Aproveitamento do soro de ricota na elaboração de bebida láctea acidificada carbonatada
Bibliographic record
Abstract
Ricotta whey, despite its low protein content, is a source of many nutrients. However, in Brazil it is still used inefficiently or simply discarded in the environment. The adequate use of this type of whey is still very restricted due to the unavailability of technological knowledge or lack of interest in the industry. However, in this type of whey, lactose, mineral salts, and vitamins still remain, consequently remaining a great nutritional value and also its polluting capacity. The objective of this work was to elaborate an acidified carbonated drink using ricotta whey and to determine its physical-chemical composition, microbiological quality and sensory acceptance. Due to its physical-chemical composition, the developed milk drink fits into the legislation as a dairy drink with addition or dairy drink with food product or substance. The product showed sensory acceptability and microbiological stability during 60 days of storage at 5°C. Thus, there is technological feasibility and applicability of the sustainable use of ricotta whey in the production of carbonated acidified dairy drinks. The use of this type of whey can encourage the consumption of dairy products by improving people's nutrition, in addition to reducing environmental problems, which can increase the profits and the productive competitiveness of industries.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".