Research advances on the formation mechanism of resistant starch type III: A review
Bibliographic record
Abstract
Resistant starch (RS) plays a key role in providing metabolic and colonic health benefits. In particular, RS type III (RS3) is of great interest because of its thermal stability and its preserved nutritional functionality. RS3 can be prepared by physical treatment, including high hydrostatic pressure, ultrasound, extrusion, autoclaving, microwave cooking, and heat-moisture treatment. The acid and enzymatic hydrolysis can also be applied to facilitate the generation of small molecules, which increases the inherent crystallinity of RS3 upon retrogradation. Depending on processing conditions, RS3 with diversified structural characteristics can be formed. These structures play a key role in determining the physiological behavior of RS3. Therefore, a deep understanding of the structural rearrangement pattern during different processing treatment is of great importance for regulating the molecular structure of RS3 and thereby its corresponding physiological properties. This review thus focuses on the past and current status of research into the in-depth study of RS3 formation mechanism and the changes to RS3 structural characteristics under different processing conditions. The objective was to provide a theoretical guidance for the rational selection of preparation methods for RS3 and for designing RS3 structures with specific physiological functionalities for relevant industrial 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 | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".