Starch thermal transitions comparatively studied by DSC and MTDSC
Bibliographic record
Abstract
Abstract The gelatinisation process of waxy starch was studied using both differential scanning calorimetry (DSC) and modulated temperature DSC (MTDSC). It was revealed that the results from the two techniques, especially the onset gelatinisation temperature, were slightly different, which may be due to the MTDSC principle and the mechanism of starch gelatinisation. Thus, it is suggested to avoid using MTDSC alone in the characterisation of starch thermal transitions especially in a quantitative way. However, MTDSC has the advantage in understanding the gelatinisation mechanism since it can separate the capacity change (reversible thermal event) from kinetic components (irreversible event). The stepwise change on reversible heat flow measured by MTDSC during gelatinisation was considered due to the phase transition of highly constrained starch polymer chains in granular packing. On the other hand, the glass transition of gelatinised starch (also thermoplastic starch) could not necessarily be detected by conventional DSC or MTDSC. However, by using a high‐speed DSC method, the extremely weak glass transition of the gelatinised starch with low moisture content could be enlarged and detected, which confirms the existence of glass transition of the gelatinised starch with low moisture content. This knowledge is helpful in the processing of starch‐based foods and polymeric materials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".