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Record W4390046785 · doi:10.1021/jasms.3c00376

Trimethylation Enhancement Using Diazomethane (TrEnDi) Enables Enhanced Detection of Glufosinate and 3-(Methylphosphinico)propionic Acid from Complex Canola Samples

2023· article· en· W4390046785 on OpenAlexafffund
Christian A. Rosales, Krysten Sheedy, Karl V. Wasslen, Jeffrey M. Manthorpe, Jeffrey C. Smith

Bibliographic record

VenueJournal of the American Society for Mass Spectrometry · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of OntarioCanada Foundation for InnovationOntario Research FoundationCarleton University
KeywordsGlufosinateChemistryDerivatizationDiazomethaneAnalyteChromatographyDetection limitElectrosprayTandem mass spectrometryMass spectrometryOrganic chemistry

Abstract

fetched live from OpenAlex

Over the past century, agriculture practices have transitioned from manual cultivation to the use of an array of chemical herbicides for weed control including phosphinothricin, or glufosinate (GLUF). Consequently, the potential for long-term residual GLUF exposure in the food chain has increased, highlighting the need for improved analytical strategies for its detection, as well as the detection of 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 analyses. Herein, we employ trimethylation enhancement using diazomethane (TrEnDi) for the first time as a means to confer analytical advantages via quantitatively derivatizing these analytes into permethylated GLUF ([GLUF Tr ] + ) and MPPA ([MPPA Tr +H] + ). Comparing [GLUF Tr ] + and [MPPA Tr +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. Successful analyte derivatization (with >99% yields) was further demonstrated on a commercial herbicide solution imparting consistent analytical enhancements. To investigate the benefits of TrEnDi in a bona fide agricultural scenario, simple aqueous extractions from distinct parts of field-grown canola plants were performed to quantify GLUF and MPPA before and after TrEnDi derivatization. In their underivatized forms, GLUF and MPPA were undetectable in all field samples, whereas [GLUF Tr ] + and [MPPA Tr +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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.028
GPT teacher head0.271
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Society for Mass SpectrometrySame topicPesticide and Herbicide Environmental StudiesFrench-language works237,207