title: Prediction of sweetness by multilinear regression analysis and support vector machine.
abstract: 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 was randomly split into a training set including 214 compounds and a test set including 106 compounds, represented by 12 selected molecular descriptors. Then, logSw was predicted using a multilinear regression (MLR) analysis and a support vector machine (SVM). For the test set, the correlation coefficients of 0.87 and 0.88 were obtained by MLR and SVM, respectively. The descriptors found in our quantitative structure-activity relationship models are prone to a structural interpretation and support the AH/B System model proposed by Shallenberger and Acree.
authors.affiliation: State Key Laboratory of Chemical Resource Engineering, Dept. of Pharmaceutical Engineering, Beijing Univ. of Chemica Technology, Beijing, China.
authors.forename: Min
authors.initials: M
authors.lastname: Zhong
authors.forename: Yang
authors.initials: Y
authors.lastname: Chong
authors.forename: Xianglei
authors.initials: X
authors.lastname: Nie
authors.forename: Aixia
authors.initials: A
authors.lastname: Yan
authors.forename: Qipeng
authors.initials: Q
authors.lastname: Yuan
mesh_terms: D005069:Evaluation Studies as Topic; D005524:Food Technology / Q000379:methods*; D016014:Linear Models; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D060388:Support Vector Machine; D013549:Sweetening Agents / Q000737:chemistry*; D013649:Taste
chemical_list: D013549:Sweetening Agents
keywords: food properties; multilinear regression (MLR); quantitative structure-activity relationships (QSAR); support vector machine (SVM); sweeteners