Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier

Abstract

Biological microparticles, including bacteria, scatter light in all directions when illuminated. The complex scatter pattern is dependent on particle size, shape, refraction index, density, and morphology. Commercial flow cytometers allow measurement of scattered light intensity at forward and perpendicular (side) angles (28 y 1 208 and 708 y 2 1108, respectively) with a speed varying from 10 to 10,000 particles per second. The choice of angle is dictated by the fact that scattered light in the forward region is primarily dependent on cell size and refractive index, whereas side-scatter intensity is dependent on the granularity of cellular structures. However, these two-parameter measurements cannot be used to separate populations of cells of similar shape, size, or structure. Hence, there have been several attempts in flow cytometry to measure the entire scatter patterns. The published concepts require the use of unique custom-built flow cytometers and cannot be applied to existing instruments. It was also not clear how much information about patterns is really necessary to separate various populations of cells present in a given sample. The presented work demonstrates application of pattern-recognition techniques to classify particles on the basis of their discrete scatter patterns collected at just five different angles, and accompanied by the measurement of axial light loss. The proposed approach can be potentially used with existing instruments because it requires only the addition of a compact enhanced scatter detector. An analytical model of scatter of laser beams by individual bacterial cells suspended in a fluid was used to determine the location of scatter sensors. Experimental results were used to train the support vector machine-based pattern recognition system. It has been shown that information provided just by five angles of scatter and axial light loss can be sufficient to recognize various bacteria with 68-99% success rate. ' 2007 International Society for Analytical Cytology

Automated bacterial identification by angle resolved dark-field imaging

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“…Angle-resolved flow cytometry systems are inherently complex and expensive, requiring complex optics to achieve sufficient angular resolution. More recently, systems with fewer channels have been explored in conjunction with more sophisticated machine learning algorithms [10].…”

Dual wavelength multiple-angle light scattering system for cryptosporidium detection

“…It has been demonstrated that forward scatter and side scatter can be used to identify a number of cell subpopulation since forward scattering is proportional to the diameter of the cell and side scattering with perpendicular light scatter is also sensitive to small internal structure and to refractive index change 26 . In addition, the scattering is also a function of wavelength 31 .…”

“…Light scattering techniques are widely used to investigate the particle properties such as such as size [14][15][16] , refractive index [17][18] and concentration [19][20] and also applied to biological particle detection [21][22][23][24][25][26] as its advantages; easy to perform; low maintenance; non-invasive; have high sensitivity; and the potential for real-time detection.…”

Cytomics and nanobioengineering

“…Not only different bacterial cells in FCM but colonies of different bacterial strains by scattered light and automated image recognition are distinguished (51,52). Light scatter differences reflect unique colony morphotypes and consequently interspecies differences and thus a new diagnostic tool.…”