UV spectrophotometry method for the monitoring of galacto-oligosaccharides production
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/9168 |
Resumo: | Monitoring the industrial production of galacto-oligosaccharides (GOS) requires a fast and accurate methodology able to quantify, in real time, the substrate level and the product yield. In this work, a simple, fast and inexpensive UV spectrophotometric method, together with partial least squares regression (PLS) and artificial neural networks (ANN), was applied to simultaneously estimate the products (GOS) and the substrate (lactose) concentrations in fermentation samples. The selected multiple models were trained and their prediction abilities evaluated by cross-validation and external validation being the results obtained compared with HPLC measurements. ANN models, generated from absorbance spectra data of the fermentation samples, gave, in general, the best performance being able to accurately and precisely predict lactose and total GOS levels, with standard error of prediction lower than 13 g kg 1 and coefficient of determination for the external validation set of 0.93–0.94, showing residual predictive deviations higher than five, whereas lower precision was obtained with the multiple model generated with PLS. The results obtained show that UV spectrophotometry allowed an accurate and non-destructive determination of sugars in fermentation samples and could be used as a fast alternative method for monitoring GOS production. |
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UV spectrophotometry method for the monitoring of galacto-oligosaccharides productionFermentation processesGalacto-oligosaccharidesUV spectrophotometerPartial least squares regressionArtificial neural networkScience & TechnologyMonitoring the industrial production of galacto-oligosaccharides (GOS) requires a fast and accurate methodology able to quantify, in real time, the substrate level and the product yield. In this work, a simple, fast and inexpensive UV spectrophotometric method, together with partial least squares regression (PLS) and artificial neural networks (ANN), was applied to simultaneously estimate the products (GOS) and the substrate (lactose) concentrations in fermentation samples. The selected multiple models were trained and their prediction abilities evaluated by cross-validation and external validation being the results obtained compared with HPLC measurements. ANN models, generated from absorbance spectra data of the fermentation samples, gave, in general, the best performance being able to accurately and precisely predict lactose and total GOS levels, with standard error of prediction lower than 13 g kg 1 and coefficient of determination for the external validation set of 0.93–0.94, showing residual predictive deviations higher than five, whereas lower precision was obtained with the multiple model generated with PLS. The results obtained show that UV spectrophotometry allowed an accurate and non-destructive determination of sugars in fermentation samples and could be used as a fast alternative method for monitoring GOS production.Fundação para a Ciência e a Tecnologia (FCT) - Bolsa de doutouramento SFRH/BDE/15510/2004Agência da Inovação – Programa IDEIA (Potugal)Elsevier Ltd.Universidade do MinhoDias, Luís G.Veloso, Ana C. A.Correia, Daniela M.Rocha, OrlandoTorres, D.Rocha, I.Rodrigues, L. R.Peres, A. M.20092009-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/9168eng"Food Chemistry." ISSN 0308-8146. 113:1 (Mar. 2009) 246–252.0308-814610.1016/j.foodchem.2008.06.072http://www.elsevier.com/info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T11:55:12Zoai:repositorium.sdum.uminho.pt:1822/9168Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:44:43.877590Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
UV spectrophotometry method for the monitoring of galacto-oligosaccharides production |
title |
UV spectrophotometry method for the monitoring of galacto-oligosaccharides production |
spellingShingle |
UV spectrophotometry method for the monitoring of galacto-oligosaccharides production Dias, Luís G. Fermentation processes Galacto-oligosaccharides UV spectrophotometer Partial least squares regression Artificial neural network Science & Technology |
title_short |
UV spectrophotometry method for the monitoring of galacto-oligosaccharides production |
title_full |
UV spectrophotometry method for the monitoring of galacto-oligosaccharides production |
title_fullStr |
UV spectrophotometry method for the monitoring of galacto-oligosaccharides production |
title_full_unstemmed |
UV spectrophotometry method for the monitoring of galacto-oligosaccharides production |
title_sort |
UV spectrophotometry method for the monitoring of galacto-oligosaccharides production |
author |
Dias, Luís G. |
author_facet |
Dias, Luís G. Veloso, Ana C. A. Correia, Daniela M. Rocha, Orlando Torres, D. Rocha, I. Rodrigues, L. R. Peres, A. M. |
author_role |
author |
author2 |
Veloso, Ana C. A. Correia, Daniela M. Rocha, Orlando Torres, D. Rocha, I. Rodrigues, L. R. Peres, A. M. |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Dias, Luís G. Veloso, Ana C. A. Correia, Daniela M. Rocha, Orlando Torres, D. Rocha, I. Rodrigues, L. R. Peres, A. M. |
dc.subject.por.fl_str_mv |
Fermentation processes Galacto-oligosaccharides UV spectrophotometer Partial least squares regression Artificial neural network Science & Technology |
topic |
Fermentation processes Galacto-oligosaccharides UV spectrophotometer Partial least squares regression Artificial neural network Science & Technology |
description |
Monitoring the industrial production of galacto-oligosaccharides (GOS) requires a fast and accurate methodology able to quantify, in real time, the substrate level and the product yield. In this work, a simple, fast and inexpensive UV spectrophotometric method, together with partial least squares regression (PLS) and artificial neural networks (ANN), was applied to simultaneously estimate the products (GOS) and the substrate (lactose) concentrations in fermentation samples. The selected multiple models were trained and their prediction abilities evaluated by cross-validation and external validation being the results obtained compared with HPLC measurements. ANN models, generated from absorbance spectra data of the fermentation samples, gave, in general, the best performance being able to accurately and precisely predict lactose and total GOS levels, with standard error of prediction lower than 13 g kg 1 and coefficient of determination for the external validation set of 0.93–0.94, showing residual predictive deviations higher than five, whereas lower precision was obtained with the multiple model generated with PLS. The results obtained show that UV spectrophotometry allowed an accurate and non-destructive determination of sugars in fermentation samples and could be used as a fast alternative method for monitoring GOS production. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009 2009-01-01T00:00:00Z |
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 |
http://hdl.handle.net/1822/9168 |
url |
http://hdl.handle.net/1822/9168 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
"Food Chemistry." ISSN 0308-8146. 113:1 (Mar. 2009) 246–252. 0308-8146 10.1016/j.foodchem.2008.06.072 http://www.elsevier.com/ |
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 |
Elsevier Ltd. |
publisher.none.fl_str_mv |
Elsevier Ltd. |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132197451464704 |