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Record W318638857 · doi:10.1093/jaoac/92.1.181

Determination of Deoxynivalenol in Soft Wheat by Immunoaffinity Column Cleanup and LC-UV Detection: Interlaboratory Study

2009· article· en· W318638857 on OpenAlexaffabout
Gary Neumann, Gary A Lombaert, S. Kotello, Nicole Fedorowich, El‐Sayed M. Abdel‐Aal, J Cea, K Kurz, Bruce Malone, E. Marley, Giuseppe Panzarini, Shiv Singh Patel, Margaret Honora Roscoe, Martine M. Savard, Michele Solfrizzo, R. Trelka, Eugênia Azevedo Vargas

Bibliographic record

VenueJournal of AOAC International · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsHealth Canada
Fundersnot available
KeywordsRepeatabilityReproducibilityChromatographyMathematicsChemistry

Abstract

fetched live from OpenAlex

An interlaboratory study was conducted to assess the applicability of a previously validated method for the analysis of deoxynivalenol (DON) in cereal and cereal products to soft wheat in the range of >0.1-3.0 microg/g. The study evaluated a generic method to determine DON at levels that bracket the existing Canadian guidelines for DON in soft wheat destined for use in baby foods and nonstaple foods. Collaborators selected one of 2 approved brands of DON immunoaffinity column for cleanup and their choice of qualified C18 liquid chromatographic (LC) column. Separation was by LC with UV detection. Blind duplicates from 5 levels of naturally contaminated wheat and a pair of spiked wheat samples were successfully analyzed by 12 laboratories in 8 countries. For samples naturally contaminated with DON from <0.1-2.2 microg/g, the relative standard deviation of repeatability (RSDr) ranged from 3.1 to 14.8%. For reproducibility, the RSDR ranged from 21.0 to 32.9% and the HorRat range was 1.0 to 1.9. Recoveries of 0.5 microg/g DON spiked into wheat ranged from 66 to 98%, with an average of 84%. The RSDr was 5.4%, the RSDR was 12.6%, and the HorRat value was 0.7.

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 score0.929
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.228
Teacher spread0.220 · 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

Citations17
Published2009
Admission routes2
Has abstractyes

Explore more

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