Flavored Drink Production Using Broken Rice: Evaluation of Physical-Chemical Properties and Power Consumption of Industrial Stirring System
Bibliographic record
Abstract
The industrial rice processing generates, in average, 14% of broken grains called grits, which are not well accepted by consumers, representing large economic loss. Researches have been conducted to increase the use of rice by-products as well as their benefit. Among them, beverages are attracting the attention, being develop. To contribute to this field, this study aimed to prepare a non-alcoholic flavored drink from rice grits; evaluate the physical-chemical properties and evaluate de power consumption of stirring system for the drink industrial production. The drink production involved the cooking of the rice grits, followed by crushing, homogenization, filtration and flavorization in a stirring tank, obtaining the final product for consumption. The power consumption calculation for mixing tanks was evaluated in three different situations at 25ºC, considering the pre-defined tank design and the drink characteristics. Results based on the physicochemical characteristics indicate that the rice flavored drink is a food alternative to substitute milk or soy extract drinks. On the industrial production aspects, the increasing in the consumed energy to the small stirring variations was observed, and it needs to be considered to the stirring equipment design in the industrial process.
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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 | 0.000 |
| 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) | 0.002 | 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".