ArticleMolecules (Basel, Switzerland)2018
Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins.Frontiers in nutrition · 2021Review
- Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds.RSC advances · 2020Article
- Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods.Frontiers in plant science · 2020Article
- Application of Laser-Induced Breakdown Spectroscopy in Detection of Cadmium Content in Rice Stems.Frontiers in plant science · 2020Article
- A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning.Molecules (Basel, Switzerland) · 2019Article
- Advances in Near-Infrared Spectroscopy and Related Computational Methods.Molecules (Basel, Switzerland) · 2019Article
- Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology.Sensors (Basel, Switzerland) · 2019Article
- Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network.Sensors (Basel, Switzerland) · 2019Article
- NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface.Sensors (Basel, Switzerland) · 2019Article
- Combining Fourier Transform Mid-Infrared Spectroscopy with Chemometric Methods to Detect Adulterations in Milk Powder.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
6 authors.
Funding
Abstract
Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874⁻1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties.
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