Potentials of Natural Preservatives to Enhance Food Safety and Shelf Life: A Review
Bibliographic record
Abstract
Food-borne illnesses are a significant concern for consumers, the food industry, and food safety authorities. Natural preservatives are very crucial for enhancing food safety and shelf life. Therefore, this review aimed to assess the literature regarding the potential of natural preservatives to enhance food safety and extend the shelf life of food products. The review paper indicated that natural antimicrobial agents that inhibit bacterial and fungal growth for better quality and shelf life have been of considerable interest in recent years. Natural antimicrobials are mainly extracted and isolated as secondary metabolites of plants, animals, and microorganisms. Plants, especially herbs and spices, are given more attention as a source of natural antimicrobials. Microorganisms used in food fermentation also produce different antimicrobial metabolites, including organic acids, hydrogen peroxide, and diacetyl, in addition to bacteriocins. Products of animal origin, such as tissues and milk, contain different antimicrobial agents. Natural antimicrobials are primarily extracted and purified before utilization for food product development. The extraction condition and purification of natural preservatives may change their structure and affect their functionality. Selecting the best extraction method coupled with minimal processing such as direct mechanical extraction seems to preserve active ingredients. The activity of natural antimicrobials could also be influenced by the source, time of harvesting, and stage of development. The effectiveness of natural antimicrobial compounds in food applications is affected by different factors, including food composition, processing method, and storage conditions. Natural antimicrobials are safe because they can limit microbial resistance and meet consumers' demands for healthier foods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".