Ultrasound‐assisted extraction and modification of pulse starches: A review
Bibliographic record
Abstract
Abstract Background and Objectives Conventional pulse starch extraction methods face challenges in terms of yield, purity, and recovery. The native starches after extraction often undergo modification for broader applications. Ultrasound is considered a promising approach for starch extraction and modification due to its unique principle and reduced processing times. This work addresses the effects of ultrasound‐assisted methods on extraction and characteristic modification of pulse starches. Findings The cavitation effect of ultrasound effectively disrupts starch–protein interactions, improves diffusion, and significantly increases the pure starch yield. When applied to starch modification, it impacts surface morphology, amylose, and amylopectin chains resulting in notable changes to the characteristic behavior. Dual modification by combining ultrasound with other methods could allow for customized starch characteristics through structural reorganization, cross‐linking, and depolymerization. Conclusions The increased yields and modified properties of pulse starches through ultrasound‐assisted methods could enable their utilization in a wide range of food and nonfood applications. Significance and Novelty This review provides new insights into the extraction and modification of pulse starches through ultrasound‐assisted methods. It benefits researchers, food and starch industries, in selecting appropriate processing methods based on yield and specific properties of pulse starches required for their intended applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".