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Record W4389303313 · doi:10.1080/19440049.2023.2289131

Determination of quinclorac and quinclorac methyl ester in honey by online SPE-UPLC-MS/MS

2023· article· en· W4389303313 on OpenAlexaff
Thomas S. Thompson, Johan P. van den Heever, Melissa J. Zarft, Snezana Dijanovic

Bibliographic record

VenueFood Additives & Contaminants Part A · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsAgriculture Food and Rural Development
Fundersnot available
KeywordsChromatographyChemistrySolid phase extractionDetection limitExtraction (chemistry)AnalyteCartridgeHigh-performance liquid chromatographyMethanolSample preparationOrganic chemistry

Abstract

fetched live from OpenAlex

A method employing online solid phase extraction (SPE) coupled to UPLC-MS/MS was developed for the determination of residues of the acid herbicide quinclorac plus its transformation product, quinclorac methyl ester, in honey. The analytical method involved dissolving the honey in a mixture of methanol:water followed by direct injection into a two-dimensional UPLC system which is used to perform an automated SPE cleanup on a reusable phenyl cartridge prior to the target analytes being transferred onto an analytical UPLC column for subsequent chromatographic separation followed by MS/MS detection. The limits of quantitation for quinclorac and quinclorac methyl ester in honey were both set at 0.5 µg kg−1 and the method detection limit was estimated to be 0.012 µg kg−1 for each compound. The working analytical range (0.5–100 µg kg−1) was validated by analysing a series of spiked replicate honey samples. The method was applied to the analysis of various honeys obtained from numerous different commercial sources. Quinclorac was detected in 9 out of 30 samples at concentrations ranging from 0.6 to 31.5 µg kg−1. Quinclorac methyl ester, which is estimated to be significantly more toxic than the parent herbicide itself, was not detected in any honey sample.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.300
Teacher spread0.260 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFood Additives & Contaminants Part ASame topicInsect and Pesticide ResearchFrench-language works237,207