Extraction, Structure, Physiological Functions, and Perspectives of Soybean Non‐Starch Polysaccharides: A Review
Bibliographic record
Abstract
ABSTRACT Soybean (Glycine max) and its byproducts, such as soybean hull, soybean meal, and okara, are good sources of non‐starch polysaccharides (NSP), a kind of potential functional food ingredient. According to in vitro and in vivo studies, this study reviewed the extraction, purification, structure, and physiological functions of soybean NSP. Currently, the preparation technology of soybean NSP was mature, and their structure–function relationship was preliminarily clear. Furthermore, soybean NSP were indicated to exert a variety of physiological functions, including gut probiotic effect, anti‐oxidant effect, anti‐diabetes, anti‐obesity, anti‐cancer, anti‐inflammation, anti‐radiation, and so on. Nevertheless, the mechanisms underlying the physiological functions of soybean NSP have not been comprehensively clarified. On the other hand, the standardization of raw materials of soybean NSP was necessary. The higher structure–function relationship of soybean NSP needed to be elucidated. Moreover, the mechanisms underlying the bioactivities of soybean NSP might be revealed based on the interaction between soybean NSP and gut microbiota. Therefore, this review indicated the potential of soybean NSP for the prevention and treatment of diseases and may help to promote the value‐added utilization of soybean byproducts.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".