Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Food Science and Technology (Campinas) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000500021 |
Resumo: | In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth. |
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Food Science and Technology (Campinas) |
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Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy networkblack-boxcellular concentrationpredictive microbiologyIn this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2013-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000500021Food Science and Technology v.33 suppl.1 2013reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/S0101-20612013000500021info:eu-repo/semantics/openAccessFurlong,Vitor BadialePereira Filho,Renato DutraMargarites,Ana CláudiaGoularte,Pâmela GuderCosta,Jorge Alberto Vieiraeng2013-03-06T00:00:00Zoai:scielo:S0101-20612013000500021Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2013-03-06T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network |
title |
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network |
spellingShingle |
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network Furlong,Vitor Badiale black-box cellular concentration predictive microbiology |
title_short |
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network |
title_full |
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network |
title_fullStr |
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network |
title_full_unstemmed |
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network |
title_sort |
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network |
author |
Furlong,Vitor Badiale |
author_facet |
Furlong,Vitor Badiale Pereira Filho,Renato Dutra Margarites,Ana Cláudia Goularte,Pâmela Guder Costa,Jorge Alberto Vieira |
author_role |
author |
author2 |
Pereira Filho,Renato Dutra Margarites,Ana Cláudia Goularte,Pâmela Guder Costa,Jorge Alberto Vieira |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Furlong,Vitor Badiale Pereira Filho,Renato Dutra Margarites,Ana Cláudia Goularte,Pâmela Guder Costa,Jorge Alberto Vieira |
dc.subject.por.fl_str_mv |
black-box cellular concentration predictive microbiology |
topic |
black-box cellular concentration predictive microbiology |
description |
In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-02-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000500021 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000500021 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0101-20612013000500021 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
dc.source.none.fl_str_mv |
Food Science and Technology v.33 suppl.1 2013 reponame:Food Science and Technology (Campinas) instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) instacron:SBCTA |
instname_str |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
instacron_str |
SBCTA |
institution |
SBCTA |
reponame_str |
Food Science and Technology (Campinas) |
collection |
Food Science and Technology (Campinas) |
repository.name.fl_str_mv |
Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
repository.mail.fl_str_mv |
||revista@sbcta.org.br |
_version_ |
1752126318237974528 |