Utilization of Nutmeg (Myristica fragrans Houtt.) Seed Hydrodistillation Time to Produce Essential Oil Fractions with Varied Compositions and Pharmacological Effects
Bibliographic record
Abstract
The intent of this study was to utilize distillation timeframes (DT) of nutmeg (Myristica fragrans) essential oil (EO) to generate fractions with differential chemical compositions and bioactivity. Ten fractions were captured at the following distillation timeframes: 0.0–0.5, 0.5–1.0, 1.0–2.5, 2.5–5.0, 5.0–10, 10–30, 30–60, 60–90, 90–120, and 120–240 min. In addition, a control EO was collected from a straight 0–240 min non-stop distillation. ANOVA and advanced regression modeling revealed that the produced EO fractions possess substantial variation in the concentration of potentially desired compounds. The concentrations (%) of α-phellandrene, 3-carene, p-cymene, limonene, α-thujene, α-pinene, camphene, sabinene, β-pinene, and myrcene decreased, while the concentrations (%) of α-terpinene, γ-terpinene, terpinolene, and myristicin increased in later DT fractions. Nutmeg EO showed some antimalarial activity against Plasmodium falciparum D6, but did not exhibit significant antifungal activity. In general, nutmeg seed oil yields increased with an increase of DT. These results may be utilized by industries using nutmeg EO.
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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 | 0.000 | 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".