NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint

Near infrared spectroscopy in-line monitoring and modelling of soybean oil methanolysis has been done using multivariate curve resolution alternating least squares (MCR-ALS) with correlation constraint. This constraint allows for quantitation of the methyl ester formed with little calibration effort...

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Autores: Sales, Rafaella F., Lima, Suzana M. de, Stragevitch, Luiz, Pimentel, Maria Fernanda, Juan Capdevila, Anna de
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/119451
Acceso en línea:https://hdl.handle.net/2445/119451
Access Level:acceso abierto
Palabra clave:Espectroscòpia infraroja
Infrared spectroscopy
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spelling NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation ConstraintSales, Rafaella F.Lima, Suzana M. deStragevitch, LuizPimentel, Maria FernandaJuan Capdevila, Anna deEspectroscòpia infrarojaInfrared spectroscopyNear infrared spectroscopy in-line monitoring and modelling of soybean oil methanolysis has been done using multivariate curve resolution alternating least squares (MCR-ALS) with correlation constraint. This constraint allows for quantitation of the methyl ester formed with little calibration effort and the MCR model provides additionally a general description (qualitative and quantitative) of the rest of components in the process. Due to the complexity of this process, which shows components with severe kinetic and spectral overlap, suitably designed multiset analysis strategies were adopted to improve the recovery of concentration profiles of the methyl ester. To assess the temperature and catalyst concentration effects on the kinetic reaction, five batches with different temperatures (20, 44 and 55 °C) and catalyst concentrations (0.75 and 1 m/m%) were produced. The concentration profiles of methyl ester obtained by MCR-ALS for each batch was the starting information used to develop a simplified kinetic model and calculate the activation energy.Sociedade Brasileira de Química2018201820172018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion12 p.application/pdfapplication/pdfhttps://hdl.handle.net/2445/119451Articles publicats en revistes (Enginyeria Química i Química Analítica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.21577/0103-5053.20160218Journal of the Brazilian Chemical Society, 2017, vol. 28, num. 5, p. 695-706https://doi.org/10.21577/0103-5053.20160218cc-by (c) Sociedade Brasileira de Química, 2017http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1194512026-05-29T05:05:01Z
dc.title.none.fl_str_mv NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint
title NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint
spellingShingle NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint
Sales, Rafaella F.
Espectroscòpia infraroja
Infrared spectroscopy
title_short NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint
title_full NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint
title_fullStr NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint
title_full_unstemmed NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint
title_sort NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint
dc.creator.none.fl_str_mv Sales, Rafaella F.
Lima, Suzana M. de
Stragevitch, Luiz
Pimentel, Maria Fernanda
Juan Capdevila, Anna de
author Sales, Rafaella F.
author_facet Sales, Rafaella F.
Lima, Suzana M. de
Stragevitch, Luiz
Pimentel, Maria Fernanda
Juan Capdevila, Anna de
author_role author
author2 Lima, Suzana M. de
Stragevitch, Luiz
Pimentel, Maria Fernanda
Juan Capdevila, Anna de
author2_role author
author
author
author
dc.subject.none.fl_str_mv Espectroscòpia infraroja
Infrared spectroscopy
topic Espectroscòpia infraroja
Infrared spectroscopy
description Near infrared spectroscopy in-line monitoring and modelling of soybean oil methanolysis has been done using multivariate curve resolution alternating least squares (MCR-ALS) with correlation constraint. This constraint allows for quantitation of the methyl ester formed with little calibration effort and the MCR model provides additionally a general description (qualitative and quantitative) of the rest of components in the process. Due to the complexity of this process, which shows components with severe kinetic and spectral overlap, suitably designed multiset analysis strategies were adopted to improve the recovery of concentration profiles of the methyl ester. To assess the temperature and catalyst concentration effects on the kinetic reaction, five batches with different temperatures (20, 44 and 55 °C) and catalyst concentrations (0.75 and 1 m/m%) were produced. The concentration profiles of methyl ester obtained by MCR-ALS for each batch was the starting information used to develop a simplified kinetic model and calculate the activation energy.
publishDate 2017
dc.date.none.fl_str_mv 2017
2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/119451
url https://hdl.handle.net/2445/119451
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.21577/0103-5053.20160218
Journal of the Brazilian Chemical Society, 2017, vol. 28, num. 5, p. 695-706
https://doi.org/10.21577/0103-5053.20160218
dc.rights.none.fl_str_mv cc-by (c) Sociedade Brasileira de Química, 2017
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Sociedade Brasileira de Química, 2017
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 12 p.
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Sociedade Brasileira de Química
publisher.none.fl_str_mv Sociedade Brasileira de Química
dc.source.none.fl_str_mv Articles publicats en revistes (Enginyeria Química i Química Analítica)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.198674

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