English

Optical elastic scattering for early label-free identification of clinical pathogens

Biological Physics 2016-10-11 v1 Optics

Abstract

We report here on the ability of elastic light scattering in discriminating Gram+, Gram-and yeasts at an early stage of growth (6h). Our technique is non-invasive, low cost and does require neither skilled operators nor reagents. Therefore it is compatible with automation. It is based on the analysis of the scattering pattern (scatterogram) generated by a bacterial microcolony growing on agar, when placed in the path of a laser beam. Measurements are directly performed on closed Petri dishes. The characteristic features of a given scatterogram are first computed by projecting the pattern onto the Zernike orthogonal basis. Then the obtained data are compared to a database so that machine learning can yield identification result. A 10-fold cross-validation was performed on a database over 8 species (15 strains, 1906 scatterograms), at 6h of incubation. It yielded a 94% correct classification rate between Gram+, Gram-and yeasts. Results can be improved by using a more relevant function basis for projections, such as Fourier-Bessel functions. A fully integrated instrument has been installed at the Grenoble hospital's laboratory of bacteriology and a validation campaign has been started for the early screening of SA and MRSA (Staphylococcus aureus, methicillin-resistant S. aureus) carriers. Up to now, all the published studies about elastic scattering were performed in a forward mode, which is restricted to transparent media. However, in clinical diagnostics, most of media are opaque, such as blood-supplemented agar. That is why we propose a novel scheme capable of collecting back-scattered light which provides comparable results.

Keywords

Cite

@article{arxiv.1610.02980,
  title  = {Optical elastic scattering for early label-free identification of clinical pathogens},
  author = {Valentin Genuer and Olivier Gal and Jérémy Méteau and Pierre Marcoux and Emmanuelle Schultz and Éric Lacot and Max Maurin and Jean-Marc Dinten},
  journal= {arXiv preprint arXiv:1610.02980},
  year   = {2016}
}