Prediction of Sweetness by Multilinear Regression Analysis and Support Vector Machine
- Author
- 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 - Source
Journal of food science. 2013, Vol 78, Num 7-9 ; S1445-S1450 ; ref : 1/4 p
- CODEN
- JFDSAZ
- ISSN
- 0022-1147
- Scientific domain
- Food science technology
- Publisher
- Wiley, Hoboken, NJ
- Publication country
- United States
- Document type
- Article
- Language
- English
- Author keyword
- food properties multilinear regression (MLR) quantitative structure―activity relationships (QSAR) support vector machine (SVM) sweeteners
- Keyword (fr)
- Aliment Analyse régression Edulcorant Propriété Prédiction Relation structure activité Support Vecteur Additif alimentaire
- Keyword (en)
- Food Regression analysis Sweetener Properties Prediction Structure activity relation Support Vector Food additive
- Keyword (es)
- Alimento Análisis regresión Edulcorante Propiedad Predicción Relación estructura actividad Soporte Vector Aditivo alimentario
- Classification
- Pascal
- 002 Biological and medical sciences / 002A Fundamental and applied biological sciences. Psychology / 002A35 Food industries / 002A35A General aspects / 002A35A08 Food additives
- Discipline
- Agrifood industries
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 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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