The Impact of Heat-Moisture Treatment on Molecular Structures and Properties of Starches Isolated from Different Botanical Sources
Bibliographic record
Abstract
Heat-moisture treatment is a hydrothermal treatment that changes the physicochemical properties of starches by facilitating starch chain interactions within the amorphous and crystalline domains and/or by disrupting starch crystallites. The extent of these changes is influenced by starch composition, moisture content and temperature during treatment, and by the organization of amylose and amylopectin chains within native starch granules. During heat-moisture treatment starch granules at low moisture levels [(<35% water (w/w)] are heated at a temperature above the glass transition temperature (T(g)) but below the gelatinization temperature for a fixed period of time. Significant progress in heat-moisture treatment has been made during the last 15 years, as reflected by numerous publications on this subject. Therefore, this review summarizes the current knowledge on the impact of heat-moisture treatment on the composition, granule morphology, crystallinity, X-ray pattern, granular swelling, amylose leaching, pasting properties, gelatinization and retrogradation parameters, and susceptibility towards α-amylase and acid hydrolysis. The application of heat-moisture treatment in the food industry is also reviewed. Recommendations for future research are outlined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".