Essential Oil Profile Diversity in Cardamom Accessions From Southern India
Bibliographic record
Abstract
The essential oil of cardamom capsules is a high-value ingredient in foods, beverages, perfumery, and traditional medicines. It is responsible for the characteristic aroma of cardamom. The present study aimed to evaluate essential oil yield and chemical constituents of 22 diverse accessions of cardamom. A total of 20 g of the cured capsules were hydrodistilled in a Clevenger apparatus for 3 h in three replications. The amount of essential oil yield ranged from 4.5 to 9.5%, indicating a substantial variation in this feature among the accessions. The GC/MS analysis results discovered 24 constituents that constituted 98.1–100% of total essential oil. The main fractions were found to be oxygenated monoterpenes (40.7–66.7%), monoterpene hydrocarbons (23.1–58.6%), and sesquiterpenes (0.1–2.0%). Among the monoterpenoids, the predominant constituents were α-terpinyl acetate (29.9–61.3%) followed by 1,8-cineole (15.2–49.4%), α-terpineol (0.83–13.2%), β-linalool (0.44–11.0%), and sabinene (1.9–4.9%). Two sesquiterpene constituents, cardinen and nerolidol and p-cresol (a phenol derivative) were also identified. The compositional data were subjected to euclidean-distance-based similarity analysis, which showed two major clusters. The major constituents of cardamom essential oil (CEO) are 1,8-cineole, α-terpinyl acetate, sabinene, and β-linalool that can be used in food, aroma, and pharmaceutical applications.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".