A comprehensive overview of emerging processing techniques and detection methods for seafood allergens
Bibliographic record
Abstract
Seafood is rich in nutrients and plays a significant role in human health. However, seafood allergy is a worldwide health issue by inducing adverse reactions ranging from mild to life-threatening in seafood-allergic individuals. Seafood consists of fish and shellfish, with the major allergens such as parvalbumin and tropomyosin, respectively. In the food industry, effective processing techniques are applied to seafood allergens to lower the allergenicity of seafood products. Also, sensitive and rapid allergen-detection methods are developed to identify and assess allergenic ingredients at varying times. This review paper provides an overview of recent advances in processing techniques (thermal, nonthermal, combined [hybrid] treatments) and main allergen-detection methods for seafood products. The article starts with the seafood consumption and classification, proceeding with the prevalence and symptoms of seafood allergy, followed by a description of biochemical characteristics of the major seafood allergens. As the topic is multidisciplinary in scope, it is intended to provide information for further research essential for food security and safety.
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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".