Biochemically assisted rice whitening for improving head rice yield
Bibliographic record
Abstract
Abstract The maximum attainable head rice yield in conventional long grain rice milling is approximately 64%, with around 15% being lost as a result of broken rice kernels. The primary objective of this project was, therefore, to improve the milling yield. To achieve this goal, biochemically assisted whitening processes involving the application of different aqueous solutions were evaluated. Head rice yield was increased for all tested liquids (3.3–3.8% depending on liquid) for Gladio‐type brown rice treated with 0.5% liquid prior to whitening to 40 Kett using a lab‐scale horizontal friction‐type McGill whitener. However, the moistening led to increased caking in the McGill milling chamber. In comparative trials, the use of moistening solutions containing enzymes, sorbit, or sodium chloride instead of pure water delivered a slightly, but nevertheless, significantly higher degree of whiteness directly after milling while it did not result in a significant reduction in the number of broken kernels. Since average head rice yield has a 43% higher commercial value than broken kernels, the 3.6% improvement in milling yield achieved by adding 0.5% water would result in an estimated increase in profit for a 7.5 t/h rice mill of 0.83%. Practical applications Rice as a global staple food bears a critical role in human nutrition. At the same time, the quality of milled rice is a key buying and price criterion in rice‐consuming countries. One key quality criterion is the number of brokens in rice. Hence, it is critical for rice millers to minimize the degree of broken kernels. Biochemically assisted rice whitening for improving head rice yield is a combined biochemical and physical method to facilitate bran removal from brown rice. The main aim of the present study was to investigate the effect of biochemically assisted rice whitening on the number of brokens and to assess potential technological challenges resulting from the liquid addition.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".