Morphotypic analysis and classification of bacteria and bacterial colonies using laser light-scattering, pattern recognition, and machine-learning system
2009
Light scattering is one of the most fundamental optical processes whereby electromagnetic waves are forced to deviate from
a straight trajectory by non-uniformities in the medium that they traverse. This presentation summarizes our recent research
on application of light-scatter measurements paired with machine learning and pattern recognition methodologies for label-free
classification of bioparticles. Two separate examples of light scatter-based techniques are discussed: forward-scatter
measurements of bacterial colonies in an imaging system, and flow cytometry measurements of scatter signals formed by
individual bacterial particles.
Recently, we have reported a first practical implementation of a system capable of label-free classification and recognition
of pathogenic species of Listeria , Salmonella, Vibrio, Staphylococcus, and E. coli using forward-scatter patterns
produced by bacterial colonies irradiated with laser light. Individual bacteria in flow also form complex patterns dependent
on particle size, shape, refraction index, density, and morphology. Although commercial flow cytometers allow scatter
measurement at two angles this rudimentary approach cannot be used to separate populations of bioparticles of similar
shape, size, or structure. The custom-built system used in the presented work collects axial light-loss and scatter signals
at five carefully chosen angles. Experimental results obtained from colony scanner, as well from the extended cytometry
instrument, were used to train the pattern-recognition algorithm. The results demonstrate that information provided by
scatter alone may be sufficient to recognize various bioparticles with 90-99% success rate, both in flow and in imaging
systems.
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