Valorization of fisheries by-products via enzymatic protein hydrolysis: A review of operating conditions, process design, and future trends
Bibliographic record
Abstract
Fisheries by-products constitute large waste streams, despite containing protein, lipids, and other valuable compounds. The enzymatic protein hydrolysis process has been established as a means of effectively retrieving these products, though there has been little study to date on the impact of process operating conditions, pre-treatments, and process design on product quality. This review studies the impact of operating conditions relevant to the process, as well as the important parameters governing design and scale-up of the process. Findings indicate pre-treatments such as defatting, while common in literature, can limit the degree of hydrolysis of protein hydrolysates, while also conferring negative environmental impacts. Process conditions, such as temperature, pH, water ratios, and enzyme dose are typically established at lab scale, and can be at a disconnect with pilot and industrial scale studies. Furthermore, the water quality and pH control methods applied at lab scale are difficult to achieve at commercial scale. Current innovations involving endogenous fish enzymes and Enzyme Membrane Reactors may improve feasibility of this process in future, though these require more work. Enzyme hydrolysis is a promising technology for valorizing fisheries and other proteinaceous by-products and could see enhanced use in industry from further study on kinetics and scale-up.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".