Temperature-Mediated Gel Texture Transformation in Starch Noodles: In Respect of Glass Transition Temperature Tg’
Bibliographic record
Abstract
Potato starch noodles (PSN), a characteristic gluten-free Asian food, are essentially high-concentration starch gels (about 35% starch) formed through gelatinization and retrogradation. This study systematically investigates freezing temperature effects, particularly across the glass transition temperature, on PSN texture and microstructure. We found that fresh PSN have a freezing point of −1 °C, supercooling temperature of −4.5 °C, and a Tg’ value of −3.1 °C. Freezing significantly reduced the adhesiveness of PSN and increased the hardness. During the 48 h freezing process, noodles frozen at −3 °C, the closest to Tg’, exhibited the highest hardness (14,065.77 g), springiness (0.98), cohesiveness (0.93), chewiness (11,971.06), and resilience (0.84), and the least adhesiveness. PSN frozen within the range near Tg’ (−3 °C) showed superior texture, continuous solid cross-section, and dense surface, attributed to the reverse transformation of starch, high mobility of starch chains, and smaller ice crystals. PSN frozen at −3 °C for 24 h displayed the most compact and desirable texture compared to the other samples. These findings deepen the understanding of the role of glass transition temperature in the texture formation of starch gel during freezing and provide valuable insights for optimizing the frozen processing of starch gel-based food.
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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".