Analysis of Triphenylmethane Dye Residues and their Leuco‐Forms in Frozen Fish by LC‐MS/MS, Fish Microbial Quality, and Effect of Immersion in Whole Milk on Dye Removal
Bibliographic record
Abstract
Abstract A rapid and sensitive liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) method was validated and used to quantify crystal violet (CV), leucocrystal violet (LCV), malachite green (MG), leucomalachite green (LMG), and brilliant green (BG) residues in frozen fish (121 samples) from various countries, in order to detect the use of prohibited antibiotic dyes in fish for human consumption. The microbial quality of the fish was also assessed along with the effectiveness of a simple treatment with whole fat milk to reduce the levels of CV and LCV contamination. CV and LCV were the only two residues detected. They were found in farmed Pangasius (0.362 to 41.34 μg/kg and 0.178 to 10.58 μg/kg, respectively) and Tilapia (1.24 to 9.48 μg/kg and 1.29 to 2.81 μg/kg). Based on aerobic plate count (APC), 74%, 59%, and 55% of the samples of Tilapia fillets (from China) and Pangasius fillets (United Arab Emirates and Vietnam), and 100% and 50% of the skin samples of Hake (Argentina and U.S.A.) were of unacceptable microbial quality (APC > 10 7 cfu/g). Human pathogens, namely Escherichia coli , Staphylococcus aureus , and Vibrio spp., were detected in most fish. A significant reduction in CV and LCV concentrations by more than a third was achieved after immersing Pangasius and Tilapia fillets in whole fat milk for 120 minutes. These findings support the necessity of regular inspections and monitoring of CV and other antibiotic dye residues in fish, along with routine assessments of fish microbial quality, in order to protect public health. Practical Application The described LC‐MS/MS method can be used to rapidly and simultaneously quantify antibiotic dye residues in frozen fish. CV and LCV were detected in farmed Pangasius and Tilapia fillets and their concentrations was reduced by more than one third after immersing the fillets in whole milk for 120 min, a treatment which is not intended to replace safe fish farming practices upstream to artificially lower the level of banned dyes in fish. The findings support the necessity of regular inspections and monitoring of CV and other antibiotic dye residues in fish, along with assessments of fish microbial quality, to protect public health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".