Chemical Characteristics and Antioxidant Activity of Astaxanthin Extracted from Shrimp Residues Using Soybean Oil
Bibliographic record
Abstract
Shrimp residues generated by the seafood industrial processing are generally discarded in the environment without any further treatment, leading to a loss of nutritional value such as proteins, lipids, chitin, carotenoids, and minerals. This practice can lead to environmental problems due to the decomposition of these substances in the air. The objective of this study was to extract astaxanthin-rich materials from shrimp (Litopenaeus vannamei) industrial residue (WO) and smaller-scale residue meal (MO) in processing shrimp with soybean oil to evaluate their physicochemical characteristics, fatty acid content, and antioxidant potential. WO and MO were found to contain 27.48 and 33.34 µg g-1 of astaxanthin, respectively. The physicochemical properties of pigmented oils are established by legislation. The MO material showed significantly higher antioxidant activity compared to the soybean oil (control), especially when based on the oxygen radical absorbance capacity (ORAC) test, which showed antioxidant activity of 0.484 and 0.264 µmol eq Trolox g-1 for the MO and soybean oil (control), respectively, possibly accompanying their respective astaxanthin contents in the MO sample. The pigmented oils from both the shrimp residues showed significant potential for being used in the food industry due to their affordability and their high antioxidant activity.
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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 | 0.001 | 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".