Occurrence and risk assessment of tetracycline residues in poultry meat of Dhaka, Bangladesh: A sensitive and reliable analytical method development approach
Bibliographic record
Abstract
• A novel QuEChERS based d-SPE cleanup method for TCs determination in poultry • Simultaneously four TCs were detected in simple method • TCs residues was detected at low levels in poultry • Low TCs residues in poultry pose no significant health risk This study presented a modified method for quantifying tetracycline antibiotics (tetracycline, oxytetracycline, chlortetracycline, and doxycycline) in poultry meat based on the analysis of 40 poultry samples collected from local markets in Bangladesh. The method employed liquid chromatography-tandem mass spectrometry (LC-MS/MS) with methanol extraction and optimized dispersive solid phase extraction (d-SPE) cleanup (using C18, GCB, and PSA). Matrix-matched calibration curves (r² ≥ 0.997) and validation at 10, 50, and 100 µg/kg spiking levels yielded recoveries of 82.6-116.5% with RSDs ≤ 8.9%. The method achieved low LODs (1.67-3.33 µg/kg) and LOQs (5-10 µg/kg). The results from the analysis of 40 poultry samples revealed that 55% were contamination with tetracycline residues (7.60–144.98 µg/kg), with oxytetracycline being the most prevalent. Hazard indices (HIs) indicated negligible health risks, although liver samples posed higher risks. The findings support regulatory measures to control antibiotic use in poultry farming in Bangladesh.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".