Development of a multispectral light‐scatter sensor for bacterial colonies

• Published in: Journal of biophotonics Vol. 10; no. 5; pp. 634 - 644
• Main Authors: Kim, Huisung, Rajwa, Bartek, Bhunia, Arun K., Robinson, J. Paul, Bae, Euiwon
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY‐VCH Verlag 01.05.2017; Wiley Subscription Services, Inc
• Subjects: Absorption; Algorithms; Bacteria; bacteria identification; Cages; Classification; Colonies; Diffraction; Diffraction patterns; Diffraction theory; E coli; Elastic scattering; Escherichia coli - growth & development; Genera; Image acquisition; Light; light scattering; Mathematical models; multi wavelength; Optical components; Optical Devices; Pattern classification; Pellicle; Robustness; Shiga toxin; Spectra; spectral measurement; Spectroscopic analysis; Spectrum Analysis; Wavelength; Wavelengths; Zernike polynomials; multi wavelength; spectral measurement; light scattering; diffraction; bacteria identification
• ISSN: 1864-063X; 1864-0648
• DOI: 10.1002/jbio.201500338

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. Mulit‐spectral forward scattering images from bacterial colonies enhances the classification of the label‐free detection system.