Comparative Analysis of Anti-Nutritional Factors in Edible Legumes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Edible legumes are rich in protein, dietary fiber, minerals and various functional active components, and are an important part of the global dietary structure. However, while these beans are providing nutritional value, they also contain a variety of anti-nutritional factors, such as phytic acid, oxalic acid, tannin, saponin, protease inhibitor and lectin, which may affect the absorption and utilization of key nutrients by the human body. This study systematically reviewed the types of common anti-nutritional factors in edible legumes, their physiological mechanisms of action, and the distribution patterns of their contents in different legumes (such as soybeans, mung beans, peas, red adzuki beans, kidney beans, and chickpeas), analyzed their interactions with nutritional components, and explored their possible positive physiological functions. The research progress of traditional and modern detoxification treatment technologies (such as fermentation, enzyme treatment, genetic modification, etc.) was also reviewed, and the practical experiences of different countries and regions in the control of anti-nutritional factors were discussed. Through in-depth comparisons of the composition, functional effects and treatment strategies of anti-nutritional factors, this study aims to provide scientific basis and technical references for the nutritional optimization, variety breeding and functional product development of edible legumes, and promote the sustainable utilization and value enhancement of legumes in the fields of food and health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it