11. What’s in a Loaf? A Bio-accessibility Study of Toxic Elements in Gluten-free and Rye Breads and the Effect of Toasting Bread on the Bio-accessibility of those Elements Using ICP-MS
Bibliographic record
Abstract
Bread is a staple in the North American diet with over 4 million tonnes consumed in the US annually. The popularity of breads made from alternative grains (such as rye, quinoa, and pumpernickel) and the increase in gluten free (GF) alternatives (made from a mixture of rice and other alternative grains) has significantly contributed to this growth[1] While the hunger for alternative breads is increasing, there is little research into the risks associated with consuming breads made from alternative grains. Studies[2] have shown that many grains can contain high levels of toxic elements, especially if they are grown in soils with high levels of these elements. A previous study [3] investigated the risk associated with toxic elements in rice, and concluded that these elements are highly bio-accessible when the rice is not washed before processing. It is therefore extremely important to investigate the risk and bio-accessibility of toxic elements in these popular alternative breads. Two different alternative breads were analyzed for their toxic element compositions, and the bio-accessibility of these elements was investigated. Results showed that the gluten free breads contained high concentrations of arsenic and selenium, and little to no levels of cadmium or lead. Rye bread, in comparison, contained little arsenic, cadmium, or selenium which was consistent with previous studies. Further work will investigate the effect of toasting and will also utilize an innovative online leaching method that significantly improves bio-accessibility results [3,4].
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".