Spray-Deposited Epigallocatechin Gallate-Based Metal–Phenolic Networks as Innovative Edible Coatings for Fresh Produce Preservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it