Spéciation de l'arsenic dans les produits de la pêche par couplage HPLC-ICP-MS après extraction assistée par micro-ondes (MAE). Contribution à l'évaluation des risques par l'estimation de sa bioaccessibilité
Bibliographic record
Abstract
The government agencies generally evaluate the food risks related to the presence of arsenic in seafood samples by analyzing only total arsenic, without considering the various involved forms nor their bioavailability. The main objective of this study was to develop and validate a method allowing the French Food Safety Agency (AFSSA) to propose with its supervisions a better evaluation of the risks incurred by the consumer by determining the speciation and the bioaccessibility of the various arsenic forms in the seafood samples. The first part of this work was to develop a method of separation of the principal arsenic species usually found in the seafood samples by coupling separation by ions exchange chromatography (IEC) and detection by ICP-MS. The experimental designs methodology used in this work, allowed, in a minimum of experiments, on the one hand to evaluate the influence of the various factors and their possible interactions on the separation from 7 to 9 arsenic species and on the other hand, to determine the optimal analytical conditions while minimizing the time of analysis (less than 15 minutes), preserving satisfactory resolutions and improving the sensitivities of the compounds considered. The second part of this work related to the development of the method to extract the arsenic species in seafood samples by microwaves assisted extraction (MAE). The optimisation of the conditions of extraction in various certified reference materials showed clearly that a solvent only composed of water was sufficient to obtain satisfactory total arsenic and arsenic species recoveries. The evaluation of the analytical criteria showed that the method was practically validated for arsenic speciation in seafood samples, even if internal reproducibility will have to necessarily be refined by evaluating it later on. Then, the validated method could be applied to the speciation of arsenic in aqueous samples, and also to the certification of a dogfish liver samples organized by the Canadian NRC. The last part of this work related to the development of a fast and pragmatic in vitro method of evaluation of the maximum total arsenic and arsenic species bioaccessibility in seafood samples, by combining a continuous leaching method (for measurement in real-time by ICP-MS of the arsenic portion released by artificial gastrointestinal fluids) and the validated speciation method. The results show that the bioaccessible arsenic (approximately 50% of the total arsenic in the samples) is released very quickly (in less than 5 min) by saliva with nothing else is then released by gastrointestinal juices. In addition, this work highlighted that the inorganic arsenic species bioaccessibility appears less important than that of the organic species in seafood samples.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".