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
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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 | 0.000 | 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) | 0.001 | 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".