ArticleMolecules (Basel, Switzerland)2018
Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Comparative classification of spectrally overlapping Allium seed genotypes using Vis-NIR spectroscopy and hyperspectral imaging with chemometric, machine, and deep learning models.Scientific reports · 2026Article
- Non-invasive assessment of cultivar and sex ofPlant-environment interactions (Hoboken, N.J.) · 2023Article
- Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection.Foods (Basel, Switzerland) · 2023Article
- Single Seed Near-Infrared Hyperspectral Imaging for Classification of Perennial Ryegrass Seed.Sensors (Basel, Switzerland) · 2023Article
- Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review.Foods (Basel, Switzerland) · 2022Review
- PredictingFrontiers in plant science · 2022Article
- Hyperspectral imaging with machine learning for non-destructive classification ofFrontiers in plant science · 2022Article
- Automated In Situ Seed Variety Identification via Deep Learning: A Case Study in Chickpea.Plants (Basel, Switzerland) · 2021Article
- Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins.Frontiers in nutrition · 2021Review
- Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods.Foods (Basel, Switzerland) · 2020Article
- Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques.Molecules (Basel, Switzerland) · 2019Article
- Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis.Molecules (Basel, Switzerland) · 2019Article
- Hyperspectral imaging for seed quality and safety inspection: a review.Plant methods · 2019Review
- Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis.Sensors (Basel, Switzerland) · 2018Article
- Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging.Molecules (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Hyperspectral images in the spectral range of 874⁻1734 nm were collected for 14,015, 14,300 and 15,042 grape seeds of three varieties, respectively. Pixel-wise spectra were preprocessed by wavelet transform, and then, spectra of each single grape seed were extracted. Principal component analysis (PCA) was conducted on the hyperspectral images. Scores for images of the first six principal components (PCs) were used to qualitatively recognize the patterns among different varieties. Loadings of the first six PCs were used to identify the effective wavelengths (EWs). Support vector machine (SVM) was used to build the discriminant model using the spectra based on the EWs. The results indicated that the variety of each single grape seed was accurately identified with a calibration accuracy of 94.3% and a prediction accuracy of 88.7%. An external validation image of each variety was used to evaluate the proposed model and to form the classification maps where each single grape seed was explicitly identified as belonging to a distinct variety. The overall results indicated that a hyperspectral imaging (HSI) technique combined with multivariate analysis could be used as an effective tool for non-destructive and rapid variety discrimination and visualization of grape seeds. The proposed method showed great potential for developing a multi-spectral imaging system for practical application in the future.
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