Single-run separation and quantification of 14 cannabinoids using affinity non-aqueous capillary electrophoresis
Bibliographic record
Abstract
Abstract The legalization of cannabis has magnified the importance of determining the quantity and identification of cannabinoids. Both industry and consumers are highly interested in the content of cannabinoids available in their products, while health care professionals and regulators are concerned with the safety of cannabis. Quantification of major cannabinoids in products answers some of these concerns. Currently, popular methods of quantifying cannabinoids use high-pressure liquid chromatography (HPLC). Still, these HPLC methods are limited to quantifying a small number of cannabinoids unless more powerful but more costly instruments are employed to achieve better analysis, such as UHPLC and mass spectrometry. We propose a quick method that successfully separates and quantifies 14 cannabinoids in a single run using capillary electrophoresis (CE) coupled with a UV detector in 25 minutes. Our CE method demonstrated the limit of detection between 1.2–1.8 µg/mL, with the detection range reaching up to 50 µg/mL.
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) | 0.001 | 0.000 |
| 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".