A New QSPR Study on Relative Sweetness

Autores
Rojas Villa, Cristian Xavier; Tripaldi, Piercosimo; Duchowicz, Pablo Román
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The aim of this work was to develop predictive structure-property relationships (QSPR) of natural and synthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composed of 233 sweeteners collected from diverse sources in the literature, which was divided into training (163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of 3763 non-conformational Dragon molecular descriptors were calculated which were simultaneously analyzed through multivariable linear regression analysis coupled with the replacement method variable subset selection technique. The established six-parameter model was validated through the cross-validation techniques, together with Y-randomization and applicability domain analysis. The results for the training set and the test set showed that the non-conformational descriptors offer relevant information for modeling the RS of a compound. Thus, this model can be used to predict the sweetness of both un-evaluated and un-synthesized sweeteners.
Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas
Materia
Ciencias Exactas
Dragon Software
k-Means Cluster Analysis
QSPR Theory
Relative Sweetness
Replacement Method
Sweeteners
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/108337
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/108337
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A New QSPR Study on Relative SweetnessRojas Villa, Cristian XavierTripaldi, PiercosimoDuchowicz, Pablo RománCiencias ExactasDragon Softwarek-Means Cluster AnalysisQSPR TheoryRelative SweetnessReplacement MethodSweetenersThe aim of this work was to develop predictive structure-property relationships (QSPR) of natural and synthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composed of 233 sweeteners collected from diverse sources in the literature, which was divided into training (163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of 3763 non-conformational Dragon molecular descriptors were calculated which were simultaneously analyzed through multivariable linear regression analysis coupled with the replacement method variable subset selection technique. The established six-parameter model was validated through the cross-validation techniques, together with Y-randomization and applicability domain analysis. The results for the training set and the test set showed that the non-conformational descriptors offer relevant information for modeling the RS of a compound. Thus, this model can be used to predict the sweetness of both un-evaluated and un-synthesized sweeteners.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas2016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf78-93http://sedici.unlp.edu.ar/handle/10915/108337enginfo:eu-repo/semantics/altIdentifier/issn/2379-7479info:eu-repo/semantics/altIdentifier/doi/10.4018/ijqspr.2016010104info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:05:30Zoai:sedici.unlp.edu.ar:10915/108337Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:05:30.462SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A New QSPR Study on Relative Sweetness
title A New QSPR Study on Relative Sweetness
spellingShingle A New QSPR Study on Relative Sweetness
Rojas Villa, Cristian Xavier
Ciencias Exactas
Dragon Software
k-Means Cluster Analysis
QSPR Theory
Relative Sweetness
Replacement Method
Sweeteners
title_short A New QSPR Study on Relative Sweetness
title_full A New QSPR Study on Relative Sweetness
title_fullStr A New QSPR Study on Relative Sweetness
title_full_unstemmed A New QSPR Study on Relative Sweetness
title_sort A New QSPR Study on Relative Sweetness
dc.creator.none.fl_str_mv Rojas Villa, Cristian Xavier
Tripaldi, Piercosimo
Duchowicz, Pablo Román
author Rojas Villa, Cristian Xavier
author_facet Rojas Villa, Cristian Xavier
Tripaldi, Piercosimo
Duchowicz, Pablo Román
author_role author
author2 Tripaldi, Piercosimo
Duchowicz, Pablo Román
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Exactas
Dragon Software
k-Means Cluster Analysis
QSPR Theory
Relative Sweetness
Replacement Method
Sweeteners
topic Ciencias Exactas
Dragon Software
k-Means Cluster Analysis
QSPR Theory
Relative Sweetness
Replacement Method
Sweeteners
dc.description.none.fl_txt_mv The aim of this work was to develop predictive structure-property relationships (QSPR) of natural and synthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composed of 233 sweeteners collected from diverse sources in the literature, which was divided into training (163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of 3763 non-conformational Dragon molecular descriptors were calculated which were simultaneously analyzed through multivariable linear regression analysis coupled with the replacement method variable subset selection technique. The established six-parameter model was validated through the cross-validation techniques, together with Y-randomization and applicability domain analysis. The results for the training set and the test set showed that the non-conformational descriptors offer relevant information for modeling the RS of a compound. Thus, this model can be used to predict the sweetness of both un-evaluated and un-synthesized sweeteners.
Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas
description The aim of this work was to develop predictive structure-property relationships (QSPR) of natural and synthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composed of 233 sweeteners collected from diverse sources in the literature, which was divided into training (163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of 3763 non-conformational Dragon molecular descriptors were calculated which were simultaneously analyzed through multivariable linear regression analysis coupled with the replacement method variable subset selection technique. The established six-parameter model was validated through the cross-validation techniques, together with Y-randomization and applicability domain analysis. The results for the training set and the test set showed that the non-conformational descriptors offer relevant information for modeling the RS of a compound. Thus, this model can be used to predict the sweetness of both un-evaluated and un-synthesized sweeteners.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/108337
url http://sedici.unlp.edu.ar/handle/10915/108337
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2379-7479
info:eu-repo/semantics/altIdentifier/doi/10.4018/ijqspr.2016010104
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
78-93
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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