Why is Antactic krill (Euphasia superba) oil on the spotlight? A review
Bibliographic record
Abstract
Abstract Antarctic krill ( Euphausia superba ) oil is attracting more interest for its nutritional as well as functional potentials. Nevertheless, its potential as new and innovative food component remains largely unexplored. This review aims to outline the chemical composition, extraction methods, and health advantages of krill oil, offering insights for its utilization and provides evidence why it is now on the spotlight. Krill oil presents a distinctive fat profile, rich in lipid classes, with phospholipids (PLs) comprising a significant portion (38.93—79.99%) with high levels of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Additionally, it includes several minor bioactive components like astaxanthin, tocopherols, sterols, flavonoids, and vitamin A. Various extraction technics, including solvent and solvent-free extraction, enzyme-assisted pretreatment extraction, super/subcritical fluid extraction, significantly influence both output as well as standard of the resulting product. Furthermore, the oil had been linked to a number of health advantages, including prevention of cardiovascular disease (CVD), anti-inflammatory effects, support for women's physiology, anticancer activities, as well as neuroprotection, among others. Despite the commercial availability of krill oil products as dietary supplement, there is a scarcity of studies exploring the underlying molecular mechanisms responsible for its various biological activities. Despite this, apply krill oil as an innovative food ingredient has not been thoroughly investigated. This review consolidates information on the chemical composition, extraction techniques, possible health advantages, as well as existing uses as applications, aiming to offer insights for its complete exploitation. In addition, it attempts to unravel the fundamental molecular mechanisms that being investigated to deeply understand how krill oil produces various biological effects. Graphical Abstract
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".