Green Alternatives to Nitrates and Nitrites in Meat-based Products–A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it