution-NonCommercial 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
dc.title | Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning | en_UK |
dc.type | Conference paper | en_UK |
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