High Pressure Extraction of Astaxanthin from Shrimp Waste (<i>Penaeus Vannamei</i> Boone): Effect on Yield and Antioxidant Activity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract High pressure extraction (HPE) of astaxanthin from shrimp (Penaeus Vannamei Boone) waste at different pressure (0.1–600 MPa) and holding times (0–20 min), and with different solvents (acetone, dichloromethane and ethanol) and solvent to solid ratios (10–50 mL/g) was evaluated, and extraction yields and quality of the extracts were determined. Antioxidant activity of the extract from HPE and conventional solvent extraction (at ambient pressure) were compared based on DPPH (1,1‐diphenyl‐2‐picrylhydrazyl) and superoxide anion radical of scavenging potentials. Besides, surfaces of shrimp shells following different treatments were characterized by scanning electron microscopy. The results revealed that (1) HPE resulted in a higher extraction yield of astaxanthin and required shorter extraction times, (2) ethanol as solvent and a solvent to solid ratio of 20 mL/g was a good combination for HPE for high extraction yield of astaxanthin and (3) HPE extraction resulted in a better antioxidant activity in the extract than conventional solvent extraction. Practical Applications This study is focused on the evaluations of the extraction yield and antioxidant activity of astaxanthin from shrimp waste by high pressure. The high pressure extraction (HPE) technology is very promising because of higher yield and antioxidant activity and would help to facilitate further utilization of shrimp waste as a source of natural antioxidants in food and pharmaceutical industries, replacing artificial chemical antioxidants.© 2016 Wiley Periodicals, Inc
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it