Development of a Repertoire and a Food Frequency Questionnaire for Estimating Dietary Fiber Intake Considering Prebiotics: Input from the FiberTAG Project
Bibliographic record
Abstract
Most official food composition tables and food questionnaires do not provide enough data to assess fermentable dietary fibers (DF) that can exert a health effect through their interaction with the gut microbiota. The aim of this study was to develop a database and a food frequency questionnaire (FFQ) allowing detailed DF intake estimation including prebiotic (oligo)saccharides. A repertoire of DF detailing total, soluble DF, insoluble DF and prebiotic (oligo)saccharides (inulin-type fructans, fructo-oligosaccharides and galacto-oligosaccharides) in food products consumed in Europe has been established. A 12 month FFQ was developed and submitted to 15 healthy volunteers from the FiberTAG study. Our data report a total DF intake of 38 g/day in the tested population. Fructan and fructo-oligosaccharides intake, linked notably to condiments (garlic and onions) ingestion, reached 5 and 2 g/day, respectively, galacto-oligosaccharides intake level being lower (1 g/day). We conclude that the FiberTAG repertoire and FFQ are major tools for the evaluation of the total amount of DF including prebiotics. Their use can be helpful in intervention or observational studies devoted to analyze microbiota-nutrient interactions in different pathological contexts, as well as to revisit DF intake recommendations as part of healthy lifestyles considering specific DF.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".