Methodologies for Increasing the Resistant Starch Content of Food Starches: A Review
Bibliographic record
Abstract
Abstract Research involving resistant starch (RS) is becoming more prominent. RS has the ability to modulate postprandial blood‐glucose levels and can be fermented by the colonic microflora to produce short‐chain fatty acids, which exert positive health benefits on the consumer such as increased colonic blood flow to ease colonic inflammation and a decreased risk of colon and/or other cancers. This paper reviews the effects of genetic manipulation on amylose levels in plants, enzymatic hydrolysis, physical treatments, chemical modifications, exposure to γ‐rays, and the effects of lipid complexation on the RS content of starches from various botanical sources. All treatments reviewed increased the RS content; however, select treatments (namely genetic manipulation, enzymatic debranching, hydrothermal treatments, high hydrostatic pressure, most chemical modifications, γ‐irradiation exposure, as well as lipid complexation) were more effective to varying degrees than were extrusion and mineral acid treatments. Various methods commonly used for measuring RS were compared. Additionally, the effects of food matrix components were also examined to gauge their effectiveness at inhibiting or enhancing RS formation, with lipids and gums known to be the most effective at enhancing (or apparently enhancing) RS. This review draws largely, but not exclusively, from research published post 2009.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".