Trimethylation enhancement using diazomethane (TrEnDi) enables enhanced detection of glufosinate and 3-(methylphosphinico)propionic acid from complex canola samples
Bibliographic record
Abstract
Over the past century, agriculture practices have transitioned from manual cultivation to the use of chemical herbicides for improved crop yields. The most common weedkiller glyphosate (GLY) has been used exponentially worldwide, leading to the emergence of GLY-resistant weeds. This has prompted a need for effective alternatives, leading to the development and increasing use of phosphinothricin, also known as glufosinate (GLUF). As the agricultural application of GLUF rises, the potential for long-term residual exposure in the food chain increases, highlighting the need for improved analytical strategies for its detection, as well as its main breakdown product 3-(methylphosphinico)propionic acid (MPPA). Chemical derivatization strategies have been developed to improve the detection of GLUF and MPPA via liquid chromatography tandem mass spectrometry (LCMS) analyses. In this article, we expand the use of trimethylation enhancement using diazomethane (TrEnDi) to quantitatively derivatize these analytes into permethylated GLUF ([GLUFTr]+) and MPPA ([MPPATr+H]+). Comparing [GLUFTr]+ and [MPPATr+H]+ to underivatized counterparts, TrEnDi yields 2.8-fold and 1.7-fold improvements in reversed-phase chromatographic retention, respectively, while MS-based sensitivity is enhanced 4.1-fold and 11.0-fold, respectively. Initial analytical improvements were performed on commercial standards; however, successful analyte derivatization (with >99% yields) was also demonstrated on a commercial herbicide solution imparting consistent analytical enhancements. To investigate the benefits of TrEnDi in a bona fide agricultural scenario, the quantities of GLUF and MPPA were determined in aqueous extracts from field-grown canola plants before and after TrEnDi derivatization. In their underivatized forms, GLUF and MPPA were undetectable in all field samples whereas [GLUFTr]+ and [MPPATr+H]+ were readily quantifiable using the same analysis conditions. Our results demonstrate that TrEnDi continues to be a useful tool to enhance the analytical characteristics of organic molecules that are traditionally difficult to detect.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".