Comparison of germination–parboiling, freeze–thaw cycle, and high pressure processing on the cooking quality of brown rice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Three treatments, namely, germination–parboiling (GP), freeze–thaw cycle (FTC), and high pressure processing (HPP) were compared for different qualities of brown rice (BR): appearance characteristics, cooking time and texture, and compared with those of untreated BR and white rice. All these three methods significantly ( p < .05) reduced cooking time by 12–23% and hardness of cooked BR by 17–23% (except GP), but reduced chewiness and generated some cracks in rice kernel. Moreover, GP process resulted in the best springiness and chewiness, FTC held the original lightness of BR well and had loose structure after cooking, while HPP (500 MPa) showed the lowest cooking time and cooking loss. The results of this study show that these three treatments could improve majority of the cooking qualities of BR and provide better commercial processing opportunities. Practical applications This study successfully tackles the rough cooking properties of BR via three treatments: germination‐parboiling (GP), freeze–thaw cycle (FTC), and high pressure processing (HPP). Among them, GP and HPP have been adopted for commercial applications and the products are getting increasingly accepted by Chinese consumers. FTC is a simple and effective processing method, which is rarely reported, and the expected cost of its equipment is less than 1/10 of that for HPP and 1/3 of GP. This research provides incentives for farmers and food processors to increase their incomes and it can also promote the development of whole grain foods focused to improve the health of consumers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it