Quantitation of Select Terpenes/Terpenoids and Nicotine Using Gas Chromatography–Mass Spectrometry with High-Temperature Headspace Sampling
Bibliographic record
Abstract
Plants are the main sources of many high-value bioactive terpenoids used in the medical, fragrance, and food industries. Increasing demand for these bioactive plants and their derivative products (e.g., cannabis and extracts thereof) requires robust approaches to verify feedstock, identify product adulteration, and ensure product safety. Reported here are single-laboratory validation details for a robust testing method to quantitate select terpenes and terpenoids in dry plant materials and terpenoid-containing vaping liquids (e.g., a derivative product) using high-temperature headspace gas chromatography-mass spectrometry, with glycerol used as a headspace solvent. Validated method recoveries were 75-103%, with excellent repeatability (relative standard deviation (RSD) < 5%) and intermediate precision (RSD < 12%). The use of high-temperature headspace (180 °C) permitted terpene and terpenoid profiles to be monitored at temperatures consistent with vaping conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".