Identification and characterization of soybean dreg soluble dietary fibre by combination of extrusion pre-treatment and enzymatic modification
Bibliographic record
Abstract
Soybean dreg is a by-product of soy milk processing, which contains high levels of soluble dietary fibre (SDF). In this study, we aimed to provide comprehensive processes of pre-treated extrusion for the improving structure and properties of soybean dreg soluble dietary fibre (SDSDF), which would be a valuable approach to enhance physiological activity. Here, we characteristic the functional role of SDSDF employing to extrusion pretreatment. Soybean dregs were pre-treated using the twin screw extrusion method followed by enzymatic modification using neutral protease, α- amylase, glucoamylase, and cellulose to produce SDSDF. The physical properties and antioxidant activity of SDSDF were investigated. The morphology and crystal structure of SDSDF were observed that, through extrusion processing and enzymatic modification, the SDSDF yield increased by 106.28%. Moreover, the surface structure showed block-shaped or reticular formations in the extruded SDSDF, and the size of block-shaped cells was about 10 μm. Infrared spectroscopic analysis showed that a characteristic absorption peak of polysaccharide appeared at 1631 cm−1 during extrusion processing. However, after extrusion processing, decreased absorption peaks were observed for the extruded SDSDF. Furthermore, XRD analysis showed that the 2θ diffraction peak changed at 24.16° for the extruded SDSDF. The overall findings suggest that the water holding capacity (WHC), oil holding capacity (OHC), expansibility, and the water solubility were significantly decreased in extruded SDSDF. In addition, the scavenging ability of 1,1-diphenyl-2-picrylhydrazyl (DPPH), -OH, O2-, and the total reducing power were significantly improved, indicating that beneficial changes had taken place in the crystal structure of cellulose or hemicellulose to improve the physiological activity in extruded SDSDF.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".