A support vector machine approach to the automatic identification of fluorescence spectra emitted by biological agents

M. Gelfusa; A. Murari; M. Lungaroni; A. Malizia; S. Parracino; E. Peluso; O. Cenciarelli; M. Carestia; R. Pizzoferrato; J. Vega; P. Gaudio

doi:10.1117/12.2241164

24 October 2016 A support vector machine approach to the automatic identification of fluorescence spectra emitted by biological agents

M. Gelfusa, A. Murari, M. Lungaroni, A. Malizia, S. Parracino, E. Peluso, O. Cenciarelli, M. Carestia, R. Pizzoferrato, J. Vega, P. Gaudio

Author Affiliations +

Proceedings Volume 9995, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII; 99950X (2016) https://doi.org/10.1117/12.2241164
Event: SPIE Security + Defence, 2016, Edinburgh, United Kingdom

Abstract

Two of the major new concerns of modern societies are biosecurity and biosafety. Several biological agents (BAs) such as toxins, bacteria, viruses, fungi and parasites are able to cause damage to living systems either humans, animals or plants. Optical techniques, in particular LIght Detection And Ranging (LIDAR), based on the transmission of laser pulses and analysis of the return signals, can be successfully applied to monitoring the release of biological agents into the atmosphere.

It is well known that most of biological agents tend to emit specific fluorescence spectra, which in principle allow their detection and identification, if excited by light of the appropriate wavelength. For these reasons, the detection of the UVLight Induced Fluorescence (UV-LIF) emitted by BAs is particularly promising. On the other hand, the stand-off detection of BAs poses a series of challenging issues; one of the most severe is the automatic discrimination between various agents which emit very similar fluorescence spectra.

In this paper, a new data analysis method, based on a combination of advanced filtering techniques and Support Vector Machines, is described. The proposed approach covers all the aspects of the data analysis process, from filtering and denoising to automatic recognition of the agents. A systematic series of numerical tests has been performed to assess the potential and limits of the proposed methodology. The first investigations of experimental data have already given very encouraging results.

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M. Gelfusa, A. Murari, M. Lungaroni, A. Malizia, S. Parracino, E. Peluso, O. Cenciarelli, M. Carestia, R. Pizzoferrato, J. Vega, and P. Gaudio "A support vector machine approach to the automatic identification of fluorescence spectra emitted by biological agents", Proc. SPIE 9995, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII, 99950X (24 October 2016); https://doi.org/10.1117/12.2241164

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