Towards the development of a MALDI/TOF-MS Fingerprint Library for the Identification and Differentiation of Cannabis Extracts
Bibliographic record
Abstract
The legalization of recreational cannabis in Canada has resulted in an increased interest for research on the plant within the scientific community. Currently in Canada, there is no requirement for the confirmation of cultivar/strain identity for cannabis that is sold in recreational distributors. Strains of cannabis differ through their chemical make-up which result in different pharmacological effects. The aim of this research is to develop a method using sequential extraction and matrix assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) and Brukers BiotyperTM software that uses cannabis extracts to fingerprint and distinguish strains purchased from the recreational market. Cannabis was first extracted sequentially using methanol, acetonitrile and hexane to obtain a range of biomolecules from cannabis. Using a methanol extract, BiotyperTM software was optimized to generate peak lists that would include the maximum number of distinguishable peaks in the desired mass range. Then, using generated score values and dendrograms, MALDI mass spectra of strain extracts were compared. It was found that the spectra of the methanol and acetonitrile extracts contained previously observed peaks that are consistent with cannabinoids. Also, some consistent patterns could be found using the methanol and acetonitrile extracts from cannabis. The use of hexane as an extracting solvent was less practical as it is not miscible with the optimized matrix solution. Biotyper software was sometimes consistent but overall showed discrepancies for how peaks were matched. Further work for this research includes the use of tandem mass spectrometry (MS/MS) to determine if matched pairs of peaks are truly identical. Another avenue for future work is method exploiting high iii molecular weight (MW) cannabis proteins for fingerprinting instead of low MW compounds as described in this work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".