Xanthan Gum‐Tragacanth Gum Nanofiber Mats Containing <scp>ZnO</scp> ‐Carbon Dots/Anthocyanins Extracted From <scp> <i>Frangula alnus</i> </scp> to Monitor the Freshness of Peeled Shrimps
Bibliographic record
Abstract
ABSTRACT To fulfill people's requirements for food quality and safety, the development of intelligent biodegradable food packaging biopolymers is a hopeful strategy. Herein, xanthan gum‐tragacanth gum (XG‐TG) nanofiber mats treated with ZnO‐carbon dots/anthocyanins derived from Frangula alnus extract (FAE) were fabricated to control shrimp freshness during chilled storage for 6 days. XG‐TG + FAE 3%, XG‐TG + ZnO‐carbon dots 0.25%, and XG‐TG + FAE 3% + ZnO‐carbon dots 0.25% nanofiber mats had higher tensile strength (4.66–7.69 MPa) along with lower elongation at break (13.25%–19.32%), moisture content (2.09%–3.25%), water solubility (15.40%–27.30%), and water vapor permeability (12.09–23.17 × 10 −5 g mm/m 2 h Pa) than the untreated group. The XG‐TG + FAE 3% and XG‐TG + FAE 3% + ZnO‐carbon dots 0.25% nanofiber mats presented distinct color changes at pH 1–10 as follows: red at pH 1–4, purple at pH 5–6, blue at pH 7, green at pH 8, and yellow‐brown at pH 9–10. In addition, the color of the corresponding nanofiber mats changed from white to blue as the shrimp began to degrade after 4 days of refrigerated storage. Meanwhile, the total viable count, total psychrotrophic bacterial count, total volatile basic nitrogen, and pH of peeled shrimp reached 7.16 log CFU/g, 6.09 log CFU/g, 20.80 mg N/100 g, and 7.71, respectively, indicating the application of the produced pH‐responsive nanofibrous polymers in shrimp freshness evaluation.
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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".