Emerging potentials of duckweed (Lemnaceae): From composition to protein uses in food and nutraceuticals – A review
Bibliographic record
Abstract
Duckweed, a rapidly growing aquatic plant, is gaining significant attention as a sustainable protein source. However, fully unlocking its potential requires a comprehensive evaluation, from its nutrient composition to its applications in both food and nutraceutical products. This review provides a holistic approach, beginning with the factors influencing duckweed's nutrient composition, including growth conditions and environmental variables. The challenges of protein extraction from duckweed are reported in comparison to other leafy plants, highlighting current obstacles and innovative methods. The functional properties of duckweed proteins after extraction-including solubility, emulsification, foaming, and gelling-are critically assessed for their relevance in food product development. Duckweed protein digestibility in animal and humans is then thoroughly examined, underscoring duckweed's prospect as a high-quality protein source. Additionally, the bioactivities of duckweed-derived compounds, particularly peptides, are explored for their potential health benefits, including antimicrobial, antihypertensive, antidiabetic, anticancer and antioxidant properties. This review also addresses consumer acceptance and real-world applications based on existing literature, commercial developments, and patents. Regulatory and safety considerations are reported, highlighting both the successes and challenges encountered in recent regulatory efforts. Overall, while duckweed has significant potential as a sustainable food source, research and commercial interest are still in their early stages. Its future role and identity in the food system, whether as a whole food, functional protein source, or source of bioactive compounds, remains to be defined.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".