Traditional and Emerging Physical Processing Technologies: Applications and Challenges in Allergen Control of Animal and Plant Proteins
Bibliographic record
Abstract
Under global food shortages and environmental challenges, the food industry is shifting from animal to plant proteins, coinciding with a rising prevalence of food allergies. Structural and functional differences between animal and plant proteins significantly influence their allergenic potential. Traditional and emerging physical processing technologies can modify protein structures and reduce immunoreactivity without chemical additives, preserving food quality. These technologies are categorized into thermal, radiation, light, high-pressure, transient pressure, mechanical wave, plasma, and electric field effects. Cold plasma is particularly effective in modifying protein structures by generating reactive species. High hydrostatic pressure also demonstrates significant potential, particularly when combined with heat treatment. Ultrasound, especially when integrated with other methods (e.g., enzymatic hydrolysis or glycosylation), offers versatile and cost-effective solutions. Microwave heating remains a practical option due to its efficiency and scalability, while gamma irradiation remains an effective tool for allergen reduction despite its regulatory and consumer acceptance challenges. Low-allergenic processed foods can induce oral tolerance in allergic individuals, necessitating comprehensive safety evaluations covering allergenicity, toxicity, and nutrition. Unintentional allergens in food processing pose risks, and current regulations are insufficient. International organizations have proposed precautionary allergen labeling to protect consumers. While processing technologies cannot fully eliminate allergenicity, optimized selection can mitigate risks. Future research should focus on their impact on functional properties, nutritional value, safety, cost, and production efficiency to ensure safe and effective application in the food industry.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".