Marine Bioactives and Their Application in the Food Industry: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The structurally diverse bioactive compounds found in marine organisms represent valuable resources for the food and pharmaceutical industries. The marine ecosystem encompasses over half of the world’s biota, providing an extensive range of bioactive compounds that can be extracted from various marine life forms, including marine microorganisms (such as bacteria, cyanobacteria, and actinobacteria), algae (both macroalgae and microalgae), invertebrates (including sponges, mollusks, echinoderms, and crustaceans), and, most importantly, fish. Many of these organisms thrive in extreme marine environments, leading to the production of complex molecules with unique biological functions. Consequently, marine biomolecules, such as lipids (especially polyunsaturated fatty acids), proteins/peptides, polysaccharides, carotenoids, phenolics, and saponins, exhibit a wide range of biological properties and can serve as valuable components in nutraceuticals and functional foods. Nevertheless, most of these biomolecules are susceptible to oxidation and degradation; encapsulation-based technologies tend to preserve them and increase their bioavailability and functions. These biological compounds demonstrate diverse activities, including antioxidant, anticancer, antithrombotic, anticoagulant, anti-inflammatory, antiproliferative, antidiabetic, antimicrobial, and cardioprotective effects, making them promising candidates for applications in the food industry. Despite their numerous health benefits, marine bioactive compounds have remained underutilized, not only in the food industry but also in the pharmaceutical and nutraceutical sectors. Therefore, this review aims to provide an overview of the various sources of marine bioactive compounds and their potential contributions to the food industry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it