Valorization of Agri-Food By-Products from Plant Sources Using Pressure-Driven Membrane Processes to Recover Value-Added Compounds: Opportunities and Challenges
Bibliographic record
Abstract
Agri-food by-products are defined as secondary products derived from food manufacturing processes. They can be of plant or animal origin. When agri-food by-products are not valorized, they regularly end up in landfills or rivers and create pollution problems. However, many of these by-products contain high value-added compounds, including carbohydrates, oligosaccharides, proteins, peptides, fibers, phenolic compounds, and isoflavones, which can be recovered. In this context, membrane processes such as microfiltration, ultrafiltration, nanofiltration, and reverse osmosis are of interest for the valorization of agri-food by-products. In this review, the advantages and limitations of membrane processes for the valorization of agrifood by-products from plant sources are discussed as well as some research avenues to reduce membrane fouling, which remains the main limitation for large-scale industrial applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".