Grinding and Fractionation during Distillation Alter Hemp Essential Oil Profile and Its Antimicrobial Activity
Bibliographic record
Abstract
The hypothesis of this study was that we can modify the essential oil (EO) profile of hemp (Cannabis sativa L.) and obtain fractions with differential composition and antimicrobial activity. Therefore, the objective was to evaluate the effects of grinding of hemp biomass before EO extraction and fractionation during distillation on EO profile and antimicrobial activity. The study generated a several EO fractions with a diversity of chemical profile and antimicrobial activity. The highest concentrations of β-pinene and myrcene in the EO can be obtained in the 5–10 min distillation time (DT) of ground material or in the 80–120 min DT of nonground material. High δ-3-carene and limonene EO can be obtained from 0–5 min DT fraction of nonground material. High eucalyptol EO can be sampled either in the 0–5 min DT of the ground material or in the 80–120 min of nonground material. Overall, the highest concentrations of β-caryophyllene, α-(E)-bergamotene, (Z)-β-farnesene, α-humulene, caryophyllenyl alcohol, germacrene D-4-ol, spathulenol, caryophyllene oxide, humulene epoxide 2, β-bisabolol, α-bisabolol, sesquiterpenes, and cannabidiol (CBD) can be obtained when EO is sampled in the 80–120 min DT and the material is nonground. Monoterpenes in the hemp EO can be increased twofold to 85% by grinding the material prior to distillation and collecting the EO in the first 10 min. However, grinding resulted in a slight but significant decrease in the CBD concentration of the EO. CBD-rich oil can be produced by collecting at 120–180 min DT. Different EO fractions had differential antimicrobial activity. The highest antimicrobial activity of EO fraction was found against Staphylococcus aureus subsp. aureus. THC-free EO can be obtained if the EO distillation is limited to 120 min. The results can be utilized by the hemp processing industry and by companies developing new hemp EO-infused products, including perfumery, cosmetics, dietary supplements, food, and pharmaceutical industries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".