Enhancing Micronutrients Bioavailability through Fermentation of Plant-Based Foods: A Concise Review
Bibliographic record
Abstract
Plant-based foods are rich sources of vitamins and essential micronutrients. For the proper functioning of the human body and their crucial role, trace minerals (iron, zinc, magnesium, manganese, etc.) are required in appropriate amounts. Cereals and pulses are the chief sources of these trace minerals. Despite these minerals, adequate consumption of plant foods cannot fulfill the human body’s total nutrient requirement. Plant foods also contain ample amounts of anti-nutritional factors such as phytate, tannins, phenols, oxalates, etc. These factors can compromise the bioavailability of several essential micronutrients in plant foods. However, literature reports show that fermentation and related processing methods can improve nutrient and mineral bioavailability of plant foods. In this review, studies related to fermentation methods that can be used to improve micronutrient bioavailability in plant foods are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".