Challenges hindering the commercialization of nutraceuticals derived from agri-food by-products
Bibliographic record
Abstract
Several agri-food by-products carry a significant amount of bioactive compounds and could potentially be transformed into nutraceuticals within the circular economy framework. However, the full realization of this potential is hindered by logistics, technological, biological, and regulatory challenges, slowing down the development of a robust nutraceutical market. The present article discusses the need for innovative solutions to optimize waste collection and transportation. The technological challenges in extracting and preserving bioactive compounds call for advancements in unconventional extraction methods and encapsulation approaches. Biological challenges, particularly regarding the bioaccessibility and bioavailability of bioactive compounds, underscore the importance of tailoring delivery methods for optimal efficacy. In addition, selected regulatory aspects need to be highlighted in order to clarify the need for harmonization in ensuring the safety and efficacy of nutraceuticals. Despite challenges, the potential rewards include health benefits, economic growth, and environmental sustainability, driven by the pivotal role of scientific research and interdisciplinary collaboration to realize the vision of a circular economy in the agri-food sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".