Establishment of a Mass Spectrometric Fingerprint of the Most Common Phytocannabinoids in Electrospray Ionization in Positive Ion Mode
Bibliographic record
Abstract
ABSTRACT Background Analysis of the phytocannabinoids holds significant importance because of their various pharmacological properties and potential therapeutic applications. Tandem mass spectrometry (MS/MS) coupled with electrospray ionization in positive ion mode is employed in this study to describe the collision‐induced dissociation (CID) behavior of a series of common phytocannabinoids with the aim of establishing a generalized MS/MS fingerprint. Materials and Methods Eight phytocannabinoids, namely, ∆ 9 ‐tetrahydrocannabinol (THC), cannabidiol (CBD), cannabichromene (CBC), cannabigerol (CBG), tetrahydrocannabivarin (THCV), 11‐hydroxy‐Δ 9 ‐tetrahydrocannabinol (11‐OH‐THC), 6‐hydroxy‐cannabidiol (6‐OH‐CBD), and 7‐hydroxy‐cannabidiol (7‐OH‐CBD), were studied. A Quadrupole‐Orbitrap mass spectrometer equipped with a heated electrospray ionization (HESI‐Q Orbitrap) is used to provide accurate mass measurement data for single‐stage and MS/MS analysis. In addition, a triple quadrupole‐linear ion trap mass spectrometer was used to perform MS/MS and second‐generation MS/MS (MS 3 ) analyses. Results An abundant, singly charged [M + H] + species during single‐stage MS analysis was observed for all phytocannabinoids, with mass accuracies less than 5 ppm. Because of their structural similarities, all compounds showed some common fragmentation behavior in their MS/MS analysis. By comparing the fragmentation patterns and identifying diagnostic ions, a universal MS/MS fragmentation pattern was established. The structures of the various product ions proposed in the fragmentation pathway were confirmed with exact mass measurements and MS 3 experiments. Conclusions The evaluated compounds contain varying functional groups, resulting in unique product ions, specific to each structure. The MS/MS fingerprints will be utilized in the future for the identification of new structures as well as the development of targeted quantification methods.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".