A new QSPR study on relative sweetness


Abstract:

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 …

Año de publicación:

2016

Keywords:

    Fuente:

    googlegoogle

    Tipo de documento:

    Other

    Estado:

    Acceso abierto

    Áreas de conocimiento:

    • Relación cuantitativa estructura-actividad

    Áreas temáticas de Dewey:

    • Química analítica
    • Biología
    • Ingeniería química
    Procesado con IAProcesado con IA

    Objetivos de Desarrollo Sostenible:

    • ODS 3: Salud y bienestar
    • ODS 12: Producción y consumo responsables
    • ODS 9: Industria, innovación e infraestructura
    Procesado con IAProcesado con IA

    Contribuidores: