Evidence map›Paper›PMID 41415173›Full record

ReviewPlant phenomics (Washington, D.C.)2025

Insights of freshness phenotype detection for postharvest fruit and vegetables.

Qiankun Wang, Hui He, Chenxia Liu, Chunfang Wang, Bingjie Chen, Xiao Wang, Qingfeng Niu, Ke Wang, Wenxin Zhu, Yongjin Qiao and 1 more

Abstract readReview
In one paragraph

Review in Plant phenomics (Washington, D.C.), 2025. 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. Review
  2. Review
  3. 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

11 authors.

Qiankun WangCrop Breeding & Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, 1000 Jinqi Road, Fengxian District, Shanghai, 201403, PR China.
Hui HeCrop Breeding & Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, 1000 Jinqi Road, Fengxian District, Shanghai, 201403, PR China.
Chenxia LiuCrop Breeding & Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, 1000 Jinqi Road, Fengxian District, Shanghai, 201403, PR China.
Chunfang WangCrop Breeding & Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, 1000 Jinqi Road, Fengxian District, Shanghai, 201403, PR China.
Bingjie ChenCrop Breeding & Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, 1000 Jinqi Road, Fengxian District, Shanghai, 201403, PR China.
Xiao WangCrop Breeding & Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, 1000 Jinqi Road, Fengxian District, Shanghai, 201403, PR China.
Qingfeng NiuSchool of Life Sciences, Anhui Agricultural University, Hefei, 230036, PR China.
Ke WangKey Laboratory of Jianghuai Agricultural Product Fine Processing and Resource Utilization of Ministry of Agriculture and Rural Affairs / Anhui Engineering Laboratory for Agro-products Processing, Anhui Agricultural University, Hefei 230036, China.
Wenxin ZhuCollege of Food Science and Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, PR China.
Yongjin QiaoCrop Breeding & Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, 1000 Jinqi Road, Fengxian District, Shanghai, 201403, PR China.
Hongru LiuCrop Breeding & Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, 1000 Jinqi Road, Fengxian District, Shanghai, 201403, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The freshness phenotype of fruit and vegetables is a critical determinant of consumer satisfaction, selection, and public health, which plays a pivotal role in postharvest quality management. This paper presents a review of the definition and detection techniques used to assess and maintain this vital freshness phenotype. Advanced intelligent packaging technologies, that incorporate sensors, indicators, and data carrier systems, and their roles in dynamically monitoring the freshness phenotype during storage and transportation are discussed. The integration of nondestructive testing (NDT) methods such as near-infrared spectroscopy (NIR), hyperspectral imaging (HSI), machine vision, and light detection and ranging (LiDAR) offers real-time, precise assessments of the freshness phenotype without compromising the integrity of the produce. By understanding the underlying mechanisms of the fruit and vegetable freshness phenotype, this paper discusses the definition, detection technologies, and gaps that require further research. The integration of advanced quantitative models with NDT and intelligent packaging solutions has the potential to reduce food waste. This advancement will lead to better quality control, extended shelf life, and increased consumer confidence in fresh produce, driving innovation and application within the food industry.

Indexed as

Difference between quality and freshnessIntelligent packagingNondestructive testingQuality indicatorsShelf-life prediction

Identifiers

PMID41415173
PMCPMC12709966

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.