Natural plant fibers obtained from agricultural residue used as an ingredient in food matrixes or packaging materials: A review
Bibliographic record
Abstract
Every year, agrifood activities generate a large amount of plant byproducts, which have a low economical value. However, the valorization of these byproducts can contribute to increasing the intake of dietary fibers and reducing the environmental pollution. This review presents an overview of a wide variety of agricultural wastes applied in the formulation of different food products and sustainable packaging. In general, the incorporation of fibers into bakery, meat, and dairy products was successful, especially at a level of 10% or less. Fibers from a variety of crops improved the consistency, texture, and stability of sauce formulations without affecting sensory quality. In addition, fiber fortification (0.01-6.4%) presented considerable advantages in terms of rheology, texture, melting behavior, and fat replacement of ice cream, but in some cases had a negative impact on color and mouthfeel. In the case of beverages, promising effects on texture, viscosity, stability, and appetite control were obtained by the addition of soluble dietary fibers from grains and fruits with small particle size. Biocomposites used in packaging benefited from reinforcing effects of various plant fiber sources, but the extent of modification depended on the matrix type, fiber pretreatment, and concentration. The information synthesized in this contribution can be used as a tool to screen and select the most promising fiber source, fiber concentration, and pretreatment for specific food applications and sustainable packaging.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".