Quantitative determination and validation of 17 cannabinoids in cannabis and hemp using liquid chromatography-tandem mass spectrometry
Bibliographic record
Abstract
Abstract The increase in production of cannabis for medical and recreational purposes in recent years has led to a corresponding increase in laboratories performing cannabinoid analysis of cannabis and hemp. We have developed and validated a quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS) method that is simple, reliable, specific, and accurate for the analysis of 17 cannabinoids in cannabis and hemp. Liquid-solid sample extraction coupled with dilution into a calibration range from 10 to 10,000 ng/mL and LC-MS/MS analysis provides quantification of samples ranging from 0.002 to 200 mg/g (0.0002 to 20.0%) in matrix. Linearity of calibration curves in methanol was demonstrated with regression r2 ≥ 0.99. Within-batch precision (0.5 to 6.5%) and accuracy (91.4 to 108.0%) and between-batch precision (0.9 to 5.1%) and accuracy (91.5 to 107.5%) were demonstrated for quality control (QC) samples in methanol. Within-batch precision (0.2 to 3.6%) and accuracy (85.4 to 111.6%) and between-batch precision (1.4 to 6.1 %) and accuracy (90.2 to 110.3%) were also evaluated with a candidate cannabis certified reference material (CRM). Repeatability (1.5 to 12.4% RSD) and intermediate precision (2.2 to 12.8% RSD) were demonstrated via analysis of seven cannabis samples with HorRat values ranging from 0.3 to 3.1. The method provides enhanced detection limits coupled with a large quantitative range for 17 cannabinoids in plant material. It is suitable for a wide range of applications including routine analysis for delta-9-tetrahydrocannabinol (Δ9-THC), delta-9-tetrahydrocannabinolic acid (Δ9-THCA), cannabidiol (CBD), cannabidiolic acid (CBDA), and cannabinol (CBN) as well as more advanced interrogation of samples for both major and minor cannabinoids.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".