Share Email Print

Proceedings Paper

Multispectral light scattering imaging and multivariate analysis of airborne particulates
Author(s): Stephen Holler; Charles R. Skelsey; Stephen D. Fuerstenau
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Light scattering patterns from non-spherical particles and aggregates exhibit complex structure that is only revealed when observing in two angular dimensions. However, due to the varied shape and packing of such aerosols, the rich structure in the two-dimensional angular optical scattering (TAOS) pattern varies from particle to particle. We examine two-dimensional light scattering patterns obtained at multiple wavelengths using a single CCD camera with minimal cross talk between channels. The integration of the approach with a single CCD camera assures that data is acquired within the same solid angle and orientation. Since the optical size of the scattering particle is inversely proportional to the illuminating wavelength, the spectrally resolved scattering information provides characteristic information about the airborne particles simultaneously in two different scaling regimes. The simultaneous acquisition of data from airborne particulate matter at two different wavelengths allows for additional degrees of freedom in the analysis and characterization of the aerosols. Whereas our previous multivariate analyses of aerosol particles has relied solely on spatial frequency components, our present approach attempts to incorporate the relative symmetry of the particledetector system while extracting information content from both spectral channels. In addition to single channel data, this current approach also examines relative metrics. Consequently, we have begun to employ multivariate techniques based on novel morphological descriptors in order to classify “unknown” particles within a database of TAOS patterns from known aerosols utilizing both spectral and spatial information acquired. A comparison is made among several different classification metrics, all of which show improved classification capabilities relative to our previous approaches.

Paper Details

Date Published: 13 May 2015
PDF: 8 pages
Proc. SPIE 9486, Advanced Environmental, Chemical, and Biological Sensing Technologies XII, 94860O (13 May 2015); doi: 10.1117/12.2177581
Show Author Affiliations
Stephen Holler, Fordham Univ. (United States)
Charles R. Skelsey, Fordham Univ. (United States)
Stephen D. Fuerstenau, SDF Consulting (United States)

Published in SPIE Proceedings Vol. 9486:
Advanced Environmental, Chemical, and Biological Sensing Technologies XII
Tuan Vo-Dinh; Robert A. Lieberman; Günter G. Gauglitz, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?