The effect of salt concentration on swelling power, rheological properties and saltiness perception of waxy, normal and high amylose maize starch
Bibliographic record
Abstract
The effect of salt concentration on swelling power, rheological properties and saltiness perception of waxy, normal and high amylose maize starch was investigated. The swelling power decreased with increasing salt concentration from 0% to 2.0% among all starch samples. Waxy starch showed the highest swelling power at different salt levels, while high amylose starch showed the least swelling power. The salt addition increased the gelatinization temperature of waxy starch and normal starch from 71.3 °C to 77.1 °C and from 72.3 °C to 78.2 °C. Their storage modulus (G'), loss modulus (G''), and viscosity values at lower salt concentration were greater than those at higher salt concentration. The increasing tan δ of waxy and normal starch against frequency sweep indicated liquid-like behavior, while high amylose starch exhibited decreasing tan δ indicating solid-like behavior as it was difficult to gelatinize. When sensory evaluation was conducted by trained panelists, it was found that high amylose starch displayed the highest initial saltiness and in-mouth saltiness intensity, accompanied by the greatest thickness, lubrication and stickiness, while waxy starch displayed the lowest values for saltiness perception and mouthfeel.
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 | 0.001 |
| 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 | 0.001 |
| 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".