Rapid identification and classification of Campylobacter spp. using laser optical scattering technology

He, Yiping; Reed, Sandra M.; Bhunia, Arun K.; Gehring, Andrew G.; Nguyen, Ly-Huong T.; Irwin, Peter L.

doi:10.1016/j.fm.2014.11.004

Cited by 23 publications

(17 citation statements)

References 32 publications

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“…A label‐free, non‐destructive, automated detection technique called BARDOT (BActerial Rapid Detection using Optical scattering Technology) based on elastic‐light‐scatter (ELS) patterns of bacteria colonies was developed for rapid detection and classification of microbial organisms . The applicability of the technology were reported for different organisms using a single‐wavelength laser and a varying number of genera or species . The merit of this technology is that the interrogation photons interact with the whole volume of a colony, thus collecting better phenotypic characteristics than reflective imaging.…”

Section: Introductionmentioning

confidence: 99%

Development of a multispectral light‐scatter sensor for bacterial colonies

Kim

Rajwa

Bhunia

et al. 2016

Journal of Biophotonics

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We report a multispectral elastic-light-scatter instrument that can simultaneously detect three-wavelength scatter patterns and associated optical densities from individual bacterial colonies, overcoming the limits of the single-wavelength predecessor. Absorption measurements on liquid bacterial samples revealed that the spectroscopic information can indeed contribute to sample differentiability. New optical components, including a pellicle beam splitter and an optical cage system, were utilized for robust acquisition of multispectral images. Four different genera and seven shiga toxin producing E. coli serovars were analyzed; the acquired images showed differences in scattering characteristics among the tested organisms. In addition, colony-based spectral optical-density information was also collected. The optical model, which was developed using diffraction theory, correctly predicted wavelength-related differences in scatter patterns, and was matched with the experimental results. Scatter-pattern classification was performed using pseudo-Zernike (GPZ) polynomials/moments by combining the features collected at all three wavelengths and selecting the best features via a random-forest method. The data demonstrate that the selected features provide better classification rates than the same number of features from any single wavelength. Three wavelength-merged scatter pattern from E. coli.

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Section: Introductionmentioning

confidence: 99%

Development of a multispectral light‐scatter sensor for bacterial colonies

Kim

Rajwa

Bhunia

et al. 2016

Journal of Biophotonics

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“…(Singh et al, 2015), and Campylobacter spp. (He et al, 2015) with classification accuracy of 90e99%.…”

Section: Introductionmentioning

confidence: 99%

Fiber optic and light scattering sensors: Complimentary approaches to rapid detection of Salmonella enterica in food samples

et al. 2016

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“…(Singh et al, 2015), Campylobacter spp. (He et al, 2015), Shiga-toxigenic E. coli (Tang et al, 2014), Listeria spp. (Banada et al, 2009), Salmonella enterica , and Vibrio spp.…”

Section: Introductionmentioning

confidence: 99%