Analysis of tetrahydrocannabinol derivative from cannabis‐infused chocolate by QuEChERS‐thin layer chromatography‐desorption electrospray ionization mass spectrometry
Bibliographic record
Abstract
Recently in Canada and some states of the United States, marijuana (cannabis) has become fully legalized and regulated, for both medical and recreational purposes. This fact is going to make cannabis products such as edibles even more popular than ever before. Therefore, it is assumed that there will be a high demand for analytical methods, which are accurate and sensitive enough to be used in different forensic and pharmaceutical cannabis-related applications. Cannabis derivatives have an extreme range and number of constituents with possible interactions with one another. Thus, this characteristic leads to their vast and highly complex chemistry, which requires robust analytical tools to be able to precisely and accurately quantify and qualify them. We developed and validated an analytical method using desorption electrospray ionization (DESI)-mass spectrometry (MS) to accurately detect, characterize, and quantify cannabinoids and also offer an easy, cost-effective, and reliable technique, which can be performed in a short time for infused edibles in complex matrices such as chocolate. We evaluated a quantitative analysis of tetrahydrocannabinol (THC) in cannabis-infused chocolate with thin-layer chromatography (TLC)-DESI-MS and QuEChERS extraction method. Both techniques of TLC and QuEChERS are cost-effective and can be run in short time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".