The Development and Single-Laboratory Validation of a Method for the Determination of Steroid Residues in Fish and Fish Products
Bibliographic record
Abstract
Due to potential use in aquacultured fish products, the Canadian Food Inspection Agency has identified residue testing for steroids as a priority. These compounds are used in aquaculture primarily to direct sexual differentiation with both androgens and estrogens applied depending on the desired outcome. Published research is lacking with respect to steroid residue testing in fish; however, recent studies in other matrixes provided transferable cleanup techniques. A simple, rapid, and sensitive method was developed and validated for use in monitoring aquacultured fish products for the presence of methyltestosterone, nandrolone, epi-nandrolone, boldenone, and epi-boldenone residues. The developed method consists of solvent extraction followed by cleanup using hexane and dual cartridge SPE with analysis by ultra-HPLC-MS/MS. The method is capable of detecting and confirming steroid residue levels ranging from 0.05 to 25 ng/g in salmon and tilapia, depending on the analyte. Recoveries ranged from 88 to 130% for the analytes. Instrument repeatability was less than 13% for all compounds, while intermediate precision ranged from 5 to 25% RSD. HorRat values were within acceptable ranges.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".