Study on Characteristic Aroma in Special Beers Brewed with Coriander (<i>Coriandrum sativum</i> L.) Seeds: Profiling of Flavor Compounds Derived from Coriander Seeds in Different Growing Areas
Bibliographic record
Abstract
The “Belgian White” style beer is generally brewed with hops, coriander seeds and orange peels as flavoring raw ingredients. Coriander ( Coriandrum sativum L.) seeds contain several terpenoids, including linalool and geraniol. Coriander seed-derived geraniol can be converted to β-citronellol during fermentation, which can form the citrus aroma found in coriander beers by sensory synergy among linalool, geraniol, and β-citronellol. However, coriander beers also have other characteristic aromas which have not yet been fully investigated. Coriander seeds harvested from different countries, including Bulgaria, Canada, Morocco, and India, imparted different flavors to the finished beer. Here, we analyzed the flavor compounds in different coriander seeds using solid-phase microextraction-gas chromatography–mass spectrometry, and found that camphor, carvone, and ( E )-anethole were unique to coriander seeds grown in Bulgaria. These compounds were also detected in beers brewed with Bulgarian coriander seeds. In addition, these compounds were revealed to enhance the flowery characteristics of beer by synergizing with excess linalool.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".