ReviewFrontiers in nutrition2021
Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins.
Review in Frontiers in nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies.Diagnostics (Basel, Switzerland) · 2026Review
- Effect of Temperature on the Drying Kinetics of Caturra Coffee: Correlation with Hyperspectral Imaging and Sensory Quality.Foods (Basel, Switzerland) · 2026Article
- Methodologies for Assessing Chemical Toxicity to Aquatic Microorganisms: A Comparative Review.Molecules (Basel, Switzerland) · 2026Review
- Non-Destructive Geographical Traceability and Quality Control ofFoods (Basel, Switzerland) · 2026Article
- Near-infrared hyperspectral imaging combined with elemental and isotopic analyses reflects geographical origins of cultured salmonids.Current research in food science · 2026Article
- Spectral Analysis of Extrahepatic Bile Ducts During Normothermic Liver Machine Perfusion.Bioengineering (Basel, Switzerland) · 2025Article
- Hyperspectral Imaging Techniques for Lyophilization: Advances in Data-Driven Modeling Strategies and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Effective Identification of Variety and Origin of Chenpi Using Hyperspectral Imaging Assisted with Chemometric Models.Foods (Basel, Switzerland) · 2025Article
- Evolving multispectral sensor configurations using genetic programming for estuary health monitoring.Journal of the Royal Society of New Zealand · 2025Article
- Freshness in Salmon by Hand-Held Devices: Methods in Feature Selection and Data Fusion for Spectroscopy.ACS food science & technology · 2024Article
- Fully connected-convolutional (FC-CNN) neural network based on hyperspectral images for rapid identification of P. ginseng growth years.Scientific reports · 2024Article
- Handheld hyperspectral imaging as a tool for the post-mortem interval estimation of human skeletal remains.Heliyon · 2024Article
- Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging.Frontiers in nutrition · 2024Article
- A Geographical Origin Classification of Durian (cv. Monthong) Using Near-Infrared Diffuse Reflectance Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review.Molecules (Basel, Switzerland) · 2023Review
- Hyperspectral dimension reduction and navel orange surface disease defect classification using independent component analysis-genetic algorithm.Frontiers in nutrition · 2022Article
- Establishment of online deep learning model for insect-affected pests in "Yali" pears based on visible-near-infrared spectroscopy.Frontiers in nutrition · 2022Article
- Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification.Current research in food science · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.
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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 Socratic graph.