Does it burn? The effect of guar gum addition on ginger beer's sensory properties
Bibliographic record
Abstract
Summary Consumer demand for ginger beer has grown within the last few years due to health benefits associated with ginger consumption identified in recent studies and its low‐calorie content. However, non‐alcoholic ginger beer, like other non‐alcoholic beverages, does not possess the same mouthfeel as its alcoholic counterparts. As such, the aim of this study was to evaluate how the addition of guar gum impacted the sensory perception, spiciness, and consumer acceptability of non‐alcoholic ginger beer. Two different formulations of ginger beer were created, one without the addition of guar gum (control) and the other with 1.9 g/L guar gum added. Samples, along with carbonated water, were presented in pairs with a 20‐s wait and no‐rinse in between to observe sensitisation and desensitisation. The participants (n = 103) evaluated each sample for spiciness, burning or stinging sensation, along with bitterness, sweetness, sourness, overall flavour intensity, liking of flavour and mouthfeel, and overall liking. The addition of guar gum significantly impacted the perception of spiciness, burning, and stinging sensation in addition to the overall flavour intensity. The guar gum addition also negatively impacted the acceptability of the ginger beer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".