Trimethylation Enhancement using Diazomethane (TrEnDi) Enhances Mass Spectrometry-Based Analysis of Glufosinate and 3-(Methylphosphinico)propionic Acid in Complex Matrices
Bibliographic record
Abstract
Glufosinate is the second most commonly used herbicide worldwide; it inhibits glutamine synthetase, which results in increased ammonia levels in plants and mammals.Due to their high polarity, low volatility, small size and lack of chromophores and fluorophores, glufosinate and its breakdown product 3-(methylphosphinico) propionic acid (3-MPPA) are difficult to detect at trace levels.Using the chemical derivatization strategy trimethylation enhancement using diazomethane (TrEnDi), glufosinate and 3-MPPA react with diazomethane and tetrafluoroboric acid to become permethylated, thus reducing their polarity and forming a fixed permanent positive charge on the amine group of glufosinate.When using reversed-phase high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS), analyte retention and sensitivity are increased after derivatization, 6.7-fold for glufosinate and 6.3-fold for 3-MPPA.TrEnDi methodology was applied to canola samples from two separate fields sprayed with Liberty®.Prior to derivatization, the analysis showed no signal associated with unmodified glufosinate or 3-MPPA; however, TrEnDi modification resulted in quantifiable signals for both permethylated species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".