QSPR analyses for aminograms in food: Citrus juices and concentrates

Autores
Pomilio, Alicia Beatriz; Giraudo, Miguel Angel Domingo; Duchowicz, Pablo Román; Castro, Eduardo Alberto
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Dragon theoretical descriptors were derived for a set of optimised amino acid structures, with the purpose of establishing quantitative structure-property relationship (QSPR) models to predict aminograms for 100% natural fresh juices and concentrates of Navel and Valencia oranges, and Eureka lemon. We used the statistical replacement method technique for designing the best multi-parametric linear regression models, which included structural features selected from a pool containing 1497 constitutional, topological, geometrical, or electronic types of molecular descriptors. The prediction results achieved in this work were in most cases in good agreement with experimental amino acid profiles obtained in our laboratories by a validated HPLC procedure, thus demonstrating the predictive power of the designed QSPR. The developed approach is of practical value, especially when it is not possible to assign the analyte concentration with an accurate degree of certainty.
Fil: Pomilio, Alicia Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Bioquímica y Medicina Molecular. Universidad de Buenos Aires. Facultad Medicina. Instituto de Bioquímica y Medicina Molecular; Argentina
Fil: Giraudo, Miguel Angel Domingo. Universidad Nacional de Lanús; Argentina
Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Materia
Amino Acids
Aminograms
Citrus Juices And Concentrates
Eureka Lemon
Multi-Variable Linear Regression
Navel And Valencia Oranges
Qspr Theory
Replacement Method
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/67590
Acceder
id CONICETDig_0e02f6c6783d9513827f3eaf6177e846
oai_identifier_str oai:ri.conicet.gov.ar:11336/67590
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling QSPR analyses for aminograms in food: Citrus juices and concentratesPomilio, Alicia BeatrizGiraudo, Miguel Angel DomingoDuchowicz, Pablo RománCastro, Eduardo AlbertoAmino AcidsAminogramsCitrus Juices And ConcentratesEureka LemonMulti-Variable Linear RegressionNavel And Valencia OrangesQspr TheoryReplacement Methodhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Dragon theoretical descriptors were derived for a set of optimised amino acid structures, with the purpose of establishing quantitative structure-property relationship (QSPR) models to predict aminograms for 100% natural fresh juices and concentrates of Navel and Valencia oranges, and Eureka lemon. We used the statistical replacement method technique for designing the best multi-parametric linear regression models, which included structural features selected from a pool containing 1497 constitutional, topological, geometrical, or electronic types of molecular descriptors. The prediction results achieved in this work were in most cases in good agreement with experimental amino acid profiles obtained in our laboratories by a validated HPLC procedure, thus demonstrating the predictive power of the designed QSPR. The developed approach is of practical value, especially when it is not possible to assign the analyte concentration with an accurate degree of certainty.Fil: Pomilio, Alicia Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Bioquímica y Medicina Molecular. Universidad de Buenos Aires. Facultad Medicina. Instituto de Bioquímica y Medicina Molecular; ArgentinaFil: Giraudo, Miguel Angel Domingo. Universidad Nacional de Lanús; ArgentinaFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaElsevier2010-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/67590Pomilio, Alicia Beatriz; Giraudo, Miguel Angel Domingo; Duchowicz, Pablo Román; Castro, Eduardo Alberto; QSPR analyses for aminograms in food: Citrus juices and concentrates; Elsevier; Food Chemistry; 123; 3; 12-2010; 917-9270308-8146CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodchem.2010.04.082info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0308814610005583info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:18:31Zoai:ri.conicet.gov.ar:11336/67590instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 12:18:31.306CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv QSPR analyses for aminograms in food: Citrus juices and concentrates
title QSPR analyses for aminograms in food: Citrus juices and concentrates
spellingShingle QSPR analyses for aminograms in food: Citrus juices and concentrates
Pomilio, Alicia Beatriz
Amino Acids
Aminograms
