Prediction of Sweetness by Multilinear Regression Analysis and Support Vector Machine. Issue 9 (5th August 2013)

• Record Type: Journal Article
• Authors: Zhong, Min; Chong, Yang; Nie, Xianglei; Yan, Aixia; Yuan, Qipeng
• Is Part Of: Journal of food science. Volume 78:Issue 9(2013)
• Page Start: S1445
• Page End: S1450
• Publication Date: 2013-08-05
• Subjects: food properties -- multilinear regression (MLR) -- quantitative structure–activity relationships (QSAR) -- support vector machine (SVM) -- sweeteners; Food -- Periodicals; Food -- Research -- Periodicals; Food -- Periodicals; Research -- Periodicals; Levensmiddelen; Voeding; 664
• DOI: 10.1111/1750-3841.12199
• Languages: English
• ISSNs: 0022-1147

Abstract

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.