Preliminary Study on the Tltrasonic-aided Extraction of Adzuki Bean Protein and It's Functional Properties
Bibliographic record
Abstract
[Objective] To optimize technique for extracting adzuki bean protein by ultrasonic assisted method,discuss functional characteristics of adzuki bean. [Method] Based on the single factor experiment,the optimal solid-liquid ratio,p H,ultrasonic time,temperature as influencing factors,extraction yield as response value,the optimal conditions were determined and functional characteristics were studied. [Result]The best combination was: solid to liquid ratio 1∶11. 2 g/ml,p H 7. 2,extraction time 52 min,extraction temperature 51 ℃,and the yield of adzuki bean protein reached up to 81. 20%. The results of studying on functional properties of adzuki bean protein indicated that better ability of water retainmen,solubility,emulsibility and emulsion stability of adzuki bean protein were observed at p H far away from isoelectric point and concentration of Na Cl at 0. 8 mol/L; and along with sucrose concentration increasing,the ability of water retainmen increased,but the solubility decreased and no pronounced changes in the emulsibility and emulsion stability; ability of water retainmen,emulsibility and emulsion stability of adzuki bean protein were better at 60 ℃,solubility,oil absorption were better at 50 ℃,and all decreased following with the further increase of temperature.[Conclusion]The study can provide a certain theoretical basis and data reference for development,utilization and production of adzuki bean protein.
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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) | 0.001 | 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".