Evidence map›Paper›PMID 42195835›Full record

ReviewFoods (Basel, Switzerland)2026

Advances in Hyperspectral Imaging for Nondestructive Food Quality and Safety Detection.

Fayun Xing, Mingming Chen

Abstract readReview
In one paragraph

Review in Foods (Basel, Switzerland), 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. Review
  2. Review
  3. Review
  4. 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

2 authors.

Fayun XingDepartment of Microelectronics, Jiangsu University, Zhenjiang 212013, China.
Mingming ChenDepartment of Microelectronics, Jiangsu University, Zhenjiang 212013, China.ORCID 0000-0002-5352-8189

Funding

National Natural Science Foundation of Zhenjiang City JC2025003
6 · The paper itself

Abstract

Hyperspectral imaging (HSI) has become a reliable nondestructive method for evaluating food quality and safety, surpassing traditional methods that are typically destructive and labor-intensive. HSI integrates spectral signatures with spatial distribution, enabling real-time, high-sensitivity analysis of both internal and external food attributes. Recently, there has been a growing number of studies focusing on food quality and safety detection using the HSI technique. This review offers a comprehensive summary of advancements in detecting food quality and safety in key areas, such as assessing the quality of fruits, vegetables, meat, grains, and tea; measuring moisture content; identifying variety and geographic origin; detecting adulterants and additives; and evaluating heavy metals and pesticide residues. Additionally, challenges and perspectives, including data dimensionality, the trade-off between signal-to-noise ratio and resolution, hardware costs, and the gap between laboratory research and applications under variable environmental conditions, are proposed. This review highlights the great potential of the HSI technique for rapidly and nondestructively detecting and monitoring food quality and safety in food and agricultural applications.

Indexed as

food qualityfood safetyhyperspectral imagingnondestructive detection

Identifiers

PMID42195835
PMCPMC13206199

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.