ReviewFoods (Basel, Switzerland)2022
Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review.
Review in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 61 citations in OpenAlex.
- Automated fruit maturity grading using deep learning with feature fusion.Scientific reports · 2026Article
- Close-Range 3D Hyperspectral Measurement System with a Physics-Guided Spectral Correction Model.Sensors (Basel, Switzerland) · 2026Article
- Study on Quality Detection Methods for Table Grapes Based on Spectral and Imaging Information.Sensors (Basel, Switzerland) · 2026Article
- DualStream-RTNet: A Multimodal Deep Learning Framework for Grape Cultivar Classification and Soluble Solid Content Prediction.Foods (Basel, Switzerland) · 2026Article
- Hyperspectral Imaging for Foreign Matter Detection in Foods: Advances, Challenges, and Future Directions.Foods (Basel, Switzerland) · 2025Review
- Determination of optimal sampling time of grape embryo rescue based on near infrared spectroscopy combined with machine learning.Plant phenomics (Washington, D.C.) · 2025Article
- A precise berry counting method for in-cluster grapes to guide berry thinning.Frontiers in plant science · 2025Article
- UV Hyperspectral Imaging with Xenon and Deuterium Light Sources: Integrating PCA and Neural Networks for Analysis of Different Raw Cotton Types.Journal of imaging · 2024Article
- Sub-Terahertz Imaging-Based Real-Time Non-Destructive Inspection System for Estimating Water Activity and Foreign Matter Depth in Seaweed.Sensors (Basel, Switzerland) · 2024Article
- Nutrient Content Prediction and Geographical Origin Identification of Bananas by Combining Hyperspectral Imaging with Chemometrics.Foods (Basel, Switzerland) · 2024Article
- Varroa Mite Counting Based on Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2024Article
- Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy.Food science & nutrition · 2024Article
- Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears.Plants (Basel, Switzerland) · 2024Article
- Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology.Foods (Basel, Switzerland) · 2023Review
- Rapid Prediction of Adulteration Content inFoods (Basel, Switzerland) · 2023Article
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 at 2 institutions in 1 country.
Funding
Abstract
Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.
Indexed as
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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.