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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Characterization and Interventions for Foodborne Pathogens » Research » Publications at this Location » Publication #406396

Research Project: Development of Innovative Technologies and Strategies to Mitigate Biological, Chemical, Physical, and Environmental Threats to Food Safety

Location: Characterization and Interventions for Foodborne Pathogens

Title: Bacterial colony phenotyping with hyperspectral elastic light scattering patterns

Author
item DOH, IYII-JOON - PURDUE UNIVERSITY
item ZUNIGA, DIANE - PURDUE UNIVERSITY
item SHIN, SUNGHO - PURDUE UNIVERSITY
item PRUITT, ROBERT - PURDUE UNIVERSITY
item RAJWA, BARTEK - PURDUE UNIVERSITY
item ROBINSON, J - PURDUE UNIVERSITY
item BAE, EUIWON - PURDUE UNIVERSITY

Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/24/2023
Publication Date: 3/27/2023
Citation: Doh, I., Zuniga, D.V., Shin, S., Pruitt, R.E., Rajwa, B., Robinson, J.P., Bae, E. 2023. Bacterial colony phenotyping with hyperspectral elastic light scattering patterns. Sensors. 23(7):3485. https://doi.org/10.3390/s23073485.
DOI: https://doi.org/10.3390/s23073485

Interpretive Summary: The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473–709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.

Technical Abstract: The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473–709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.