Critical reviews and recent advances of novel non-thermal processing techniques on the modification of food allergens
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
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.
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.
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 | 0.003 | 0.010 |
| 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 | 0.002 |
| 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 it