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 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. Practical Application In this study, 2 quantitative models were built based on multilinear regression and support vector machine to predict the logSw of 320 compounds. The sweet taste system of a sweetener has extensively been investigated but much still needs clarification. The quantitative models for predicting sweetness built in this work can be helpful for research in food additives.