ReviewFood engineering reviews2022
Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain.
Review in Food engineering reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Review
- Advances in Sensing Techniques and Deep Learning for Food Quality Detection: Opportunities, Challenges, and Perspectives from Image Recognition to Multimodal Data Fusion.Foods (Basel, Switzerland) · 2026Review
- Spectral-Structural Decoupled Hypergraph Neural Network forFoods (Basel, Switzerland) · 2026Article
- A two-stage hierarchical support vector machine framework detects roasted coffee adulterants through principal component analysis of hyperspectral imaging data.Scientific reports · 2026Article
- Transformative artificial intelligence integration in aquatic supply chains: synergizing precision aquaculture with intelligent logistics and data-driven consumption.Food chemistry: X · 2026Review
- Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications.Current research in food science · 2026Review
- Research on Geographical Origin Traceability ofPlants (Basel, Switzerland) · 2025Article
- Hyperspectral Imaging-Based Deep Learning Method for Detecting Quarantine Diseases in Apples.Foods (Basel, Switzerland) · 2025Article
- Hyperspectral Imaging Techniques for Lyophilization: Advances in Data-Driven Modeling Strategies and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Advances in Hyperspectral Imaging Technology for Grain Quality and Safety Detection: A Review.Foods (Basel, Switzerland) · 2025Review
- Spectroscopic and Imaging Technologies Combined with Machine Learning for Intelligent Perception of Pesticide Residues in Fruits and Vegetables.Foods (Basel, Switzerland) · 2025Review
- Development of a digital micromirror device-based hyperspectral imaging system with dynamically adjustable measurement regions.Scientific reports · 2025Article
- Advances in Food Quality Management Driven by Industry 4.0: A Systematic Review-Based Framework.Foods (Basel, Switzerland) · 2025Review
- Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.Foods (Basel, Switzerland) · 2025Review
- A Comprehensive Review of Artificial Intelligence (AI)-Driven Approaches to Meat Quality and Safety.Food science of animal resources · 2025Review
- Nondestructive Detection and Quality Grading System of Walnut Using X-Ray Imaging and Lightweight WKNet.Foods (Basel, Switzerland) · 2025Article
- Conventional Near-Infrared Spectroscopy and Hyperspectral Imaging: Similarities, Differences, Advantages, and Limitations.Molecules (Basel, Switzerland) · 2025Review
- Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.Foods (Basel, Switzerland) · 2025Review
- A review of robotic and automated systems in meat processing.Frontiers in robotics and AI · 2025Review
- Advancements in Intelligent Sensing Technologies for Food Safety Detection.Research (Washington, D.C.) · 2025Review
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
No grant is acknowledged in the PubMed record.
Abstract
Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.
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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.