Hydrodistillation Extraction Time Effect on Essential Oil Yield, Composition, and Bioactivity of Coriander Oil
Bibliographic record
Abstract
Coriander (Coriandrum sativum L.) is a major essential oil crop grown throughout the world. Coriander essential oil is extracted from coriander fruits via hydrodistillation, with the industry using 180-240 min of distillation time (DT), but the optimum DT for maximizing essential oil yield, composition of constituents, and antioxidant activities are not known. This research was conducted to determine the effect of DT on coriander oil yield, composition, and bioactivity. The results show that essential oil yield at the shorter DT was low and generally increased with increasing DT with the maximum yields achieved at DT between 40 and 160 min. The concentrations of the low-boiling point essential oil constituents: α-pinene, camphene, β-pinene, myrcene, para-cymene, limonene, and γ-terpinene were higher at shorter DT (< 2.5 min) and decreased with increasing DT; but the trend reversed for the high-boiling point constituents: geraniol and geranyl-acetate. The concentration of the major essential oil constituent, linalool, was 51% at DT 1.15 min, and increased steadily to 68% with increasing DT. In conclusion, 40 min DT is sufficient to maximize yield of essential oil; and different DT can be used to obtain essential oil with differential composition. Its antioxidant capacity was affected by the DT, with 20 and 240 min DT showing higher antioxidant activity. Comparisons of coriander essential oil composition must consider the length of the DT.
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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) | 0.001 | 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) | 0.001 | 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".