Functional foods with a tailored glycemic response based on food matrix and its interactions: Can it be a reality?
Bibliographic record
Abstract
Functional foods are considered the future of nutrition because they benefit human health and environmental sustainability. They offer natural solutions for managing post-prandial glycemia and its long-term consequences. Therefore, understanding the composition and inherent dynamics of the functional food matrix (FM) is crucial. Within the FM, components like proteins, fats, carbohydrates, phenolic compounds, fibres, and minor elements interact dynamically, highlighting how individual components within the system behave. This review highlights the significance of diverse FM interactions in modulating inherent glycemic potential (IGP). These interactions comprise major binary, ternary, quaternary interactions, and minor interactions, in contemporary functional food formulations that include starch-derived additives, biopeptides, and flavouring agents. The starch quality matrix (SQM), a prediction model for customised functional foods with low IGP, has been briefed as a pilot concept. We also investigate the impact of these interactions on gut health, fill in the knowledge gaps, and provide recommendations for further study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".