Investigating the temporality of binary taste interactions in blends of sweeteners and citric acid in solution
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study investigated sweet–sour taste interactions in novel sweeteners using a 3 × 2 factorial design consisting of Sweetening System (three levels: sucrose; d ‐allulose; and a blend of d ‐allulose and Monk fruit extract) and Acidity (two levels: with or without citric acid). 110 untrained Chinese subjects participated using the temporal check‐all‐that‐apply (TCATA) method. Mixed‐model ANOVA was conducted to investigate the effect of Sweetening System, Acidity, and their interactions on the fractional Area Under the Curve within three 20 s time intervals (attack, evolution, finish). Treatments were compared using Dunnett's test with sucrose as control. Citric acid suppressed the sweet taste of both sucrose and d ‐allulose more than the blend of d ‐allulose and Monk fruit extract throughout attack and evolution time intervals. This finding was confirmed by a significant interaction between Sweetening System and Acidity for sweet taste. Sour taste was not affected differently by different Sweetening Systems or the difference in sweetener concentration. Practical Applications This study showed that the sweet taste of a blend of sweeteners could be altered by citric acid to have a similar temporal profile as sucrose in most of the evaluation time. This emphasizes the importance of not only conducting evaluations of novel sweeteners in aqueous solutions but also considering studies in more complex matrices and the choice of the methodology used to measure the sensory profile.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it