Sweetness profiles of glycosylated rebaudioside A and its binary mixtures with allulose and maltitol

Rebaudioside A is a promising natural alternative sweetener but they produce increased bitterness, astringency, and unpleasant aftertastes. Glycosylation and blending with different sweeteners are known to improve the sensory characteristics of rebaudioside A. The present study was conducted to identify the relative sweetness and sensory profile of glycosyl rebaudioside A (g-reb A). The relative sweetness of g-reb A compared to 5% sucrose was determined using the two-alternative forced choice method. The sensory profiles of g-reb A and its mixtures with allulose and maltitol (1:1 ratio) were compared to those of rebaudioside A, rebaudioside D, rebaudioside M, sucralose, allulose, maltitol, and sucrose using descriptive analysis conducted by eight trained panelists. The relative sweetness of g-reb A was 155, which was lower than that of rebaudioside A. In addition, the bitter taste and aftertaste, astringency, and sweet onset of g-reb A were decreased compared to those of rebaudioside A.

A New QSPR Study on Relative Sweetness

The aim of this work was to develop predictive structure-property relationships (QSPR) of natural and synthetic sweeteners in order to predict and model relative sweetness (RS). The data set was composed of 233 sweeteners collected from diverse sources in the literature, which was divided into training (163) and test (70) molecules according to a procedure based on k-means cluster analysis. A total of 3763 non-conformational Dragon molecular descriptors were calculated which were simultaneously analyzed through multivariable linear regression analysis coupled with the replacement method variable subset selection technique. The established six-parameter model was validated through the cross-validation techniques, together with Y-randomization and applicability domain analysis. The results for the training set and the test set showed that the non-conformational descriptors offer relevant information for modeling the RS of a compound. Thus, this model can be used to predict the sweetness of both un-evaluated and un-synthesized sweeteners.