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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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%. |
format | Online Article Text |
id | pubmed-10098818 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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