ReviewFoods (Basel, Switzerland)2026
Advances in Hyperspectral Imaging for Nondestructive Food Quality and Safety Detection.
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.
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.
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.
Who cites it
4 citing papers in PubMed.
- Meat Freshness Monitoring in the Modern Supply Chain: Analytical Advances, Smart Packaging, and Digital Integration.Food science & nutrition · 2026Review
- Hyperspectral Imaging for Agricultural and Food Sensing: Applications, Data Analytics, and Deployment Challenges.Foods (Basel, Switzerland) · 2026Review
- Advances in Machine Learning and Deep Learning Algorithm-Assisted Hyperspectral Imaging for Food Quality and Safety Assessment.Foods (Basel, Switzerland) · 2026Review
- Automated Individual-Level ROI-to-Spectrum Extraction for Hyperspectral Analysis in Forensic Entomology.Insects · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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
Identifiers
What OpenQuestion holds
Registered trials
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.