Evidence map›Paper›PMID 36249982›Full record

ArticleFood science & nutrition2022

Fabrication and characterization of a smart film based on cassava starch and pomegranate peel powder for monitoring lamb meat freshness.

Azadeh Esfahani, Abdorreza Mohammadi Nafchi, Homa Baghaei, Leila Nouri

Abstract read
In one paragraph

Article in Food science & nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Sago Starch Bionanocomposite Films WithFood science & nutrition · 2026
    Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Reflections on food security and smart packaging.Polymer bulletin (Berlin, Germany) · 2023
    Review
  16. Article
  17. Article
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Azadeh EsfahaniDepartment of Food Science and Technology Damghan Branch Islamic Azad University Damghan Iran.
Abdorreza Mohammadi NafchiDepartment of Food Science and Technology Damghan Branch Islamic Azad University Damghan Iran.ORCID https://orcid.org/0000-0002-6065-5098
Homa BaghaeiDepartment of Food Science and Technology Damghan Branch Islamic Azad University Damghan Iran.
Leila NouriDepartment of Food Science and Technology Damghan Branch Islamic Azad University Damghan Iran.ORCID https://orcid.org/0000-0002-2290-2799

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, the development of pH-sensitive smart edible films using biopolymers and natural plant extracts (especially those rich in anthocyanins) has attracted much attention. Therefore, in this study, the intelligent edible film was produced and characterized using cassava starch and pomegranate peel powder (PPP) and the possibility of using production films to monitor the freshness of lamb meat. The smart films were prepared using different concentrations of PPP (2, 4, 6, and 8% w/w) and the solvent casting method. The results showed that the incorporation of PPP had a significant effect on the mechanical parameters of the starch films. With increasing the levels of PPP, the color of the films became darker and redder. Increasing the PPP levels also led to an increase in total phenol content (TPC) (from 0 to 13 mg GAE (gallic acid equivalent)/g) and antioxidant activity (from 0% to 70% DPPH (1,1-diphenyl-2-picryl hydrazyl) radical scavenging) of the produced films (

Indexed as

active packagingantioxidant activitycassava starchintelligent filmpomegranate peelTVB‐N

Identifiers

PMID36249982
PMCPMC9548365

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.