Novel hydrothermal modification to alter functionality and reduce glycemic response of pea starch
Bibliographic record
Abstract
Despite being an effective and clean-label method, heat-moisture treatment (HMT) is not commonly used for starch modification in industry due to the difficulty of scale-up. This study aimed to develop a novel method of using extrusion combined with high-temperature drying (EHTD) as an alternative to HMT for starch modification. Pea starch was subjected to extrusion at 37.5 % moisture level and with a low-temperature profile (≤ 65 °C), followed by immediate heating at 130 °C for 1 h. EHTD significantly damaged the granules, altered the X-ray diffraction pattern, and reduced the relative crystallinity of pea starch. Overall, EHTD-modified pea starch exhibited increased gelatinization temperatures and decreased gelatinization enthalpy change, lowered pasting viscosity and gel hardness, as well as enhanced enzymatic resistance than the native pea starch. More importantly, in a human feeding trial (n = 20 healthy participants) to monitor plasma glucose response over a period of 2 h after consuming water-boiled sample (35 g starch, dry basis), EHTD-modified pea starch exhibited 22 % reduction (p < 0.01) in plasma glucose incremental area under the curve as compared to the native counterpart. The results indicated that EHTD could be a new simple and clean-label method to produce functional and low-glycemic starch ingredients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".