Tuning the mechanical properties of pectin films with polyphenol-rich plant extracts
Bibliographic record
Abstract
The mechanical properties of pectin films enhanced with polyphenol-rich fruit extracts were investigated. The scavenging and reducing activity of plant extracts incorporated into the pectin films were determined using bench assays, and their antioxidant activity was correlated with a high presence of polyphenols, which were predominantly comprised of flavonoids and anthocyanins. The pectin films generated from the extracts exhibited a range of mechanical properties; tensile strength (4.99 MPa – 6.91 MPa), elongation at break (45.8 % - 52.3 %), and stiffness (1835 g mm−1 - 2765 g mm−1). To investigate the underlying relationships between plant extract composition and mechanical properties, Projection to Latent Structures (PLS) models were developed. The PLS models revealed that extracts containing high sugar and polyphenol content increase the tensile strength and moisture content of films. The elongation at break of the films was improved or diminished depending on the profile of sugar, acids, and polyphenols in the fruit extracts. Furthermore, the structures and concentration of anthocyanins and flavonoids were identified to strongly influenced the elongation at break differences. By modifying the concentration of sugars, organic acids, and polyphenols, the mechanical properties of pectin-based films can be tuned for tailored applications as food packaging materials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".