Improved Chromatography and MS-Based Detection of Glyphosate and Aminomethylphosphonic Acid Using iTrEnDi
Bibliographic record
Abstract
Glyphosate (GLY), a synthetic, nonselective systemic herbicide that is particularly effective against perennial weeds, is the most used weedkiller in the world. There are growing concerns over GLY accumulation in the environment and the attendant human health-associated risks, and despite increased attention in the media, GLY and its breakdown product aminomethylphosphonic acid (AMPA) remain elusive to many analytical strategies. Chemical derivatization coupled with high-performance liquid chromatography-mass spectrometry (HPLC-MS) addresses the challenge of quantifying low levels of GLY and AMPA in complex samples. Here we demonstrate the use of in situ trimethylation enhancement using diazomethane (iTrEnDi) to derivatize GLY and AMPA into permethylated products ([GLY Tr ] + and [AMPA Tr ] +, respectively) prior to analysis via HPLC-MS. iTrEnDi produced quantitative yields and resulted in a 12–340-fold increases in HPLC-MS-based sensitivity for [GLY Tr ] + and [AMPA Tr ] +, respectively, compared with underivatized counterparts. The limits of detection of derivatized compounds were found to be 0.99 ng/L for [GLY Tr ] + and 1.30 ng/L for [AMPA Tr ] +, demonstrating significant sensitivity improvements compared to previously established derivatization techniques. iTrEnDi is compatible with the direct derivatization of Roundup formulations. Finally, as proof of principle, a simple aqueous extraction followed by iTrEnDi enabled the detection of [GLY Tr ] + and [AMPA Tr ] + on the exterior of field-grown soybeans that were sprayed with Roundup. Overall, iTrEnDi ameliorates issues relating to low proton affinity and chromatographic retention, boosting HPLC-MS-based sensitivity and enabling the elucidation of elusive analytes such as GLY and AMPA within agricultural systems.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".