MétaCan
Menu
Back to cohort
Record W2327933822 · doi:10.5740/jaoacint.12-322

Development and Method Validation for the Determination of Nitroimidazole Residues in Salmon, Tilapia and Shrimp Muscle

2014· article· en· W2327933822 on OpenAlexafffund
Lynn Watson, Ross A Potter, James D. MacNeil, Cory Murphy

Bibliographic record

VenueJournal of AOAC International · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsShrimpNitroimidazoleTilapiaFisheryFish <Actinopterygii>ChemistryBiology

Abstract

fetched live from OpenAlex

The use of nitroimidazoles in aquacultured fish has been banned in many countries due to the suspected mutagenic and carcinogenic effects of these compounds. In response to the need to conduct residue testing of these compounds in fish, a simple, rapid, and sensitive method was developed and validated that is suitable for regulatory monitoring of nitroimidazole residues and their hydroxy metabolites in fish muscle tissue. Following solvent extraction of homogenized tissue and clean-up using a C18 SPE cartridge, analyses were conducted by ultra-performance UPLC-MS/MS. A precursor ion and two product ions were monitored for each of the parent compounds and metabolites included in the method. The validated method has an analytical range from 1 to 50 ng/g in the representative species (tilapia, salmon, and shrimp), with an LOD and LOQ ranging from 0.07 to 1.0 nglg and 0.21 to 3.0 nglg, respectively, depending on the analyte. Recoveries ranged from 81 to 124% and repeatability was between 4 and 17%. HorRat values were within typical limits of acceptability for a single laboratory. Working standards were stable for 12 months, extracts were stable for 5 days, and tissues for 2 months under appropriate storage conditions. This method was determined to be suitable for routine use for screening, quantification, and confirmation of nitroimidazole residues in a residue monitoring program for fish with regulatory oversight.

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)0.001
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 score0.939
Threshold uncertainty score0.062

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.280
Teacher spread0.258 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of AOAC InternationalSame topicPesticide Residue Analysis and SafetyFrench-language works237,207