Upcycling Shellfish Waste: Distribution of Amino Acids, Minerals, and Carotenoids in Body Parts of North Atlantic Crab and Shrimp
Bibliographic record
Abstract
The snow/pink crab (Chionoecetes opilio) and Northern shrimp (Pandalus borealis) are widely distributed in the North Atlantic Ocean. During processing/consumption, about 80% of the harvest is discarded as processing waste, which is a rich source of protein, chitin, minerals, and carotenoids. This study, for the first time, investigated the proximate composition and individual amino acids, minerals, and carotenoids from different body parts (carapace, shoulder, claw, tip, and leg) of snow crabs and shrimp shells. Shrimp proteins were found to be abundant and well-balanced in their amino acid composition. Compared to shrimp shells, a lower content of amino acids was found in the snow crab, depending on the part of the shell used. Moreover, crab shells, mainly crab claws, contained a higher (p < 0.05) level of chitin compared to shrimp shells. Seven micro-elements (Mn, Fe, Cu, Zn, As, Ba, and Ce) and six macro-elements (Ca, Na, K, Mg, P, and Sr) were identified using inductively coupled plasma-mass spectrometry (ICP-MS). Among them, calcium and iron were higher in crab carapaces (p < 0.05), followed by shrimp shells and other crab shell segments. Additionally, shrimp and crab carapaces contained a significant level of carotenoids, and these were mainly composed of astaxanthin and its mono- and diesters, along with zeaxanthin, astacene, canthaxanthin, and lutein. Thus, this investigation provides detailed information to allow upcycling of shellfish waste and addresses the knowledge gap concerning the availability of various nutrients in different crab sections and shrimp shells.
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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".