Evidence map›Paper›PMID 41623976›Full record

ArticleFood chemistry: X2026

Chitosan-PVA composite films incorporated with roselle anthocyanins: Colorimetric smart sensor for real-time monitoring of meat and seafood freshness.

Alfianita Nuril Hidayaty, Saidun Fiddaroini, Ahmad Luthfi Fahmi, Stephani Rebecca Amabel Siahaan, Qonitah Fardiyah, Arie Srihardyastutie, Akhmad Sabarudin

Abstract read
In one paragraph

Article in Food chemistry: X, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Valorization ofRSC advances · 2026
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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

7 authors.

Alfianita Nuril HidayatyDepartment of Chemistry, Faculty of Science, Brawijaya University, Malang, Indonesia.
Saidun FiddaroiniDepartment of Chemistry, Faculty of Science, Brawijaya University, Malang, Indonesia.
Ahmad Luthfi FahmiDepartment of Chemistry, Faculty of Science, Brawijaya University, Malang, Indonesia.
Stephani Rebecca Amabel SiahaanDepartment of Chemistry, Faculty of Science, Brawijaya University, Malang, Indonesia.
Qonitah FardiyahDepartment of Chemistry, Faculty of Science, Brawijaya University, Malang, Indonesia.
Arie SrihardyastutieDepartment of Chemistry, Faculty of Science, Brawijaya University, Malang, Indonesia.
Akhmad SabarudinDepartment of Chemistry, Faculty of Science, Brawijaya University, Malang, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ensuring freshness in perishable foods like meat and seafood is vital for food safety. This study developed a biodegradable colorimetric film from chitosan-polyvinyl alcohol (Cs/PVA) infused with roselle anthocyanin extract (CPAR) to monitor spoilage. Among variants, CPAR-5 (5 mL extract) showed the highest pH sensitivity (ΔE = 44.69, S% = 42%) and rapid response to ammonia vapor. Characterization via UV-Vis, FTIR, and FESEM confirmed effective pigment integration and film structure. Applied to chicken, shrimp, and tilapia, CPAR-5 visually tracked spoilage in real time under ambient and cold storage, correlating with critical pH thresholds (≥6.8 for chicken, ≥7.4 for shrimp, ≥7.0 for fish). A visual card reader was developed for semi-quantitative freshness evaluation, demonstrating that CPAR-5 is a highly sensitive, robust, user-friendly, and environmentally sustainable smart indicator with strong potential for practical food safety monitoring and reduce food waste.

Indexed as

Chitosan–PVA compositeColorimetric filmFood freshness monitoringRoselle anthocyaninSmart sensor

Identifiers

PMID41623976
PMCPMC12853055

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.