Exploring the dual role of anti-nutritional factors in soybeans: a comprehensive analysis of health risks and benefits
Bibliographic record
Abstract
Soybeans (Glycine max [L.] Merr.) are a globally significant crop, valued for their high protein content and nutritional versatility. However, they contain anti-nutritional factors (ANFs) that can interfere with nutrient absorption and pose health risks. This comprehensive review examines the presence and impact of key ANFs in soybeans, such as trypsin inhibitors, lectins, oxalates, phytates, tannins, and soybean polysaccharides, based on recent literature. The physiological roles, potential health hazards of the ANFs, and the detailed balance between their harmful and beneficial effects on human health, as well as the efficacy of deactivation or removal techniques in food processing, were discussed. The findings highlight the dual nature of ANFs in soybeans. Some ANFs have been found to offer health benefits include acting as antioxidants, potentially reducing the risk of cancer, and exhibiting anti-inflammatory effects. However, it is important to note that the same ANFs can also have negative impacts. For instance, trypsin inhibitors, lectins, and tannins may lead to gastrointestinal discomfort and contribute to mineral deficiencies when consumed in excess or without proper processing. This review will provide a clear understanding of the role of ANFs in soybean-based diets and to inform future research and food processing strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".