Folate and synthetic folic acid content in Canadian fortified foods two decades after mandatory fortification
Bibliographic record
Abstract
In 1998, Health Canada mandated folic acid fortification of white flour and enriched grain products to prevent neural tube defects. At the time, neither the Canadian Nutrient File (CNF) nor product labels reflected the actual folate content of foods. We aimed to assess if 20 years post-fortification, the CNF values for total folate and synthetic folic acid accurately reflect amounts determined by direct analysis. Using the 2001 Food Expenditure Survey and ACNielsen Company data, we identified 10–15 of the most purchased fortified foods across seven food categories in Canada. Total folate concentrations were determined by tri-enzyme digestion and microbial assay. Folic acid concentrations were determined using liquid chromatography-tandem mass spectrometry. Except for “cooked pastas”, mean total folate content of foods ( n = 89) were significantly higher than CNF values across categories ( p < 0.05), reflecting 167% ± 54% of CNF values. Similarly, mean folic acid content of foods was higher than CNF values for all categories except “cooked pastas” ( p < 0.05), with a mean of 188% ± 94% of CNF values; the latter CNF values included uncooked pasta. In sum, 20 years post-fortification, and 10 years since the last direct measurement, CNF and product label values still underestimate actual total folate and the folic acid content of foods. These findings emphasize that dietary estimates established using the CNF may significantly underestimate actual intakes and thus caution should be exercised when interpreting estimates of nutritional adequacy based on these values.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".