Brown seaweed as a food ingredient contributing to an adequate but not excessive amount of iodine in the European diet. A case study with bread
Bibliographic record
Abstract
Abstract BACKGROUND The large amounts of iodine in brown seaweeds may contribute towards an adequate iodine intake, but also pose a food safety risk. In the current work we estimate the maximum amount of the cultivated brown seaweeds Saccharina latissima and Alaria esculenta (blanched and non‐blanched) that can be added to white loaf‐type wheat‐bread so European consumers are protected against excessive chronic intakes. We use data for high‐level consumers of bread with special ingredients added from the EFSA comprehensive European food consumption database to construct a conservative risk management model. We bake prototype seaweed bread and use the output from the model to assess exposure to iodine and trace metals. We also assess some bread quality parameters such as sensory characteristics, crumb firmness and specific volume. RESULTS The maximum level of iodine in bread that would mitigate the European consumer from excess intakes was 857 μg I kg −1 bread. Assuming a typical 60% wheat flour per kilogram of bread, a maximum amount of 11.3 g of dried blanched A . esculenta (115 mg I kg −1 dry weight) could be incorporated per kilogram of wheat flour into the bread recipe whilst for non‐blanched S . latissima , which contained 3500 mg I kg −1 dry weight, only a minuscule 350 mg of seaweed was the limit. For prototype breads, seaweed addition had no significant impact on the specific volume and crumb firmness. Levels of cadmium and arsenic in the bread were also of low toxicological concern. CONCLUSION A maximum 1–2% brown seaweed of total flour weight could be added to bread to contribute towards European consumers’ adequate iodine intake without overexposure. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical 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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".