Formation of <i>N</i>-Nitroso-<i>N</i>-methylurea in Various Samples of Smoked/Dried Fish, Fish Sauce, Seafoods, and Ethnic Fermented/Pickled Vegetables Following Incubation with Nitrite under Acidic Conditions
Bibliographic record
Abstract
In continuation of our previous studies on N-nitroso-N-methylurea (NMU) formation in cured meats following incubation with nitrite at gastric pH, we extended the investigation to other foods mentioned in the title of this paper. The main objective was to determine whether these foods have the potential to form NMU at pH's that can be found in the human stomach. This was done by nitrosating an aliquot (5 g for fish sauce, 10 g for the others) of each with 7.25 microM to 1.59 mM levels of sodium nitrite for 2 h at room temperature at pH 0.8--1.5 and measuring the amounts of NMU formed. Of the samples tested, fish sauce formed 2--712 ng of NMU, followed in decreasing order by herring (<0.3--688 ng); dried anchovy, shrimp, and other fishes (<0.3--134 ng); crab and lobster paté (<0.3--342 ng); sardines (6--59 ng); oysters and mussels (11--31 ng); dried squid (3--47 ng); kimchi (7--107 ng); and Japanese pickled radish (<0.3--72 ng). Incorporation of 200-2000 ppm of ascorbic acid in the fish sauce and other foods, prior to nitrosation, appreciably inhibited such NMU formation. Although previous researchers in China reported NMU formation in nitrosated samples of fish sauce, this is the first reported formation of NMU upon nitrosation of the other foods mentioned above, and the first reported inhibition of such formation by added ascorbic acid.
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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 | 0.000 | 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".