Microalgae: Bioactive Composition, Health Benefits, Safety and Prospects as Potential High-Value Ingredients for the Functional Food Industry
Bibliographic record
Abstract
Global population is estimated to reach about 9.22 billion by 2075. The increasing knowledge on the relationship between food biochemistry and positive health gives an indication of the urgency to exploit food resources that are not only sustainable but also impact human health beyond basic nutrition. A typical example of such novel food is microalgae, an aquatic microorganism with a plethora of diverse bioactive compounds including phenolics, carotenoids, vitamin B12 and peptides. Microalgal bioactive compounds have been shown to possess positive health effects such as antihypertensive, anti-obesity, antioxidative, anticancer and cardiovascular protection. Although, the utilization of microalgal biomass by the functional food industry has faced lots of challenges because of species diversity and variations in biomass and cultivation factors. Other documented challenges were ascribed to changes in functional structures during extraction and purification due to inefficient bio-processing techniques, inconclusive literature information on the bioavailability and safety of the microalgal bioactive compounds and the fishy odor and taste when applied in food formulations. In spite of these challenges, great opportunities exist to exploit their utilization for the development of functional foods. Microalgae are a renewable resource and have fast growth rate. Therefore, detailed research is needed to bridge these challenges to pave way for large-scale commercialization of microalgal-based healthy foods. The focus of this review is to discuss the potential of microalgae as natural ingredients for functional food development, factors limiting their acceptance and utilization in the food industry as well as their safety concerns with respect to human consumption.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".