Critical reviews and recent advances of novel non-thermal processing techniques on the modification of food allergens
Bibliographic record
Abstract
Nowadays, the increasing prevalence of food allergy has become a public concern related to human health worldwide. Thus, it is imperative and necessary to provide some efficient methods for the management of food allergy. Some conventional processing methods (e.g., boiling and steaming) have been applied in the reduction of food immunoreactivity, while these treatments significantly destroy nutritional components present in food sources. Several studies have shown that novel processing techniques generally have better performance in retaining original characteristics of food and improving the efficiency of eliminating allergens. This review has focused on the recent advances of novel non-thermal processing techniques including high-pressure processing, ultrasound, pulsed light, cold plasma, fermentation, pulsed electric field, enzymatic hydrolysis, and the combination processing of them. Meanwhile, general information on global food allergy prevalence and food allergy pathology are also described. Hopefully, these findings regarding the modifications on the food allergens through various novel food processing techniques can provide an in-depth understanding in the mechanism of food allergy, which in turn possibly provides a strategy to adapt in the reduction of food immunoreactivity for the food industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.008 | 0.003 |
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".