Sample preparation for the analysis of key metabolites from cannabinoids biosynthesis in phytoplankton using gas chromatography–mass spectrometry
Bibliographic record
Abstract
Abstract Cannabinoids biosynthesis in phytoplankton has attracted much attention due to the rapid development of genetic tools and the optimization of genetic transformation methods in microalgae. To monitor the biosynthesis process, proper sample preparation and practical instrumental methods are needed to measure the various precursors, intermediates, cannabinoids, and their degradation products. The objective of this study was to develop a sample preparation procedure for the quantification of olivetolic acid (OA), cannabigerolic acid (CBGA), cannabidiolic acid (CBDA), tetrahydrocannabinolic acid (THCA), olivetol (OL), cannabidiol (CBD), and tetrahydrocannabinol (THC) using single‐quadrupole gas chromatography–mass spectrometry (GC–MS). Isochrysis galbana was used as the model matrix. After methanol extraction, samples were purified using solid phase extraction (SPE), silylated with N‐methyl‐N‐(trimethylsilyl)trifluoroacetamide, and analyzed using GC–MS in electron ionization mode. A strong anion‐exchange SPE efficiently recovered OA, CBGA, CBDA, and THCA. A graphitized carbon black SPE was necessary to purify OL, CBD, and THC. Both columns removed amino acids, sugars, polyols, and pigments from the algae extract and prepared samples that are suitable for silylation and GC–MS analysis. The total protocol, including solvent extraction, SPE, silylation, and GC–MS analysis, was validated in accordance with the ICH guidelines. Performance characteristics of our method are superior to existing protocols with similar complexity in the literature.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".