Effect of Milling Temperature and Postmilling Cooling Procedures on Rice Milling Quality Appraisals
Bibliographic record
Abstract
ABSTRACT The objective of this research was to study the effects of different milling conditions and postmilling handling procedures on appraised milling quality of rough rice. Rough rice (M202) with moisture content of 11.5 ± 0.2% was used for this study. The samples were milled with a McGill number 3 mill under four milling conditions, including normal milling, milling at high temperature, milling with cooling using ice water, and room temperature water. The milled rice samples were cooled in closed and open plastic containers and in open pans with three temperatures: 15, 23, and 35°C. The effects of milling and postmilling conditions on milled rice temperature, moisture loss, cooling rate, single and multiple fissuring rates, total rice yield (TRY), head rice yield (HRY), whiteness index (WI), and total lipid content (TLC) were evaluated. Results showed that high single and multiple fissuring rates and low TRY and HRY were inherent in improper milling and postmilling conditions. Single fissuring rates were 15.9 and 17.6% and multiple fissuring rates were 3.5 and 7.2% for rice samples milled under normal and high‐temperature conditions, respectively. Cooling methods that used open containers and pans had more moisture losses and further resulted in lowering appraised milling quality than methods that used closed containers. Low‐temperature milling conditions followed by cooling in closed containers significantly reduced single and multiple fissuring rates and improved TRY and HRY by 0.9 and 1.5 percentage points, respectively. The effects of tested milling and postmilling conditions on WI and TLC were not significant. Obtained results constitute valuable information for developing milling and cooling procedures to achieve consistent, accurate, and reliable milling quality appraisals for rough rice.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".