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Record W10693944 · doi:10.1093/jaoac/89.1.214

Determination of Pesticides in Soy-Based Infant Formula Using Liquid Chromatography with Electrospray Ionization Tandem Mass Spectrometry

2006· article· en· W10693944 on OpenAlexaff
Jian Wang, Wendy Cheung

Bibliographic record

VenueJournal of AOAC International · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsChromatographyElectrospray ionizationChemistryMass spectrometryElectrosprayLiquid chromatography–mass spectrometryTandem mass spectrometryPesticide

Abstract

fetched live from OpenAlex

A sensitive method using liquid chromatography with electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) was developed and validated to quantify and confirm 13 pesticides, including aldicarb sulfoxide, aldicarb sulfone, oxamyl, methomyl, formetanate, 3-hydroxycarbofuran, carbendazim, thiabendazole, aldicarb, propoxur, carbofuran, carbaryl, and methiocarb, in soy-based infant formula. Data acquisition under MS/MS was achieved by applying multiple reaction monitoring of 2 fragment ion transitions to provide a high degree of sensitivity and selectivity for both quantitation and confirmation. Different approaches to constructing calibration curves were compared and discussed to address issues of the extraction efficiency or recovery, and matrix effects. Matrix-matched standard calibration curves with the use of isoprocarb as an internal standard were finally used to achieve the best accuracy of the method. Under most circumstances, recoveries of 13 pesticides, spiked at 5.0, 25.0, and 45.0 microg/kg, were close to 100%. The method detection limits (signal-to-noise ratio > or =3:1; microg/kg) of 13 pesticides were 0.2 for thiabendazole and methiocarb, 0.6 for aldicarb, and 0.1 for the others.

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 score0.548
Threshold uncertainty score0.159

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)0.0000.000

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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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

Citations19
Published2006
Admission routes1
Has abstractyes

Explore more

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