Prediction of Sweetness by Multilinear Regression Analysis and Support Vector Machine

The sweetness of a compound is of large interest for the food additive industry. In this work, 2 quantitative models were built to predict the logSw (the logarithm of sweetness) of 320 unique compounds with a molecular weight from 132 to 1287 and a sweetness from 22 to 22500000. The whole dataset wa...

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Veröffentlicht in:	Journal of food science 2013-09, Vol.78 (9), p.S1445-S1450
Hauptverfasser:	Zhong, Min, Chong, Yang, Nie, Xianglei, Yan, Aixia, Yuan, Qipeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Additives Biological and medical sciences Correlation analysis Evaluation Studies as Topic Food additives Food industries food properties Food science Food Technology - methods Foods Fundamental and applied biological sciences. Psychology General aspects Linear Models Mathematical models Models, Chemical Molecular weight multilinear regression (MLR) Quantitative Structure-Activity Relationship quantitative structure-activity relationships (QSAR) Regression Regression Analysis Support Vector Machine support vector machine (SVM) Support vector machines sweeteners Sweetening Agents - chemistry Sweets Taste
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