Preventing Mislabeling: A Comparative Chromatographic Analysis for Classifying Medical and Industrial Cannabis
Bibliographic record
Abstract
Gas chromatography (GC) techniques for analyzing and determining the cannabinoid profile in cannabis (Cannabis sativa L.) are widely used in standard laboratories; however, these methods may mislabel the profile when used under rapid conditions. Our study aimed to highlight this problem and optimize GC column conditions and mass spectrometry (MS) parameters to accurately identify cannabinoids in both standards and forensic samples. The method was validated for linearity, selectivity, and precision. It was observed that when tetrahydrocannabinol (Δ9-THC) and cannabidiolic acid (CBD-A) were examined using rapid GC conditions, the resulting derivatives generated identical retention times. Wider chromatographic conditions were applied. The linear range for each compound ranged from 0.02 μg/mL to 37.50 μg/mL. The R2 values ranged from 0.996 to 0.999. The LOQ values ranged from 0.33 μg/mL to 5.83 μg/mL, and the LOD values ranged from 0.11 μg/mL to 1.92 μg/mL. The precision values ranged from 0.20% to 8.10% RSD. In addition, forensic samples were analyzed using liquid chromatography (HPLC-DAD) in an interlaboratory comparison test, with higher CBD and THC content than GC–MS determination (p < 0.05) in samples. Overall, this study highlights the importance of optimizing GC techniques to avoid mislabeling cannabinoids in cannabis samples.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".