Valorization of snow crab (<i>Chionoecetes opilio</i>) cooking effluents for food applications
Bibliographic record
Abstract
Abstract BACKGROUND Seafood processing generates significant amounts of solid and liquid waste in the environment. Such waste represents a potential source of high‐value biomolecules for food, pharmaceutic and cosmetic applications. There are very few studies on the valorization of wastewaters compared to solid by‐products. However, cooking waters are characterized by a high organic polluting load, which could contain valuable molecules such as proteins, pigments and flavor compounds. Snow crab ( Chionoecetes opilio ) processing is included among the most important processes in Canadian fisheries, although its cooking effluent composition is not well characterized. RESULTS The present study concentrated and valorized the biomass in snow crab cooking wastewaters for the development of products for food applications. A membrane process was designed and optimized to concentrate the effluents. The chemical composition of the concentrates was analyzed, including characterizing the flavor profile compounds. The extracts were mainly composed of proteins (592 g kg −1 ) and minerals (386 g kg −1 ) and contained desirable flavor compounds. Their functional properties (solubility, water‐holding capacity, oil‐holding capacity) and antioxidant activities were also assessed, and their safety was verified. CONCLUSION The cooking effluents generated by snow crab processing facilities, usually considered as waste, can be concentrated and turned into a natural aroma for the food industry. © 2019 Society of Chemical Industry
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.
How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".