A current review of structure, functional properties, and industrial applications of pulse starches for value‐added utilization
Bibliographic record
Abstract
Pulse crops have received growing attention from the agri-food sector because they can provide advantageous health benefits and offer a promising source of starch and protein. Pea, lentil, and faba bean are the three leading pulse crops utilized for extracting protein concentrate/isolate in food industry, which simultaneously generates a rising volume of pulse starch as a co-product. Pulse starch can be fractionated from seeds using dry and wet methods. Compared with most commercial starches, pea, lentil, and faba bean starches have relatively high amylose contents, longer amylopectin branch chains, and characteristic C-type polymorphic arrangement in the granules. The described molecular and granular structures of the pulse starches impart unique functional attributes, including high final viscosity during pasting, strong gelling property, and relatively low digestibility in a granular form. Starch isolated from wrinkled pea-a high-amylose mutant of this pulse crop-possesses an even higher amylose content and longer branch chains of amylopectin than smooth pea, lentil, and faba bean starches, which make the physicochemical properties and digestibility of the former distinctively different from those of common pulse starches. The special functional properties of pulse starches promote their applications in food, feed, bioplastic and other industrial products, which can be further expanded by modifying them through chemical, physical and/or enzymatic approaches. Future research directions to increase the fractionation efficiency, improve the physicochemical properties, and enhance the industrial utilization of pulse starches have also been proposed. The comprehensive information covered in this review will be beneficial for the pulse industry to develop effective strategies to generate value from pulse starch.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".