Physical properties and enzyme susceptibility of rice and high‐amylose maize starch mixtures
Bibliographic record
Abstract
BACKGROUND: Resistant starch has promising health benefits and possesses great potential as a functional ingredient in diverse food formulation and production. Here, 10-50% high-amylose maize starch with 70% amylose content (H7) representing type 2 resistant starch was incorporated into three types of rice starch with varying amylose content (1.6-32%). Thermal properties, rheology, and enzyme susceptibility to porcine pancreatic α-amylase of the starch mixtures were analyzed. RESULTS: Addition of H7 at levels of 10-50% decreased the yield stress and consistency coefficient of rice starches for the flow properties as modeled by the Herschel-Bulkley equation. Dynamic rheological analysis showed that addition of H7 decreased the storage modulus during heating and increased it during cooling and frequency sweeping for all rice starches tested. Gelatinization, retrogradation, and enzyme susceptibility of the resulting mixtures appeared additive of that of individual components. CONCLUSION: A desirable reduction in the digestibility of starchy foods could be achieved by adding high-amylose maize starch. The physical modifications in properties of the starch blends are dependent on the addition level of resistant 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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".