Effect of pre-harvest and post-harvest conditions on the fruit allergenicity: A review
Bibliographic record
Abstract
Fruits are an important source of vitamins and antioxidants that can effectively delay aging and contribute to health and well-being of the human kind. However, they are growing to be one of the primary elicitors of food allergies around the world. Fruit allergens can induce an IgE-mediated (Immunoglobulin E) reaction, presenting with a symptom like localized oral allergy syndrome (OAS). Numerous studies showed that varying environmental and cultivation conditions can influence the fruit allergen content during flowering and ripening stages. Further, the variety, harvesting maturity, and storage conditions can also significantly influence the allergenicity potential. For example, unripe apples and tomatoes have lower levels of allergens compared to ripened fruits. Researchers have also reported that modified atmosphere packaging (MAP) can help reduce Mal d 3 content present in apples during storage. Post-harvest processing like peeling is also considered a good method to help reduce the overall allergenicity in few fruits whose peel might contain majority of the allergens. This review will discuss the overall influence of both pre-harvest and post-harvest factors on the fruit allergens. We will also discuss the progress regarding the cause, symptoms and diagnostic methods of fruit based allergies.
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 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".