Evidence map›Paper›PMID 39766991›Full record

ArticleFoods (Basel, Switzerland)2024

Preparation and Characterization of Bio-Based Freshness Indicator Labels Loaded with Natural Pigments with High Stability and Sensitivity.

Yinglin Tan, Xiao Liu, Zhi Cheng, Qiping Zhan, Liyan Zhao

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

5 authors.

Yinglin TanCollege of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
Xiao LiuCollege of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
Zhi ChengCollege of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
Qiping ZhanCollege of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.ORCID 0000-0002-1043-1010
Liyan ZhaoCollege of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.

Funding

National Key Research and Development Program of China 2023YFF1104904-04National Natural Science Foundation of China 32402086Natural Science Foundation of Jiangsu Province for Youth BK20221022
6 · The paper itself

Abstract

Freshness indicator labels are crucial for food quality monitoring. However, existing labels often lack stability and sensitivity. This study aims to develop a safe freshness indicator label with high stability and sensitivity. By evaluating the pH response characteristics and stability of four natural pigments, purple potato anthocyanin (PA) was identified as having the best color properties. Mixing the more stable alizarin (AL) with PA improved the stability of the pigment solution without reducing sensitivity. These film labels are prepared with three natural biomolecules and polymers that are a two-by-two composite of them: soybean isolate protein, acacia bean gum, and sodium alginate. Through comparisons of ammonia response, color stability, water solubility, and mechanical properties, the soy protein isolate and locust bean gum composite were determined to be the optimal substrate system. The label of soybean protein isolate and locust bean gum was initially applied to the freshness identification of shrimp and chicken. The results show that the label can effectively respond to the spoilage of aquatic products and meat products and has great application potential in the field of food packaging.

Indexed as

freshnessindicator labelnatural colorsensitivitystability

Identifiers

PMID39766991
PMCPMC11727871

What OpenQuestion holds

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Registered trials

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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.