Count of bacteria and yeast in microbial bioproduct using digital image processing

Detalhes bibliográficos
Autor(a) principal: Martín, Jorge Peña
Data de Publicação: 2021
Outros Autores: Alvarado-Capó, Yelenys, Morales, Rubén Orozco, Pichardo, Tatiana, López, Ailet Abreu
Tipo de documento: Artigo
Idioma: eng
Título da fonte: ITEGAM-JETIA
Texto Completo: https://itegam-jetia.org/journal/index.php/jetia/article/view/781
Resumo: The count of microorganisms in substances from different industries, like the count of bacteria and yeasts, is a necessary and important process since long time ago. Traditionally, in the industries this process is performed by experts observing the samples in the microscopes, which is time-consuming and varies depending on the degree of expertise of the experts. Currently, the use of digital images of the samples to be analyzed is a variant widely used for such count task. In that sense, several methods have been created in recent years to make this process, but none of them covers the wide range of diversity that can be found in the real microbiological world. With these ideas as premises, a new method for count bacteria and yeasts in microbial bioproducts using digital images is presented in this paper, in order to provide to experts the approximate number of those microorganism. The method involves basic operations of digital image processing like contour detection, morphological operations and statistical analysis; and it was developed in Python language using the OpenCV library. The results obtained were evaluated by microbiological experts proved to have an acceptable performance for the context of use.
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spelling Count of bacteria and yeast in microbial bioproduct using digital image processingRecuento de bacterias y levaduras en bioproductos microbianos mediante procesamiento de imágenes digitalesContagem de bactérias e leveduras em bioproduto microbiano usando processamento digital de imagensThe count of microorganisms in substances from different industries, like the count of bacteria and yeasts, is a necessary and important process since long time ago. Traditionally, in the industries this process is performed by experts observing the samples in the microscopes, which is time-consuming and varies depending on the degree of expertise of the experts. Currently, the use of digital images of the samples to be analyzed is a variant widely used for such count task. In that sense, several methods have been created in recent years to make this process, but none of them covers the wide range of diversity that can be found in the real microbiological world. With these ideas as premises, a new method for count bacteria and yeasts in microbial bioproducts using digital images is presented in this paper, in order to provide to experts the approximate number of those microorganism. The method involves basic operations of digital image processing like contour detection, morphological operations and statistical analysis; and it was developed in Python language using the OpenCV library. The results obtained were evaluated by microbiological experts proved to have an acceptable performance for the context of use.El recuento de microorganismos en sustancias de diferentes industrias, como el recuento de bacterias y levaduras, es un proceso necesario e importante desde hace mucho tiempo. Tradicionalmente, en las industrias, este proceso lo realizan expertos que observan las muestras en los microscopios, lo que requiere mucho tiempo y varía según el grado de experiencia de los expertos. Actualmente, el uso de imágenes digitales de las muestras a analizar es una variante muy utilizada para dicha tarea de conteo. En ese sentido, en los últimos años se han creado varios métodos para realizar este proceso, pero ninguno de ellos cubre el amplio abanico de diversidad que se puede encontrar en el mundo microbiológico real. Con estas ideas como premisas, en este trabajo se presenta un nuevo método para el recuento de bacterias y levaduras en bioproductos microbianos mediante imágenes digitales, con el fin de proporcionar a los expertos el número aproximado de esos microorganismos. El método involucra operaciones básicas de procesamiento de imágenes digitales como detección de contornos, operaciones morfológicas y análisis estadístico; y fue desarrollado en lenguaje Python usando la biblioteca OpenCV. Los resultados obtenidos fueron evaluados por expertos en microbiología que demostraron tener un desempeño aceptable para el contexto de uso.A contagem de microrganismos em substâncias de diferentes indústrias, como a contagem de bactérias e leveduras, é um processo necessário e importante há muito tempo. Tradicionalmente, nas indústrias, esse processo é realizado por especialistas que observam as amostras nos microscópios, o que é demorado e varia de acordo com o grau de especialização dos especialistas. Atualmente, o uso de imagens digitais das amostras a serem analisadas é uma variante amplamente utilizada para tal tarefa de contagem. Nesse sentido, vários métodos foram criados nos últimos anos para fazer esse processo, mas nenhum deles cobre a ampla gama de diversidade que pode ser encontrada no mundo microbiológico real. Com essas ideias como premissas, um novo método para contagem de bactérias e