Elaboration of Aromatic Extracts From the Industrial Waste of White Shrimp
Bibliographic record
Abstract
The objective of this research was the elaboration of extracts from the solid industrial waste of Litopenaeus vannamei shrimp and to define the extract with the highest acceptance rate and highest extraction yield through physicochemical, microbiological and sensorial analysis. The extracts were obtained through a process of lyophilization of the liquid extracts from the baking of the waste. Three types of extracts were prepared with different raw materials: Ecasca (shells), Ecefa (cephalothorax) and Emix (50% shells±50% cephalothorax). With the exception of humidity, all other physical-chemical parameters presented significant differences between them. All data from the microbiological analysis were within the limits required by current Brazilian legislation. In the sensory evaluation, all the extracts were well accepted, the Ecefa being the treatment that obtained the highest index of acceptability for the aroma and flavor. The Ecefa treatment obtained the highest extraction yield. In view of the obtained data, it was possible to determine that the Ecefa treatment would have a greater potential as an extract to be applied in foods that can present shrimp aroma and flavor, thus contributing to the use of shrimp industry waste in the offer of a natural aromatic extract and for the preservation of the environment by avoiding the disposal of such waste improperly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".