Effect of heating rate at different moisture contents on starch retrogradation and starch–water interactions during gelatinization
Bibliographic record
Abstract
Abstract The objective of this research was to investigate the effect of heating rate at different moisture contents on starch retrogradation and gelatinization process. Starch retrogradation was not influenced by either moisture content (water/starch ratio of 0.7 or 2.0) or heating rate (5°C/min, 20°C/min, or 40°C/min). In order to further understand the effects of heating rate on starch–water interactions, starch suspensions at a water/starch ratio ranging from 0.7 to 3.0 were heated at 5, 15, or 25°C/min by using a DSC to different final temperatures and rescanned. The deconvoluted G and M1 endotherms and the corresponding additional unfrozen water (AUW) were determined. The results showed that the G and M1 endotherms merged at higher heating rates and at higher moisture contents as expected. A significant interaction was observed between moisture content and heating rate. The results suggest that the gelatinization process is governed by moisture content at the lower heating rate (5°C/min) and by heating rate at the higher heating rates (15 or 25°C/min). Results from the AUW data suggest that the M1 component of gelatinization dominated at moisture content below water/starch ratio of 1.5 and at 5°C/min heating rate. However, at moisture contents above water/starch ratio 1.0, an interaction was observed between moisture content and heating rate. The data suggest that at higher moisture content (>1.5 water/starch ratio) and at higher heating rate (≥15°C/min), there is still a kinetic limitation to the complete melting of the M1 endotherm.
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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) | 0.000 | 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 | 0.001 |
| 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".