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.

Crosta, G., Pan, Y., Videen, G., Aptowicz, K., Chang, R. (2013). Discrimination of airborne material particles from light scattering (TAOS) patterns. In S.O. Southern (a cura di), SPIE Proceedings Vol. 8723 Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III. Bellingham, WA : SPIE [10.1117/12.2017969].

Discrimination of airborne material particles from light scattering (TAOS) patterns

CROSTA, GIOVANNI FRANCO FILIPPO;
2013

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.
Capitolo o saggio
Angle-resolved optical scattering; Machine learning; Airborne particle; Scattering pattern; Feature extraction; Linear classifier; Supervised training; Bacillus subtilis; Outdoor sampling
English
SPIE Proceedings Vol. 8723 Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III
Southern, SO
6-giu-2013
9780819495143
8723
SPIE
Crosta, G., Pan, Y., Videen, G., Aptowicz, K., Chang, R. (2013). Discrimination of airborne material particles from light scattering (TAOS) patterns. In S.O. Southern (a cura di), SPIE Proceedings Vol. 8723 Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III. Bellingham, WA : SPIE [10.1117/12.2017969].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/47972
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