Effects of Traditional Smoking Methods on the Concentrations of Polynuclear Aromatic Hydrocarbons (PAHs) in Some Species of Smoked Fish Traded in Benue State, Nigeria
Bibliographic record
Abstract
The effects of three traditional smoking methods on the concentrations of polynuclear aromatic hydrocarbons (PAHs) in smoked fishes were studied. Samples of five different species of fish highly traded for immediate consumption were purchased from fishermen and processed using saw dust smoke, firewood smoke and charcoal smoke respectively. Some of the fresh fishes were also sun-dried and analyzed as control. The PAHs content were extracted with standard dichloromethane using solid-liquid extraction, and analyzed using Gas chromatography – Mass spectrophotometer (GC-MS) method. The results showed that fish samples processed with saw dust smoke recorded the highest concentrations of total PAHs, ranging from 815.75 µg/kg to 1550.28 µg/kg, followed by firewood smoked samples with total PAHs content varying between 738.14 µg/kg to 994.09 µg/kg while charcoal smoked samples recorded the least total PAHs levels of 135.02 µg/kg to 614.42 µg/kg. Benzo(a)pyrene concentrations of 5.68 µg/kg and 5.44 µg/kg respectively were detected in the samples of Arius heude loti and Mud minnow processed using saw dust smoke. The Benzo(a)pyrene levels exceeded the EC regulatory limit of 5 µg/kg. Because benzo(a)pyrene has been associated with intense carcinogenicity in humans, its levels recorded in the smoked Arius heude loti and Mud minnow may have implication for the quality and safety of these fish products. Therefore, it is imperative that regulatory bodies conduct awareness campaigns to educate both the smoked fish processors, traders and consumers on the need to discourage the use of saw dust in smoking fish and adopt safer and improved methods of smoking fishes.
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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".