Stalked protozoa identification by image analysis and multivariable statistical techniques

Bibliographic Details
Main Author: Amaral, A. L.
Publication Date: 2008
Other Authors: Ginoris, Y. P., Nicolau, Ana, Coelho, M. A. Z., Ferreira, Eugénio C.
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/1822/7946
Summary: Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.
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spelling Stalked protozoa identification by image analysis and multivariable statistical techniquesProtozoaMetazoaActivated sludgeImage analysisMultivariable statistical techniquesScience & TechnologyProtozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.Springer VerlagUniversidade do MinhoAmaral, A. L.Ginoris, Y. P.Nicolau, AnaCoelho, M. A. Z.Ferreira, Eugénio C.2008-062008-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/7946eng"Analytical and Bioanalytical Chemistry". ISSN 1618-2642. 391:4 (June 2008) 1321-1325.1618-264210.1007/s00216-008-1845-y18327573info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T05:22:12Zoai:repositorium.sdum.uminho.pt:1822/7946Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:16:09.303514Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Stalked protozoa identification by image analysis and multivariable statistical techniques
title Stalked protozoa identification by image analysis and multivariable statistical techniques
spellingShingle Stalked protozoa identification by image analysis and multivariable statistical techniques
Amaral, A. L.
Protozoa
Metazoa
Activated sludge
Image analysis
Multivariable statistical techniques
Science & Technology
title_short Stalked protozoa identification by image analysis and multivariable statistical techniques
title_full Stalked protozoa identification by image analysis and multivariable statistical techniques
title_fullStr Stalked protozoa identification by image analysis and multivariable statistical techniques
title_full_unstemmed Stalked protozoa identification by image analysis and multivariable statistical techniques
title_sort Stalked protozoa identification by image analysis and multivariable statistical techniques
author Amaral, A. L.
author_facet Amaral, A. L.
Ginoris, Y. P.
Nicolau, Ana
Coelho, M. A. Z.
Ferreira, Eugénio C.
author_role author
author2 Ginoris, Y. P.
Nicolau, Ana
Coelho, M. A. Z.
Ferreira, Eugénio C.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Amaral, A. L.
Ginoris, Y. P.
Nicolau, Ana
Coelho, M. A. Z.
Ferreira, Eugénio C.
dc.subject.por.fl_str_mv Protozoa
Metazoa
Activated sludge
Image analysis
Multivariable statistical techniques
Science & Technology
topic Protozoa
Metazoa
Activated sludge
Image analysis
Multivariable statistical techniques
Science & Technology
description Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.
publishDate 2008
dc.date.none.fl_str_mv 2008-06
2008-06-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/7946
url https://hdl.handle.net/1822/7946
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv "Analytical and Bioanalytical Chemistry". ISSN 1618-2642. 391:4 (June 2008) 1321-1325.
1618-2642
10.1007/s00216-008-1845-y
18327573
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 Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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