Determination of cannabinoids in hemp nut products in Taiwan by HPLC‐MS/MS coupled with chemometric analysis: quality evaluation and a pilot human study
Bibliographic record
Abstract
Hemp nuts are mature cannabis seeds obtained after shelling and that are commonly used in traditional Chinese medicine for treating functional constipation. In this work, we screened hemp nut products, classified them, and verified the legality of consuming them. A total of 18 products were purchased from Taiwan, China, and Canada. Validated high‐performance liquid chromatography with tandem mass spectrometry methods were developed for analyzing the cannabinoid (i.e., Δ9‐tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabinol) content of the products and the concentration of urinary 11‐nor‐9‐carboxy‐THC. Chemometric techniques, namely hierarchical clustering analysis (HCA) and principal component analysis (PCA), were applied for rapidly classifying 11 concentrated powder products in Taiwan. A pilot human study comprising single and multiple administrations of a product with 1.5 µg/g of THC was conducted to examine the urinary 11‐nor‐9‐carboxy‐THC concentration. Through optimization of 32 full factorial design, using 60% isopropanol as the extraction solvent exhibited the highest yield of cannabinoids and was applied as the optimal condition in further analysis. The results of HCA and PCA on quality evaluation were in good agreement; however, the tested products possessed distinct CBD‐to‐THC ratios which ranged widely from 0.1:1 to 46.8:1. Particularly, the products with CBD‐to‐THC ratios higher than 1:1 were the majority in Taiwan. Our data suggested that all the tested hemp nut products met the Taiwan restriction criterion of 10 µg/g of THC. We propose a usual consumption amount of hemp nut products in Taiwan would unlikely to violate the cut‐off point of 15 ng/mL of urinary 11‐nor‐9‐carboxy‐THC. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".