Study on Creep Properties of Japonica Cooked Rice and Its Relationship with Rice Chemical Compositions and Sensory Evaluation
Bibliographic record
Abstract
Creep properties of four varieties japonica cooked rice were tested using a dynamic mechanical analyzer (DMA Q800). The creep curve was described by Burgers model. The creep process of japonica cooked rice mainly consisted of retarded elastic deformation, ?R and viscous flow deformation, ?V, and negligible instantaneous elastic deformation, ?E. The retarded elastic modulus, ?2, relaxation time, ?, and the viscosity coefficient, ?1, of the cooked rice according to the Burger model were estimated through nonlinear regression. The effect of the chemical compositions on the rheological parameters based on creep behavior of cooked rice was assessed. Correlation analysis indicated that the retarded elastic modulus, ?2, had positive correlations with protein (r = 0.952, P = 0.048). The relaxation time, ?, had negative correlation with protein content (r = -0.909, P = 0.091). The viscosity coefficient, ?1, was negatively correlated to amylose (r = -0.915, P = 0.085). The analysis between creep parameters and sensory evaluation showed that the viscosity coefficient, ?1, positively correlated to stickiness and total evaluation of cooked rice. So it can be used to evaluate the eating quality of cooked rice.
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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".