Wine Oligosaccharides: Underutilized or Irrelevant? A Study into the Effects of Oligosaccharides on Wine Taste and Mouthfeel
Bibliographic record
Abstract
The taste and mouthfeel of a wine are two of the most important aspects of wine tasting. However, while much is known about phenolic compounds and other macromolecules direct effects on wine taste and mouthfeel, little is known about other wine compounds such as oligosaccharides. This experiment uses Fructo-oligosaccharide (FOS) and Galacto-oligosaccharide (GOS) at two different concentrations, 450 mg/L and 900 mg/L within a simple model wine matrix. A model matrix was used to control for any unknown interactions between oligosaccharides and the multitude of wine components. Oligosaccharides were added individually to the model wine matrix at each concentration to create four treatments. Triangle tests were performed on all treatments against the control base model wine and between the high and low concentrations of each oligosaccharide treatment. Following the triangle tests, each treatment and the control underwent descriptive analysis (DA) using line intensity scales for sweetness, bitterness, astringency, acidity, and viscosity. Triangle test results revealed a significant difference only between the FOS450 and FOS900 samples. The wine matrix was made more complicated by adding polyphenols and still, none of the four oligosaccharide treatment groups were found to be significantly different. DA found no significant differences for the five attributes but did show clear trends in increased sweetness and acidity, decreased bitterness, as well as changes to astringency and viscosity. This suggests there may be more complex interactions happening within the mouth. However, given the lack of significant results in the simple wine model and the more complex wine model wine, any complex interactions between oligosaccharides and other wine compounds are likely to be minimal.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".