Developments in the Classification of TAOS Optical Scattering Patterns from Single, Heterogeneous Airborne Particles

Experimental: TAOS (two-angle optical scattering) instruments record the intensity patterns of laser light scattered by single airborne particles over a wide range of the scattering angles {θ,ϕ} [1]. Large data sets are available, which consist of scattering patterns from a variety of known materials and from environmental sampling. Data analysis: Due to the lack of methods which exactly solve the inverse obstacle scattering problem, artificial intelligence techniques can be used. A classifier has been developed that is based on the spectrum enhancement algorithm [2]. This classifier extracts vectors of morphological descriptors from TAOS patterns and submits them to principal components analysis. Supervised training of the classifier occurs by processing hundreds of patterns from known materials and maximizing a suitable figure of merit. The trained and validated classifier is applied to patterns from new materials for the purpose of recognition. A typical recent classification result is shown by Figure 1 below. The purpose of ongoing work is to design classification experiments with patterns from new materials (NaCl crystals, soot, outdoor dust, . . .) and apply the above outlined method to particle recognition and scoring