ReviewCurrent research in food science2021
Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review.
Review in Current research in food science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.
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Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- GMoE-AD: Generalized Hyperspectral Anomaly Detection via Mixture-of-Experts and Domain-Invariant Learning.Sensors (Basel, Switzerland) · 2026Article
- Carbohydrate and enzymatic activity profiling for the quality assessment of Canadian honeys.Food chemistry: X · 2026Article
- Spectral-Structural Decoupled Hypergraph Neural Network forFoods (Basel, Switzerland) · 2026Article
- AI-Driven Nondestructive Measurement Technologies for Meat Quality and Safety: A Review.Foods (Basel, Switzerland) · 2026Review
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Non-Targeted Hyperspectral Imaging Screening of Adulterants and Congeneric Species inFoods (Basel, Switzerland) · 2026Article
- A comparative ML approach to classify Lupinus species using VIS-NIR spectral data from whole seeds and various data transformation techniques and resampling methods.Scientific reports · 2026Article
- Enhancing spectral analysis of ghee adulteration via a deep learning-based multimodal attention mechanism.Scientific reports · 2026Article
- KG-FT-Transformer: a knowledge-guided feature tokenizer transformer for hyperspectral prediction of lettuce quality trait and fingerprint analysis.Food chemistry: X · 2026Article
- The Ability of Vibrational Spectroscopy to Analyze Holistically the Food Matrix-Moving Away from the Concept of Individual Compounds.Methods and protocols · 2026Review
- Quantitative analysis and visualization of live spotted seabass (Food chemistry: X · 2026Article
- The contribution of vibrational spectroscopy and data analytics towards the achievement of the global sustainability goals.Journal of the science of food and agriculture · 2026Review
- Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples.Foods (Basel, Switzerland) · 2026Review
- Rheological analysis in food processing: factors, applications, and future outlooks with machine learning integration.RSC advances · 2026Review
- Investigation and Rapid Determination of Storage-Induced Multidimensional Quality Deterioration of Wenyujin Rhizoma Concisum.Foods (Basel, Switzerland) · 2026Article
- Computational QSAR and structure-based identification of plerixafor-derived PIM-1 kinase inhibitors in diffuse large B-Cell lymphoma.Frontiers in chemistry · 2026Article
- Precise Discrimination Between Rape Honey and Acacia Honey Based on Sugar and Amino Acid Profiles Combined with Machine Learning.Foods (Basel, Switzerland) · 2025Article
- Assessing and optimizing Team-Based Learning in undergraduate cosmetic dermatology education: an empirical study using interpretable machine learning.BMC medical education · 2025Article
- Machine Learning-Driven Precision Nutrition: A Paradigm Evolution in Dietary Assessment and Intervention.Nutrients · 2025Review
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.
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