Current trends in nano-delivery systems for functional foods: a systematic review
Bibliographic record
Abstract
Background: Increased awareness of the relationship between certain components in food beyond basic nutrition and health has generated interest in the production and consumption. Functional foods owe much of their health benefits to the presence of bioactive components. Despite their importance, their poor stability, solubility, and bioavailability may require the use of different strategies including nano-delivery systems (NDS) to sustain delivery and protection during handling, storage, and ingestion. Moreover, increasing consumer trend for non-animal sourced ingredients and interest in sustainable production invigorate the need to evaluate the utility of plant-based NDS. Method: In the present study, 129 articles were selected after screening from Google Scholar searches using key terms from current literature. Scope: This review provides an overview of current trends in the use of bioactive compounds as health-promoting ingredients in functional foods and the main methods used to stabilize these components. The use of plant proteins as carriers in NDS for bioactive compounds and the merits and challenges of this approach are also explored. Finally, the review discusses the application of protein-based NDS in food product development and highlights challenges and opportunities for future research. Key Findings: Plant-based NDS is gaining recognition in food research and industry for their role in improving the shelf life and bioavailability of bioactives. However, concerns about safety and possible toxicity limit their widespread application. Future research efforts that focus on mitigating or enhancing their safety for food applications is warranted.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".