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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".