ArticleJournal of biological engineering2012
Portable bacterial identification system based on elastic light scatter patterns.
Article in Journal of biological engineering, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Addressing the Challenges of IdentifyingMicroorganisms · 2026Article
- Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping.Applied microbiology and biotechnology · 2024Article
- Identification of colonies of cultured shellfish-associatedCurrent research in microbial sciences · 2021Article
- Elastic Light Scatter Pattern Analysis for the Expedited Detection ofFrontiers in microbiology · 2021Article
- Integrated multi-channel optical system for bacteria characterization and its potential use for monitoring of environmental bacteria.Biomedical optics express · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundConventional 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.
resultsThis 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.
conclusionsExperiments 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%.
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Registered trials
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