Evaluation of Arsenic Concentration in Poultry and Calf Meat Samples by Hydride Generation Atomic Fluorescence Spectrometry
Bibliographic record
Abstract
A simple, cost effective hydride generation atomic florescence spectrometry (HG-AFS) method was used for determination of total arsenic (As) in poultry and calf meat samples. The samples were digested in long necked glass digestion tubes using concentrated HNO3, HClO4 and H2SO4 as a mixture. The volume of acids (HNO3, HClO4) and the amount of sample to be used for digestion were optimized to achieve appropriate digestion. The accuracy of the proposed HG-AFS method was tested with certified reference material (DOLT 3 Dogfish Liver, NRC, Canada) and obtained results were in good agreement with certified value. The method limit of detection (LOD) value was calculated as 0.3 ng/g and dynamic range was 25 – 5000 pg/ml. Arsenic concentrations of poultry and calf meat samples were determined accurately by using aqueous calibration standards. Totally 31 samples (calf, chicken and turkey) obtained from local markets were analyzed. It was found that the average As concentration in calf meat (12.1 ± 3.9 ng/g) was significantly higher than the poultry samples whereas the arsenic concentrations were similar in turkey (3.1 ± 1.2 ng/g) and chicken (2.8 ± 1.1 ng/g) samples. In addition, dietary intake estimation of arsenic through consumption of calf and poultry meat was calculated and according to the gathered results daily intake of arsenic via calf meat was almost two times higher than poultry meat.
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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 | 0.001 | 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".