Essential oil fractions of hemp profiles at different hydro-distillation periods
Bibliographic record
Abstract
The hypothesis of this study was that hemp (Cannabis sativa L.) essential oil (EO) constituents from high-cannabinoid hemp are eluted at different times during the hydro-distillation process, allowing the generation of fractions with distinct chemical profile, with or without cannabinoids. The objective was to reveal changes in the compositional profile of EO fractions captured at ten sequential distillation timeframes (DT) and a control. Regression analysis was conducted to model the relationship between DT and 20 EO compounds, classified in four groups (monoterpenes, sesquiterpenes, cannabinoids, and others (acid esters, ketone, alcohol)) using ten DT values (5, 10, 20, 40, 80, 120, 160, 200, 240, and 280 min). The results showed that most of the EO compounds were eluted early in the distillation process, until 10 min, then the EO compounds gradually decreased until 120 min and decreased to negligible amounts after 160 min DT. Monoterpenes eluted early in the distillation process, accounting for 69.79% of the total EO collected during the 0-5 min distillation interval. After that, the monoterpenes gradually decreased to 13.77% in the 240-280 min DT, while their concentration in the 0-180 min non-stop distillation was 33.55%. Conversely, the sesquiterpenes were eluted late in the distillation process. The concentration of sesquiterpenes in the 0-5 min DT EO was 25.73%, then gradually increased to reach a plateau at 160-280 min DT (75.3-76.8%), while their concentration in the 0-180 min DT was 54.4%. The results demonstrated that hemp EO with higher concentration of monoterpenes and free of cannabinoids can be obtained by separating the initial fractions, while hemp EO with higher concentration of sesquiterpenes and cannabinoids such as cannabidiol (CBD), cannabichromene (CBC), δ8-tetrahydrocannabinol (commonly known as δ8-THC), and δ9-tetrahydrocannabinol (commonly known as δ9-THC or dronabinol) can be obtained by capturing the fraction eluted after 160 min 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) | 0.000 | 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".