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Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns

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 ad...

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Autores principales: Doh, Iyll-Joon, Zuniga, Diana Vanessa Sarria, Shin, Sungho, Pruitt, Robert E., Rajwa, Bartek, Robinson, J. Paul, Bae, Euiwon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098818/
https://www.ncbi.nlm.nih.gov/pubmed/37050545
http://dx.doi.org/10.3390/s23073485
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author Doh, Iyll-Joon
Zuniga, Diana Vanessa Sarria
Shin, Sungho
Pruitt, Robert E.
Rajwa, Bartek
Robinson, J. Paul
Bae, Euiwon
author_facet Doh, Iyll-Joon
Zuniga, Diana Vanessa Sarria
Shin, Sungho
Pruitt, Robert E.
Rajwa, Bartek
Robinson, J. Paul
Bae, Euiwon
author_sort Doh, Iyll-Joon
collection PubMed
description 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%.
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spelling pubmed-100988182023-04-14 Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns Doh, Iyll-Joon Zuniga, Diana Vanessa Sarria Shin, Sungho Pruitt, Robert E. Rajwa, Bartek Robinson, J. Paul Bae, Euiwon Sensors (Basel) Article 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%. MDPI 2023-03-27 /pmc/articles/PMC10098818/ /pubmed/37050545 http://dx.doi.org/10.3390/s23073485 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Doh, Iyll-Joon
Zuniga, Diana Vanessa Sarria
Shin, Sungho
Pruitt, Robert E.
Rajwa, Bartek
Robinson, J. Paul
Bae, Euiwon
Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns
title Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns
title_full Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns
title_fullStr Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns
title_full_unstemmed Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns
title_short Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns
title_sort bacterial colony phenotyping with hyperspectral elastic light scattering patterns
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098818/
https://www.ncbi.nlm.nih.gov/pubmed/37050545
http://dx.doi.org/10.3390/s23073485
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