Fluorescence spectra of atmospheric aerosol particles measured using one or two excitation wavelengths: comparison of classification schemes employing different emission and scattering results.

An improved Dual-wavelength-excitation Particle Fluorescence Spectrometer (DPFS) has been reported. It measures two fluorescence spectra excited sequentially by lasers at 263 nm and 351 nm, from single atmospheric aerosol particles in the 1-10 mum diameter size range. Here we investigate the different levels of discrimination capability obtained when different numbers of excitation and fluorescence-emission wavelengths are used for analysis. We a) use the DPFS to measure fluorescence spectra of Bacillus subtilis and other aerosol particles, and a 25-hour sample of atmospheric aerosol at an urban site in Maryland, USA; b) analyze the data using six different algorithms that employ different levels of detail of the measured data; and c) show that when more of the data measured by the DPFS is used, the ability to discriminate among particle types is significantly increased.

Infrared differential-absorption Mueller matrix spectroscopy and neural network-based data fusion for biological aerosol standoff detection.

An active spectrophotopolarimeter sensor and support system were developed for a military/civilian defense feasibility study concerning the identification and standoff detection of biological aerosols. Plumes of warfare agent surrogates gamma-irradiated Bacillus subtilis and chicken egg white albumen (analytes), Arizona road dust (terrestrial interferent), water mist (atmospheric interferent), and talcum powders (experiment controls) were dispersed inside windowless chambers and interrogated by multiple CO(2) laser beams spanning 9.1-12.0 microm wavelengths (lambda). Molecular vibration and vibration-rotation activities by the subject analyte are fundamentally strong within this "fingerprint" middle infrared spectral region. Distinct polarization-modulations of incident irradiance and backscatter radiance of tuned beams generate the Mueller matrix (M) of subject aerosol. Strings of all 15 normalized elements {M(ij)(lambda)/M(11)(lambda)}, which completely describe physical and geometric attributes of the aerosol particles, are input fields for training hybrid Kohonen self-organizing map feed-forward artificial neural networks (ANNs). The properly trained and validated ANN model performs pattern recognition and type-classification tasks via internal mappings. A typical ANN that mathematically clusters analyte, interferent, and control aerosols with nil overlap of species is illustrated, including sensitivity analysis of performance.

Bioaerosol analysis with Raman chemical imaging microspectroscopy.

Raman chemical imaging microspectroscopy is evaluated as a technology for waterborne pathogen and bioaerosol detection. Raman imaging produces a three-dimensional data cube consisting of a Raman spectrum at every pixel in a microscope field of view. Binary and ternary mixtures including combinations of polystyrene beads, gram-positive Bacillus anthracis, B. thuringiensis, and B. atrophaeus spores, and B. cereus vegetative cells were investigated by Raman imaging for differentiation and characterization purposes. Bacillus spore aerosol sizes were varied to provide visual proof for corroboration of spectral assignments. Conventional applications of Raman imaging consist of differentiating relatively broad areas of a sample in a microscope field of view. The spectral angle mapping data analysis algorithm was used to compare a library spectrum with experimental spectra from pixels in the microscope field of view. This direct one-to-one matching is straightforward, does not require a training set, is independent of absolute spectral intensity, and only requires univariate statistics. Raman imaging is expanded in its capabilities to differentiate and distinguish between discrete 1-6 microm size bacterial species in single particles, clusters of mixed species, and bioaerosols with interference background particles.

Multivariate analysis and classification of two-dimensional angular optical scattering patterns from aggregates.

Two-dimensional light-scattering patterns from aggregates have undergone feature extraction followed by multivariate statistical analysis. The aggregates are comprised of primary particles of varying shape and size. Morphological descriptors (features) were extracted by a nonlinear filtering algorithm (spectrum enhancement) and then processed by principal component analysis and discriminant function analysis. The analysis was performed on two data sets, one in which the aggregates had a fixed primary particle size but varied in overall dimension and another in which the aggregate size was fixed but the primary particle size varied. Classification of the samples was performed adequately, providing some distinction among the limited classes that were analyzed.

Backward-enhanced fluorescence from clusters of microspheres and particles of tryptophan.

Measured fluorescence from single-particle clusters of dye-doped polystyrene microspheres, dried nonspherical particles of tryptophan, and single polystyrene microspheres is enhanced in the backward direction (180 degrees from the incident laser). This enhancement (a factor of 2-3 compared to 90 degrees), which can be interpreted as a consequence of the reciprocity principle, increases with the particle refractive index.