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Prediction of Sweetness by Multilinear Regression Analysis and Support Vector Machine
Author(s) -
Zhong Min,
Chong Yang,
Nie Xianglei,
Yan Aixia,
Yuan Qipeng
Publication year - 2013
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/1750-3841.12199
Subject(s) - sweetness , multilinear map , support vector machine , mathematics , regression analysis , test set , artificial intelligence , logarithm , linear regression , set (abstract data type) , pattern recognition (psychology) , chemistry , biological system , statistics , food science , computer science , flavor , biology , pure mathematics , mathematical analysis , programming language
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 225. 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.