Isolation of Hydroxyapatite from Atlantic Salmon Processing Waste Using an Enzyme Cocktail
Bibliographic record
Abstract
There is a need to solve ongoing waste management issues in food processing industries. The demand for fish, including salmon, is higher than ever because of the growing global population and protein needs, however this results in large quantities of wasted by-products. This waste is problematic because it is potentially harmful to the environment and results in significant disposal costs for industries. The salmon frame (bones) is disposed of during processing but is a potential feedstock for hydroxyapatite, a mineral for value-added applications. While previous research has accessed hydroxyapatite from animal wastes, these processes either use very high temperatures or chemicals that are more costly and hazardous than the method described herein. In this study, we developed an enzymatic treatment using proteases and lipases simultaneously to clean the residual meat from salmon frames to isolate collagen-containing hydroxyapatite (sHAP) using Design of Experiment (DoE) under benign conditions. The variables were optimized using 2x3 and 2x4 factorial designs and it was determined by characterization techniques, weight loss calculations, and thermogravimetric analysis that the meat from the salmon frame was successfully hydrolyzed with 15 μL/g Neutrase and 7.5 μL/g Lipozyme CALB L in 40 °C tap water for 6 h. We have performed a life cycle analysis to compare the current method with previously reported processes used to treat other types of fishery waste. The method reported herein is less impactful (environment, hazard, cost, carbon footprint) than others in the literature, as there are no solvents required, enzymes are easily disposed, and temperatures do not exceed 100 °C during the entire process. Furthermore, the optimized conditions were then used on a larger scale and up 15 salmon frames were easily processed at one time.
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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 | 0.001 |
| 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 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".