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Record W2095978945 · doi:10.1002/rcm.7381

Liquid chromatography/tandem mass spectrometry analysis of neonicotinoids in environmental water

2015· article· en· W2095978945 on OpenAlexaffabout
Chunyan Hao, David Morse, Xiaoming Zhao, Laura Sui

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsClothianidinThiaclopridChemistryAcetamipridThiamethoxamChromatographyNeonicotinoidSolid phase extractionLiquid chromatography–mass spectrometryImidaclopridTandem mass spectrometrySelected reaction monitoringExtraction (chemistry)Detection limitMass spectrometryPesticideEnvironmental chemistry

Abstract

fetched live from OpenAlex

RATIONALE: Neonicotinoids (NNIs) are the fastest expanding group of pesticides in the world over the last two decades; however, they may be a significant contributing factor to bee mortality. The widespread use of NNIs makes it critical to monitor their residuals in the environment. Published methods for NNI analysis are mainly focused on agricultural and food products, and many of them only measured a portion of the commercially available NNIs. METHODS: Utilizing a biphenyl stationary-phase column, a sensitive liquid chromatography/tandem mass spectrometry (LC/MS/MS) method was developed to determine eight NNIs, including acetamiprid, clothianidin, dinotefuran, flonicamid, imidacloprid, nitempyram, thiacloprid and thiamethoxam in environmental water. Two multiple reaction monitoring (MRM) transitions were monitored for each compound to ascertain true positive identification. Isotope-labelled NNIs, d3-acetamiprid, d3-clothianidin, d4-imidacloprid and d3-thiamethoxam, were used to compensate for extraction efficiency, matrix effects and instrument variability while monitoring real-time method performance. Target compounds in aqueous samples were analyzed by direct aqueous injection (DAI) or after solid-phase extraction (SPE). RESULTS: The method detection limits (MDLs) of NNIs in drinking water, surface water and groundwater were in the ranges of 50 to 190 and 2 to 7 ng/L for DAI and SPE procedures, respectively, and target compound recoveries ranged from 78 to 110%. The stability of target compounds in water samples and SPE extracts was also investigated for the first time to ensure accurate results. No obvious degradation was observed for target compounds within four weeks in either water samples or SPE extracts. CONCLUSIONS: The method developed for neonicotinoid pesticide analysis is very sensitive and efficient. It provides good flexibility to meet various environmental monitoring needs and is employed for an extensive study to determine the distribution of NNIs in Ontario's water.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.277
Teacher spread0.240 · 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

Citations25
Published2015
Admission routes2
Has abstractyes

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