Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
Chinese walnuts have extraordinary nutritional and organoleptic qualities, and counterfeit Chinese walnut products are pervasive in the market. The aim of this study was to investigate the feasibility of hyperspectral imaging (HSI) technique to accurately identify and visualize Chinese walnut variet...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/19/9124 |
| _version_ | 1797516824394334208 |
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| author | Hongzhe Jiang Liancheng Ye Xingpeng Li Minghong Shi |
| author_facet | Hongzhe Jiang Liancheng Ye Xingpeng Li Minghong Shi |
| author_sort | Hongzhe Jiang |
| collection | DOAJ |
| description | Chinese walnuts have extraordinary nutritional and organoleptic qualities, and counterfeit Chinese walnut products are pervasive in the market. The aim of this study was to investigate the feasibility of hyperspectral imaging (HSI) technique to accurately identify and visualize Chinese walnut varieties. Hyperspectral images of 400 Chinese walnuts including 200 samples of Ningguo variety and 200 samples of Lin’an variety were acquired in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and principal component analysis (PCA) of spectra showed that the characteristic second principal component (PC<sub>2</sub>) was potentially effective in variety identification. The PC transformation was also conducted to hyperspectral images to make an exploratory visualization according to pixel-wise PC scores. Three different modeling methods including partial least squares-discriminant analysis (PLS-DA), <i>k</i>-nearest neighbor (KNN), and support vector machine (SVM) were individually employed to develop classification models. Results indicated that raw full spectra constructed PLS-DA model performed best with correct classification rates (CCRs) of 97.33%, 95.33%, and 92.00% in calibration, cross-validation, and prediction sets, respectively. Successful projects algorithm (SPA), competitive adaptive reweighted sampling (CARS), and PC loadings were individually used for effective wavelengths selection. Subsequently, simplified PLS-DA model based on wavelengths selected by CARS yielded the best 96.33%, 95.67% and 91.00% CCRs in the three sets. This optimal CARS-PLS-DA model acquired a sensitivity of 93.62%, a specificity of 88.68%, the area under the receiver operating characteristic curve (AUC) value of 0.91, and Kappa coefficient of 0.82 in prediction set. Classification maps were finally generated by classifying the varieties of each pixel in multispectral images at CARS-selected wavelengths, and the general variety was then readily discernible. These results demonstrated that features extracted from HSI had outstanding ability, and could be applied as a reliable tool for the further development of an on-line identification system for Chinese walnut variety. |
| first_indexed | 2024-03-10T07:06:15Z |
| format | Article |
| id | doaj.art-f36f160efd84431e83f7227dac3d6e2e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:06:15Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f36f160efd84431e83f7227dac3d6e2e2023-11-22T15:48:03ZengMDPI AGApplied Sciences2076-34172021-09-011119912410.3390/app11199124Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with ChemometricsHongzhe Jiang0Liancheng Ye1Xingpeng Li2Minghong Shi3College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaChinese walnuts have extraordinary nutritional and organoleptic qualities, and counterfeit Chinese walnut products are pervasive in the market. The aim of this study was to investigate the feasibility of hyperspectral imaging (HSI) technique to accurately identify and visualize Chinese walnut varieties. Hyperspectral images of 400 Chinese walnuts including 200 samples of Ningguo variety and 200 samples of Lin’an variety were acquired in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and principal component analysis (PCA) of spectra showed that the characteristic second principal component (PC<sub>2</sub>) was potentially effective in variety identification. The PC transformation was also conducted to hyperspectral images to make an exploratory visualization according to pixel-wise PC scores. Three different modeling methods including partial least squares-discriminant analysis (PLS-DA), <i>k</i>-nearest neighbor (KNN), and support vector machine (SVM) were individually employed to develop classification models. Results indicated that raw full spectra constructed PLS-DA model performed best with correct classification rates (CCRs) of 97.33%, 95.33%, and 92.00% in calibration, cross-validation, and prediction sets, respectively. Successful projects algorithm (SPA), competitive adaptive reweighted sampling (CARS), and PC loadings were individually used for effective wavelengths selection. Subsequently, simplified PLS-DA model based on wavelengths selected by CARS yielded the best 96.33%, 95.67% and 91.00% CCRs in the three sets. This optimal CARS-PLS-DA model acquired a sensitivity of 93.62%, a specificity of 88.68%, the area under the receiver operating characteristic curve (AUC) value of 0.91, and Kappa coefficient of 0.82 in prediction set. Classification maps were finally generated by classifying the varieties of each pixel in multispectral images at CARS-selected wavelengths, and the general variety was then readily discernible. These results demonstrated that features extracted from HSI had outstanding ability, and could be applied as a reliable tool for the further development of an on-line identification system for Chinese walnut variety.https://www.mdpi.com/2076-3417/11/19/9124hyperspectral imagingChinese walnutsvariety classificationidentification modelsvisualization |
| spellingShingle | Hongzhe Jiang Liancheng Ye Xingpeng Li Minghong Shi Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics Applied Sciences hyperspectral imaging Chinese walnuts variety classification identification models visualization |
| title | Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics |
| title_full | Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics |
| title_fullStr | Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics |
| title_full_unstemmed | Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics |
| title_short | Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics |
| title_sort | variety identification of chinese walnuts using hyperspectral imaging combined with chemometrics |
| topic | hyperspectral imaging Chinese walnuts variety classification identification models visualization |
| url | https://www.mdpi.com/2076-3417/11/19/9124 |
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