Phytoestrogens as Bioactive Ingredients in Functional Foods: Canadian Regulatory Update
Bibliographic record
Abstract
Some food products naturally contain phytoestrogens, but there are few in Western diets that are significant sources. The foods that are the most significant sources are soy beans, containing isoflavones, and flaxseed, which contains lignans. These foods have, however, until recently been relatively little used in Canada and the United States in the human diet. While the Japanese diet contains from 20 to 80 mg of isoflavones per day, Canadian and U.S. diets tend to be below 1 mg/day in the absence of soy protein. The number of foods sold in the West that have ingredients derived from soybeans has been increasing. Many of these have soybean oil, soy protein, or other ingredients serving functional roles in the food. However, with increasing interest among consumers in dietary choices that help to improve health and reduce risk of disease, products in which soy is featured are quite readily available, and it is common, at least in Canada, to find bread and other bakery products with flaxseed added. Soy foods sales in the United States increased at a 15% compounded annually growth rate between 1992 and 2003, with a major increase occurring between 2000 and 2001 (1). Growth rates are reported to have declined somewhat in 2004 (2). The U.S. Food and Drug Administration approval of a health claim associating soy protein intake with reduced risk of heart disease in 1999 is attributed with fueling the very sizable growth since 2000. The claim identifies 25 g/day as the amount needed to derive the claimed health benefit. Because the isoflavone content of soy products is highly variable, depending on the method of processing (3), it is difficult to estimate quantitatively the impact of the increased rate of soy product consumption on soy isoflavone intake, although an overall increase can be expected. In contrast,
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".