A New QSPR Study on Relative Sweetness

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 train...

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Autores principales: Rojas Villa, Cristian Xavier, Tripaldi, Piercosimo, Duchowicz, Pablo Román
Formato: Articulo
Lenguaje:Inglés
Publicado: 2016
Materias:
Ciencias Exactas
Dragon Software
k-Means Cluster Analysis
QSPR Theory
Relative Sweetness
Replacement Method
Sweeteners
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/108337
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-108337
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Exactas
Dragon Software
k-Means Cluster Analysis
QSPR Theory
Relative Sweetness
Replacement Method
Sweeteners
spellingShingle Ciencias Exactas
Dragon Software
k-Means Cluster Analysis
QSPR Theory
Relative Sweetness
Replacement Method
Sweeteners
Rojas Villa, Cristian Xavier
Tripaldi, Piercosimo
Duchowicz, Pablo Román
A New QSPR Study on Relative Sweetness
topic_facet Ciencias Exactas
Dragon Software
k-Means Cluster Analysis
QSPR Theory
Relative Sweetness
Replacement Method
Sweeteners
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.
format Articulo
Articulo
author Rojas Villa, Cristian Xavier
Tripaldi, Piercosimo
Duchowicz, Pablo Román
author_facet Rojas Villa, Cristian Xavier
Tripaldi, Piercosimo
Duchowicz, Pablo Román
author_sort Rojas Villa, Cristian Xavier
title A New QSPR Study on Relative Sweetness
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 new qspr study on relative sweetness
publishDate 2016
url http://sedici.unlp.edu.ar/handle/10915/108337
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AT duchowiczpabloroman anewqsprstudyonrelativesweetness
AT rojasvillacristianxavier newqsprstudyonrelativesweetness
AT tripaldipiercosimo newqsprstudyonrelativesweetness
AT duchowiczpabloroman newqsprstudyonrelativesweetness
bdutipo_str Repositorios
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