The applications of conventional and innovative mechanical technologies to tailor structural and functional features of dietary fibers from plant wastes: A review
Bibliographic record
Abstract
Plant food wastes generated through the food chain have attracted increasing attention over the last few years not only due to critical environmental and economic issues but also as an available source of valuable components such as dietary fibers. However, the exploitation of plant waste remains limited due to the lack of appropriate processing technologies to recover and tailor fiber functionalities. Among the different technologies developed for waste valorization, mechanical techniques were suggested to be a promising and sustainable strategy to extract fibers with improved functionalities. In this context, the present review describes different mechanical technologies (conventional and innovative) with potential applications to produce micro/nanofibers from various plant residues, highlighting the operating principle as well as the main advantages and pitfalls. The impact on the structural, technological, and functional properties of fibrous materials is comprehensively discussed. The extent of fiber modification not only highly depended on the technology and operation conditions used but also on fiber composition and the application of posttreatments such as dehydration. Other variables, including economic and environmental issues such as equipment cost, energy demand, and eco-friendly features, are also reviewed. The outputs of this review can be used by both the industrial sector and academia to select a suitable combination of fiber and processing technology for designing novel foods with improved functionalities that fulfill market trends and consumer needs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".