leveduras em bioprodutos microbianos por meio de imagens digitais é apresentado neste trabalho, a fim de fornecer aos especialistas o número aproximado desses microrganismos. O método envolve operações básicas de processamento de imagem digital, como detecção de contorno, operações morfológicas e análise estatística; e foi desenvolvido na linguagem Python usando a biblioteca OpenCV. Os resultados obtidos foram avaliados por especialistas em microbiologia e comprovaram ter um desempenho aceitável para o contexto de utilização.ITEGAM - Instituto de Tecnologia e Educação Galileo da Amazônia2021-12-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://itegam-jetia.org/journal/index.php/jetia/article/view/78110.5935/jetia.v7i32.781ITEGAM-JETIA; v.7 n.32 2021; 12-22ITEGAM-JETIA; v.7 n.32 2021; 12-22ITEGAM-JETIA; v.7 n.32 2021; 12-222447-022810.5935/jetia.v7i32reponame:ITEGAM-JETIAinstname:Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)instacron:ITEGAMenghttps://itegam-jetia.org/journal/index.php/jetia/article/view/781/515Martín, Jorge PeñaAlvarado-Capó, YelenysMorales, Rubén OrozcoPichardo, TatianaLópez, Ailet Abreuinfo:eu-repo/semantics/openAccess2021-12-16T12:59:41Zoai:ojs.itegam-jetia.org:article/781Revistahttps://itegam-jetia.org/journal/index.php/jetiaPRIhttps://itegam-jetia.org/journal/index.php/jetia/oaieditor@itegam-jetia.orgopendoar:2021-12-16T12:59:41ITEGAM-JETIA - Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)false
dc.title.none.fl_str_mv Count of bacteria and yeast in microbial bioproduct using digital image processing
Recuento de bacterias y levaduras en bioproductos microbianos mediante procesamiento de imágenes digitales
Contagem de bactérias e leveduras em bioproduto microbiano usando processamento digital de imagens
title Count of bacteria and yeast in microbial bioproduct using digital image processing
spellingShingle Count of bacteria and yeast in microbial bioproduct using digital image processing
Martín, Jorge Peña
title_short Count of bacteria and yeast in microbial bioproduct using digital image processing
title_full Count of bacteria and yeast in microbial bioproduct using digital image processing
title_fullStr Count of bacteria and yeast in microbial bioproduct using digital image processing
title_full_unstemmed Count of bacteria and yeast in microbial bioproduct using digital image processing
title_sort Count of bacteria and yeast in microbial bioproduct using digital image processing
author Martín, Jorge Peña
author_facet Martín, Jorge Peña
Alvarado-Capó, Yelenys
Morales, Rubén Orozco
Pichardo, Tatiana
López, Ailet Abreu
author_role author
author2 Alvarado-Capó, Yelenys
Morales, Rubén Orozco
Pichardo, Tatiana
López, Ailet Abreu
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Martín, Jorge Peña
Alvarado-Capó, Yelenys
Morales, Rubén Orozco
Pichardo, Tatiana
López, Ailet Abreu
description The count of microorganisms in substances from different industries, like the count of bacteria and yeasts, is a necessary and important process since long time ago. Traditionally, in the industries this process is performed by experts observing the samples in the microscopes, which is time-consuming and varies depending on the degree of expertise of the experts. Currently, the use of digital images of the samples to be analyzed is a variant widely used for such count task. In that sense, several methods have been created in recent years to make this process, but none of them covers the wide range of diversity that can be found in the real microbiological world. With these ideas as premises, a new method for count bacteria and yeasts in microbial bioproducts using digital images is presented in this paper, in order to provide to experts the approximate number of those microorganism. The method involves basic operations of digital image processing like contour detection, morphological operations and statistical analysis; and it was developed in Python language using the OpenCV library. The results obtained were evaluated by microbiological experts proved to have an acceptable performance for the context of use.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-15
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://itegam-jetia.org/journal/index.php/jetia/article/view/781
10.5935/jetia.v7i32.781
url https://itegam-jetia.org/journal/index.php/jetia/article/view/781
identifier_str_mv 10.5935/jetia.v7i32.781
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://itegam-jetia.org/journal/index.php/jetia/article/view/781/515
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv ITEGAM - Instituto de Tecnologia e Educação Galileo da Amazônia
publisher.none.fl_str_mv ITEGAM - Instituto de Tecnologia e Educação Galileo da Amazônia
dc.source.none.fl_str_mv ITEGAM-JETIA; v.7 n.32 2021; 12-22
ITEGAM-JETIA; v.7 n.32 2021; 12-22
ITEGAM-JETIA; v.7 n.32 2021; 12-22
2447-0228
10.5935/jetia.v7i32
reponame:ITEGAM-JETIA
instname:Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)
instacron:ITEGAM
instname_str Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)
instacron_str ITEGAM
institution ITEGAM
reponame_str ITEGAM-JETIA
collection ITEGAM-JETIA
repository.name.fl_str_mv ITEGAM-JETIA - Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)
repository.mail.fl_str_mv editor@itegam-jetia.org
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