Application of Natural Edible Coating to Enhance the Shelf Life of Red Fruits and Their Bioactive Content
Bibliographic record
Abstract
Red fruits contain bioactive substances including phenolic acids and flavonoids, which provide many health advantages for the human body. Industries find them intriguing because of their color and their ability to prevent chronic ailments such as metabolic, degenerative, and cardiovascular disorders. Nevertheless, the resilience of these organic molecules is influenced by several environmental, physical, and chemical phenomena. Therefore, the beneficial health properties of red fruits may diminish during postharvest processing. In this scenario, many postharvest methods have been implemented to enhance the shelf life and preserve the bioactive components of red fruits. The objectives of this review were to provide a comprehensive assessment of the health benefits of red fruits, and to explore the possibilities of edible coatings in retaining their freshness and protecting their bioactive contents. Co-occurrence networks were built using VOSviewer software to produce a two-dimensional map based on term frequency, and the examination of the 1364 keywords obtained from the scientific papers revealed the presence of at least 71 co-occurrences that provide insight into many natural components used in edible coatings for red fruits, such as proteins, polysaccharides, lipids, phospholipids, and minerals. The review examined their composition, functioning, application techniques, limits, safety considerations, legal regulations, and potential future developments. This review has shown that an edible coating may act as a protective layer on the surface of the fruit, alter the interior gas composition, reduce water loss, and postpone fruit ripening, thereby enhancing the health-promoting properties.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".