Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM

The most commonly used biomass estimate for microalgae is obtained from cell biovolume, usually calculated from microscopically measured linear dimensions. Although reliable, this is a highly time-consuming and specialized technique. Automated sampling devices that acquire images of cells and use pa...

Descripción completa

Detalles Bibliográficos
Autores: Santaella-Álvarez, Eladio, López-Urrutia-Lorente, Ángel, Nogueira, Enrique
Tipo de recurso: artículo
Fecha de publicación:2012
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/319565
Acceso en línea:http://hdl.handle.net/10261/319565
Access Level:acceso abierto
Palabra clave:Centro Oceanográfico de Gijón
Medio Marino
id ES_f06b0a6ffb08493dfafaff4e2e9cd07e
oai_identifier_str oai:digital.csic.es:10261/319565
network_acronym_str ES
network_name_str España
repository_id_str
spelling Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAMSantaella-Álvarez, EladioLópez-Urrutia-Lorente, ÁngelNogueira, EnriqueCentro Oceanográfico de GijónMedio MarinoThe most commonly used biomass estimate for microalgae is obtained from cell biovolume, usually calculated from microscopically measured linear dimensions. Although reliable, this is a highly time-consuming and specialized technique. Automated sampling devices that acquire images of cells and use pattern recognition techniques to identify the images have been developed as an alternative to microscopy-based methods. There are some aspects of automatic sampling and classification methods, however, which can be improved for the analysis of field samples including living and non-living particles. In this work, we demonstrate how the accuracy of a state-of the-art technique for plankton classification (Support Vector Machine) can be improved up to 86% if a previous automated step designed to remove non-living images is included. There is a tendency with the currently applied automatic methods to misestimate cell biovolume due to the two-dimensionality of the images. Here, we present a data set of more than 500 samples to show that the greatest effect is caused by the incorrect estimation of biovolume of the chain-forming diatoms. This results in an overestimate of biomass of between 20 and 100% where chain-forming diatoms represent more than the 20% of the biomass of the sample. We show how the classification method can be adapted to provide not only taxonomic but also the morphological classification of cells in order to obtain a more reliable estimate of biovolume according to the predicted cell shape, in a way comparable with microscopy-based estimates.Sí202320232012info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://hdl.handle.net/10261/319565reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésCentro Oceanográfico de Gijóninfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3195652026-05-22T06:33:51Z
dc.title.none.fl_str_mv Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM
title Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM
spellingShingle Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM
Santaella-Álvarez, Eladio
Centro Oceanográfico de Gijón
Medio Marino
title_short Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM
title_full Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM
title_fullStr Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM
title_full_unstemmed Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM
title_sort Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM
dc.creator.none.fl_str_mv Santaella-Álvarez, Eladio
López-Urrutia-Lorente, Ángel
Nogueira, Enrique
author Santaella-Álvarez, Eladio
author_facet Santaella-Álvarez, Eladio
López-Urrutia-Lorente, Ángel
Nogueira, Enrique
author_role author
author2 López-Urrutia-Lorente, Ángel
Nogueira, Enrique
author2_role author
author
dc.subject.none.fl_str_mv Centro Oceanográfico de Gijón
Medio Marino
topic Centro Oceanográfico de Gijón
Medio Marino
description The most commonly used biomass estimate for microalgae is obtained from cell biovolume, usually calculated from microscopically measured linear dimensions. Although reliable, this is a highly time-consuming and specialized technique. Automated sampling devices that acquire images of cells and use pattern recognition techniques to identify the images have been developed as an alternative to microscopy-based methods. There are some aspects of automatic sampling and classification methods, however, which can be improved for the analysis of field samples including living and non-living particles. In this work, we demonstrate how the accuracy of a state-of the-art technique for plankton classification (Support Vector Machine) can be improved up to 86% if a previous automated step designed to remove non-living images is included. There is a tendency with the currently applied automatic methods to misestimate cell biovolume due to the two-dimensionality of the images. Here, we present a data set of more than 500 samples to show that the greatest effect is caused by the incorrect estimation of biovolume of the chain-forming diatoms. This results in an overestimate of biomass of between 20 and 100% where chain-forming diatoms represent more than the 20% of the biomass of the sample. We show how the classification method can be adapted to provide not only taxonomic but also the morphological classification of cells in order to obtain a more reliable estimate of biovolume according to the predicted cell shape, in a way comparable with microscopy-based estimates.
publishDate 2012
dc.date.none.fl_str_mv 2012
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/319565
url http://hdl.handle.net/10261/319565
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Centro Oceanográfico de Gijón
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869423954681135104
score 15,811543

Similares en LA Referencia