Green Alternatives to Nitrates and Nitrites in Meat-based Products–A Review
Bibliographic record
Abstract
Several food additives are added in food for their preservation to maintain the freshness of food (antioxidants) or to slow down or stop the growth of microorganisms (preservative agents). Nitrites and nitrates are used as preservative agents in meat. Nitrites give a smoked taste, a pinkish color in the meat and protect the consumers against the risk of bacterial deterioration. Their addition is however very limited as, in high dose, it can have risks on human health and the environment. Nitrites may also combine with secondary or tertiary amines to form N-nitroso derivatives. Certain N-nitroso compounds have been shown to produce cancers in a wide range of laboratory animals. Thus, alternatives of nitrates and nitrites are the object of numerous research studies. Alternatives, such as the addition of vitamins, fruits, chemicals products, natural products containing nitrite or spices, which have similar properties of nitrites, are in evaluation. In fact, spices are considered to have several organoleptic and anti-microbial properties which would be interesting to study. Several spices and combinations of spices are being progressively evaluated. This review discusses the sources of nitrites and nitrates, their use as additives in food products, their physicochemical properties, their negatives effects and the use of alternatives of nitrites and nitrates in preserving meat products.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".