Potential Blue Bioresources to Develop Functional Foods
Bibliographic record
Abstract
Functional foods are foods with therapeutic properties that enhance health along with nutritional properties. This review provides information about the potential of using marine ingredients to develop functional foods by elaborating on the nutritional and therapeutic effects of bioactive compounds found in marine bioresources. Microalgae, marine fungi, bacteria, marine invertebrates, vertebrates, and marine plants are marine resources, and some of the bioactive compounds obtained from marine resources are polysaccharides, fatty acids proteins, peptides, amino acids, many types of essential macro and trace elements, pigments, and phenolic compounds. Marine bioactive compounds have shown many therapeutic properties, including anticancer, antimicrobial, antioxidant, anti-proliferative, anti-inflammatory, antidiabetic, and immune regulatory activities. These compounds can be used in the functional food industry in the form of nano or micro-particles, liposomes, gels, liquids, solids, pastes, and emulsions to overcome the challenges that could occur during product formulation and processing. Overall, this book chapter reveals the important facts about marine bioresources (except Seaweeds) and their functional potentials that the majority are unaware of. It also identifies that future research studies should be carried out.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".