Prediction of Sweetness by Multilinear Regression Analysis and Support Vector Machine
- Autor
- MIN ZHONG1 ; YANG CHONG1 ; XIANGLEI NIE1 ; AIXIA YAN1 ; QIPENG YUAN1
[1] State Key Laboratory of Chemical Resource Engineering, Dept. of Pharmaceutical Engineering, Beijing Univ. of Chemica Technology, Beijing, China - Fuente
Journal of food science. 2013, Vol 78, Num 7-9 ; S1445-S1450 ; ref : 1/4 p
- CODEN
- JFDSAZ
- ISSN
- 0022-1147
- Campo Científico
- Food science technology
- Editor
- Wiley, Hoboken, NJ
- País de la publicación
- United States
- Tipo de documento
- Article
- Idioma
- English
- Palabra clave de autor
- food properties multilinear regression (MLR) quantitative structure―activity relationships (QSAR) support vector machine (SVM) sweeteners
- Palabra clave (fr)
- Aliment Analyse régression Edulcorant Propriété Prédiction Relation structure activité Support Vecteur Additif alimentaire
- Palabra clave (in)
- Food Regression analysis Sweetener Properties Prediction Structure activity relation Support Vector Food additive
- Palabra clave (es)
- Alimento Análisis regresión Edulcorante Propiedad Predicción Relación estructura actividad Soporte Vector Aditivo alimentario
- Clasificación
- Pascal
- 002 Biological and medical sciences / 002A Fundamental and applied biological sciences. Psychology / 002A35 Food industries / 002A35A General aspects / 002A35A08 Food additives
- Disciplina
- Agrifood industries
- Procedencia
- Inist-CNRS
- Base de datos
- PASCAL
- Identificador INIST
- 27761984
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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