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Proceedings Paper

Discrimination of airborne material particles from light scattering (TAOS) patterns
Author(s): Giovanni F. Crosta; Yong-Le Pan; Gorden Videen; Kevin B. Aptowicz; Richard K. Chang
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Paper Abstract

Two-dimensional angle-resolved optical scattering (TAOS) is an experimental method which collects the intensity pattern of monochromatic light scattered by a single, micron-sized airborne particle. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. The solution proposed herewith relies on a learning machine (LM): rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified. The LM consists of two interacting modules: a feature extraction module and a linear classifier. Feature extraction relies on spectrum enhancement, which includes the discrete cosine Fourier transform and non-linear operations. Linear classification relies on multivariate statistical analysis. Interaction enables supervised training of the LM. The application described in this article aims at discriminating the TAOS patterns of single bacterial spores (Bacillus subtilis) from patterns of atmospheric aerosol and diesel soot particles. The latter are known to interfere with the detection of bacterial spores. Classification has been applied to a data set with more than 3000 TAOS patterns from various materials. Some classification experiments are described, where the size of training sets has been varied as well as many other parameters which control the classifier. By assuming all training and recognition patterns to come from the respective reference materials only, the most satisfactory classification result corresponds to ≈ 20% false negatives from Bacillus subtilis particles and ≤ 11% false positives from environmental and diesel particles.

Paper Details

Date Published: 29 May 2013
PDF: 12 pages
Proc. SPIE 8723, Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III, 872318 (29 May 2013); doi: 10.1117/12.2017969
Show Author Affiliations
Giovanni F. Crosta, Univ. degli Studi di Milano-Bicocca (Italy)
Yong-Le Pan, U.S. Army Research Lab. (United States)
Gorden Videen, U.S. Army Research Lab. (United States)
Kevin B. Aptowicz, West Chester Univ. of Pennsylvania (United States)
Richard K. Chang, Yale Univ. (United States)

Published in SPIE Proceedings Vol. 8723:
Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III
Šárka O. Southern, Editor(s)

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