Combining proteolytic enzyme with non-thermal ultrasound and microwave methods for enhanced extractability and modified functionality of dry bean (Phaseolus vulgaris) starch: A chemical-free strategy
Bibliographic record
Abstract
This study investigated the extraction of dry bean starch using a food-grade protease enzyme and its integration with ultrasound and microwave treatments, to compare their effects on starch recovery and functional properties. Enzymatic treatment at 0.5 % protease concentration yielded a starch recovery comparable to the conventional alkaline method (∼ 84 %). Integration with ultrasound and microwave allowed higher recovery (∼ 87 %) at lower enzyme concentrations of 0.1 % and 0.25 %, respectively. Ultrasound treatment significantly modified starch characteristics, including surface damage, amylose content, thermal stability, retrogradation behavior, swelling power, solubility, pasting viscosities, rheological properties, and crystallinity. In contrast, microwave treatment induced limited changes, mainly enhancing gel strength and pasting properties while reducing crystallinity. In vitro digestibility revealed monophasic behavior, with ultrasound-treated starch exhibiting increased digestibility and a higher expected glycemic index (eGI). Thus, protease-assisted extraction offers a sustainable alternative to alkaline methods, while its integration with ultrasound or microwave reduces enzyme requirements, enhances starch recovery, and alters starch functionality, yielding application-specific starch without additional modification.
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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 | 0.001 |
| 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".