Comparison of low‐temperature processes for oil and coenzyme Q10 extraction from mackerel and herring
Bibliographic record
Abstract
Abstract Among the fat fish species available from Eastern Quebec (Canada), whole Atlantic mackerel (Scomber scombrus) and herring (Clupea harengus) represent abundant fishery resources which are currently under‐utilized. They have relatively high contents of oil and coenzyme Q10 (CoQ10) in their tissues, which could be valuable for nutraceutical applications. Therefore, two low‐temperature extraction processes were compared for the recovery of oil and CoQ10 from these resources, such as enzymatic hydrolysis using Protamex™ and supercritical CO2 (SCO2) using fish lyophilizates. The results revealed that highest yields of oil and CoQ10 were obtained using the enzymatic hydrolysis process with mackerel. Whatever the process used, CoQ10 concentrations were higher in herring oil, due mainly to a more selective extraction of CoQ10 over that of the oil. The highest CoQ10 recovery rates (extraction efficiencies) were obtained using the enzymatic hydrolysis process with both types of fish, but also the SCO2 process with herring under some conditions. For mackerel, the lower CoQ10 recovery rates obtained from the SCO2 process were explained by its more important matrix effect. An economic assessment of both processes revealed that the enzymatic hydrolysis extraction process would be the most promising for up‐scaling the recovery of oil and CoQ10 from these resources.
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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".