|Kim, Huisung - Purdue University|
|Rajwa, Bartek - Purdue University|
|Bhunia, Arun - Purdue University|
|Robinson, John - Purdue University|
|Bae, Euiwon - Purdue University|
Submitted to: Journal of Biophotonics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2016
Publication Date: 7/14/2016
Citation: Kim, H., Rajwa, B., Bhunia, A.K., Robinson, J.P., Bae, E. 2016. Development of a multispectral light-scatter sensor for bacterial colonies. Journal of Biophotonics. 10:634–644.
Interpretive Summary: Rapid classification is important for detection of foodborne pathogens and bacterial colony morphology (form and structure)-based classification is one commonly used method. Though previous single laser scatter pattern-based classification of bacterial colonies has been effective, the method has shown some limitations with regard to specificity of pathogen identification. A new bacterial colony interrogation system was developed that uses three wavelengths of lasers to enhance the specificity of the classification using four foodborne pathogenic bacteria (E. coli, Listeria monocytogenes, Salmonella Enteritidis, Staphylococcus aureus). In addition to the hardware design, multispectral simulation code based on diffraction theory can assist scientists to find the origin of the difference of laser scatter patterns. This technology significantly improves the ability to confidently and rapidly identify foodborne bacterial pathogens directly from culture plates.
Technical Abstract: 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.