Impact of thermo-sonication on quality indices of starch-based sauces
Bibliographic record
Abstract
In this study, ultrasonication, a physical, relatively cheap, and environmentally benign technology, was investigated to characterize its effect on functional properties of rice starch and rice starch-based sauces. Temperature-assisted ultrasound treatment improved the granular swelling power, fat and water absorption capacities, and thermal properties of rice starch, signifying its suitability in the formulation of starch-based sauces. Rheological characterization of the formulated sauces revealed a shear-thinning flow behavior, well described by the Ostwald de Waele model, while viscoelastic properties showed the existence of a weak gel. Results indicated that ultrasonication significantly enhanced the pseudoplastic behavior of starch-based sauces. Additionally, textural analysis showed that textural attributes (stickiness, stringiness, and work of adhesion) were also improved with ultrasonication. Moreover, enhanced freeze/thaw stability was also achieved with ultrasound-treated starch-based sauces. Overall, the results from this study show that ultrasound-treated starches can be used in the formulation of sauces and potentially other food products, which meets the requirements for clean label and minimally processed foods.
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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 | 0.000 | 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".