Edible algae allergenicity – a short report
Bibliographic record
Abstract
Abstract The use of seaweed and algal derived products in the food industry has grown rapidly in recent times. Major areas of expansion have been in Western countries where algae derived commodities are being utilised as edible foods or sources of high value ingredients. However, studies focused on potential allergenicity attributed to these food items, prevalence of allergenicity, and public health awareness are limited. Therefore, the current research summarises the existing literature focused on algal induced allergy in humans. Of the available literature, a total of 937 titles were identified, and 33 articles underwent subsequent full-text screening. Most research focused on prevalence and were derived from studies conducted in Europe (58%), North America and Canada (33%), and the remainder Australia and South Korea (9%). No studies addressed the need for public education or labelling of algal products. Our review reports that the available evidence identified points to algal derived products as being potential sources of allergens in the human food chain. Several components have been characterised that are shown to induce allergic responses in humans. Few studies have assessed the prevalence of algal allergenicity in the general population and as such further research is warranted given the increased usage of these products in the food industry.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.006 | 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".