Spray-Deposited Epigallocatechin Gallate-Based Metal–Phenolic Networks as Innovative Edible Coatings for Fresh Produce Preservation
Bibliographic record
Abstract
Postharvest spoilage of fresh produce is a major contributor to global food loss, with existing preservation methods often constrained by sustainability or scalability. Metal–phenolic networks (MPNs), formed through coordination between metal ions and polyphenols, offer a promising alternative due to their inherent antioxidant and antimicrobial properties. This study presents a systematic evaluation of epigallocatechin gallate (EGCG)-based MPN coatings for fresh produce preservation, focusing on the effects of varying concentrations and metal ion types under controlled conditions. Using strawberries as a model, spray-applied Fe 3+ –EGCG and Zn 2+ –EGCG coatings delayed spoilage by at least 1.3-fold while maintaining key quality indicators. Notably, Zn–EGCG coatings reduced weight loss by up to 27% and retained 21% more firmness compared to uncoated controls over 5 days. While Zn–EGCG coatings, particularly at higher concentrations, demonstrated superior oxidative stability and moisture barrier properties, Fe–EGCG coatings showed reduced performance over time, likely due to iron-induced redox activity. Antibacterial assays showed Fe–EGCG to be more potent than Zn–EGCG, but high-concentration Zn–EGCG also inhibited both Gram-positive and Gram-negative bacteria. These findings highlight EGCG-based MPNs as an effective, scalable, and biocompatible strategy for extending shelf life and reducing postharvest food waste.
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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) | 0.000 | 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".