Abstract. Air pollution has had an increasingly powerful impact on the everyday life of
humans. More and more people are aware of the health problems that may result
from inhaling air which contains dust, bacteria, pollens or fungi. There is a
need for real-time information about ambient particulate matter. Devices
currently available on the market can detect some particles in the air but
cannot classify them according to health threats. Fortunately, a new type of
technology is emerging as a promising solution. Laser-based bio-detectors are characterizing a new era in aerosol research. They
are capable of characterizing a great number of individual particles in
seconds by analyzing optical scattering and fluorescence characteristics. In
this study we demonstrate the application of artificial neural networks
(ANNs) to real-time analysis of single-particle fluorescence fingerprints
acquired using BARDet (a Bio-AeRosol Detector). A total of 48 different aerosols
including pollens, bacteria, fungi, spores, and nonbiological substances
were characterized. An entirely new approach to data analysis using a
decision tree comprising 22 independent neural networks was discussed.
Applying confusion matrices and receiver operating characteristics (ROC) analysis the best sets of ANNs for each
group of similar aerosols were determined. As a result, a very high accuracy
of aerosol classification in real time was achieved. It was found that for
some substances that have characteristic spectra, almost each particle can be
properly classified. Aerosols with similar spectral characteristics can be
classified as specific clouds with high probability. In both cases the system
recognized aerosol type with no mistakes. In the future, it is planned that performance of the system may be
determined under real environmental conditions, involving characterization
of fluorescent and nonfluorescent particles.
Improved real-time bio-aerosol classification using artificial neural networks