The effect of different treatments on the accumulation of histamine in herring and fesikh
Bibliographic record
Abstract
In Egypt, the request for salt fish products has been increasing during many feasts because it is one of Egypt's favorite foods. It has been reported that in certain seasons, these products cause toxicity and even death. Most fish poisonings around the world are known to be caused by elevated histamine levels. Thus, the study assessed the histamine levels of herring and fesikh samples available on the market and established the safety of these products with recommendations for various treatments that may inhibit the production of histamine in herring and fesikh through bacteria and enzyme production. Before manufacturing herring and fesikh, the fresh fish is soaked for one hour in a modified pH solution of 4 by vinegar with the addition of natural substances individually or in combination (such as garlic, onion, hot pepper, or aloe vera) or some chemicals singly (such as edta, nisin, h2o2, formic acid, and so2). The levels of histamine in herring (111 to 138 mg/kg) and fesikh (214 to 279 mg/kg) were unsafe in marketable samples. The histamine levels of herring and fesikh in proposed treatments were safe since they did not exceed 26 mg/kg after 30 days of cold storage for herring or 45 mg/kg after maturity of fesikh. The proposed treatments enhanced greatly herring and fesikh organoleptic qualities, especially those containing natural ingredients, such as garlic, hot pepper, or their mixture beside onion, which are more accepted treatments.
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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) | 0.001 | 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 | 0.001 |
| 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".