ReviewFoods (Basel, Switzerland)2023
Applications of Gas Sensing in Food Quality Detection: A Review.
Review in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Smart Chemical Sensors for Monitoring and Detection of Spoilage in Fermented and Non-Fermented Food Products.Sensors (Basel, Switzerland) · 2026Review
- The Volatile Signature: Tracking Ripening Dynamics to Ensure Goat Cheese Quality.Sensors (Basel, Switzerland) · 2026Article
- Recent progress of gas sensors toward olfactory display development.Nano convergence · 2025Review
- Metal oxide heterostructure towards gas sensing of trimethylamine: recent progress and challenges.RSC advances · 2025Review
- Indole Schiff Base Complex: Synthesis and Optical Binding Investigation with Biogenic Amines.Journal of fluorescence · 2025Article
- Conducting Polymers-Based Gas Sensors: Principles, Materials, and Applications.Sensors (Basel, Switzerland) · 2025Review
- Article
- Review
- Sensitivity Analysis of an Optical Interferometric Surface Stress Ethanol Gas Sensor with a Freestanding Nanosheet.Sensors (Basel, Switzerland) · 2024Article
- Unraveling the Chicken Meat Volatilome with Nanostructured Sensors: Impact of Live and Dehydrated Insect Larvae Feeding.Sensors (Basel, Switzerland) · 2024Article
- Distinguishing between Wheat Grains Infested by FourSensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.
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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.