Citrus Juices And Concentrates
Eureka Lemon
Multi-Variable Linear Regression
Navel And Valencia Oranges
Qspr Theory
Replacement Method
title_short QSPR analyses for aminograms in food: Citrus juices and concentrates
title_full QSPR analyses for aminograms in food: Citrus juices and concentrates
title_fullStr QSPR analyses for aminograms in food: Citrus juices and concentrates
title_full_unstemmed QSPR analyses for aminograms in food: Citrus juices and concentrates
title_sort QSPR analyses for aminograms in food: Citrus juices and concentrates
dc.creator.none.fl_str_mv Pomilio, Alicia Beatriz
Giraudo, Miguel Angel Domingo
Duchowicz, Pablo Román
Castro, Eduardo Alberto
author Pomilio, Alicia Beatriz
author_facet Pomilio, Alicia Beatriz
Giraudo, Miguel Angel Domingo
Duchowicz, Pablo Román
Castro, Eduardo Alberto
author_role author
author2 Giraudo, Miguel Angel Domingo
Duchowicz, Pablo Román
Castro, Eduardo Alberto
author2_role author
author
author
dc.subject.none.fl_str_mv Amino Acids
Aminograms
Citrus Juices And Concentrates
Eureka Lemon
Multi-Variable Linear Regression
Navel And Valencia Oranges
Qspr Theory
Replacement Method
topic Amino Acids
Aminograms
Citrus Juices And Concentrates
Eureka Lemon
Multi-Variable Linear Regression
Navel And Valencia Oranges
Qspr Theory
Replacement Method
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Dragon theoretical descriptors were derived for a set of optimised amino acid structures, with the purpose of establishing quantitative structure-property relationship (QSPR) models to predict aminograms for 100% natural fresh juices and concentrates of Navel and Valencia oranges, and Eureka lemon. We used the statistical replacement method technique for designing the best multi-parametric linear regression models, which included structural features selected from a pool containing 1497 constitutional, topological, geometrical, or electronic types of molecular descriptors. The prediction results achieved in this work were in most cases in good agreement with experimental amino acid profiles obtained in our laboratories by a validated HPLC procedure, thus demonstrating the predictive power of the designed QSPR. The developed approach is of practical value, especially when it is not possible to assign the analyte concentration with an accurate degree of certainty.
Fil: Pomilio, Alicia Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Bioquímica y Medicina Molecular. Universidad de Buenos Aires. Facultad Medicina. Instituto de Bioquímica y Medicina Molecular; Argentina
Fil: Giraudo, Miguel Angel Domingo. Universidad Nacional de Lanús; Argentina
Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
description Dragon theoretical descriptors were derived for a set of optimised amino acid structures, with the purpose of establishing quantitative structure-property relationship (QSPR) models to predict aminograms for 100% natural fresh juices and concentrates of Navel and Valencia oranges, and Eureka lemon. We used the statistical replacement method technique for designing the best multi-parametric linear regression models, which included structural features selected from a pool containing 1497 constitutional, topological, geometrical, or electronic types of molecular descriptors. The prediction results achieved in this work were in most cases in good agreement with experimental amino acid profiles obtained in our laboratories by a validated HPLC procedure, thus demonstrating the predictive power of the designed QSPR. The developed approach is of practical value, especially when it is not possible to assign the analyte concentration with an accurate degree of certainty.
publishDate 2010
dc.date.none.fl_str_mv 2010-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/67590
Pomilio, Alicia Beatriz; Giraudo, Miguel Angel Domingo; Duchowicz, Pablo Román; Castro, Eduardo Alberto; QSPR analyses for aminograms in food: Citrus juices and concentrates; Elsevier; Food Chemistry; 123; 3; 12-2010; 917-927
0308-8146
CONICET Digital
CONICET
url http://hdl.handle.net/11336/67590
identifier_str_mv Pomilio, Alicia Beatriz; Giraudo, Miguel Angel Domingo; Duchowicz, Pablo Román; Castro, Eduardo Alberto; QSPR analyses for aminograms in food: Citrus juices and concentrates; Elsevier; Food Chemistry; 123; 3; 12-2010; 917-927
0308-8146
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodchem.2010.04.082
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0308814610005583
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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