Portable bacterial identification system based on elastic light scatter patterns
1 School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47907, USA
2 School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
3 Visiting researcher, Cytometry Laboratory, Purdue University, West Lafayette, IN, 47907, USA
4 Dr. J. Paul Robinson Purdue University Cytometry Laboratory, Bindley Bioscience Center, Purdue University, 1203 West State Street, Discovery Park, West Lafayette, IN, 47907, USA
5 Department of Basic Medical Sciences, Purdue University, West Lafayette, IN, 47907, USA
6 Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, 47907, USA
7 Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA
Journal of Biological Engineering 2012, 6:12 doi:10.1186/1754-1611-6-12Published: 28 August 2012
Conventional diagnosis and identification of bacteria requires shipment of samples to a laboratory for genetic and biochemical analysis. This process can take days and imposes significant delay to action in situations where timely intervention can save lives and reduce associated costs. To enable faster response to an outbreak, a low-cost, small-footprint, portable microbial-identification instrument using forward scatterometry has been developed.
This device, weighing 9 lb and measuring 12 × 6 × 10.5 in., utilizes elastic light scatter (ELS) patterns to accurately capture bacterial colony characteristics and delivers the classification results via wireless access. The overall system consists of two CCD cameras, one rotational and one translational stage, and a 635-nm laser diode. Various software algorithms such as Hough transform, 2-D geometric moments, and the traveling salesman problem (TSP) have been implemented to provide colony count and circularity, centering process, and minimized travel time among colonies.
Experiments were conducted with four bacteria genera using pure and mixed plate and as proof of principle a field test was conducted in four different locations where the average classification rate ranged between 95 and 100%.