A novel film based on gluten, pectin, and polyphenols and its potential application in high‐fat food
Bibliographic record
Abstract
BACKGROUND: A novel film based on some natural ingredients (wheat gluten, pectin, and polyphenols) was used to improve the quality and storage stability of high-fat food due to their good sustainable, mechanical, and edible properties. RESULTS: With the addition of polyphenols from Cedrus deodara (in the form of pine-needle extract (PNE)), the physicochemical properties (thickness, moisture content, and color), mechanical properties (tensile strength and elongation), barrier properties (water vapor, oil, and oxygen permeability, transmittance), and thermal stability of the composite film were improved. According to the analysis of infrared spectroscopy and molecular docking, the main compounds of PNE interacted with wheat gluten by hydrogen bonds and hydrophobic forces to form a compact and stable structure. In addition, the composite film showed a remarkable antioxidant capability to scavenge free radicals, and the film matrix could effectively protect the antioxidant activity of PNE. Furthermore, using cured meat as a model, the composite film exhibited a fine packaging performance in high-fat food during storage, which could obviously inhibit the excessive oxidation of fat and protein of cured meat and was beneficial in forming its special flavor. CONCLUSION: Our results suggest that the composite film possessed good properties and had potential for packing of high-fat foods, which could improve the quality and safety of food during processing and storage. © 2023 Society of Chemical Industry.
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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 | 0.000 |
| 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".