Hyperspectral Imaging for Prediction and Distribution Visualization of Total Acidity and Hardness of Red Globe Grapes
In this paper, hyperspectral imaging technology was used for nondestructive detection and distribution visualization of total acidity and firmness of red globe grapes. The hyperspectral information of 360 samples of growing red globe grapes in the wavelength range from 450 to 1 000 nm was collected...
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| Format: | Article |
| Language: | English |
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China Food Publishing Company
2023-01-01
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| Series: | Shipin Kexue |
| Subjects: | |
| Online Access: | https://www.spkx.net.cn/fileup/1002-6630/PDF/2023-44-2-041.pdf |
| _version_ | 1811159495357759488 |
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| author | GAO Sheng, XU Jianhua |
| author_facet | GAO Sheng, XU Jianhua |
| author_sort | GAO Sheng, XU Jianhua |
| collection | DOAJ |
| description | In this paper, hyperspectral imaging technology was used for nondestructive detection and distribution visualization of total acidity and firmness of red globe grapes. The hyperspectral information of 360 samples of growing red globe grapes in the wavelength range from 450 to 1 000 nm was collected using a hyperspectral instrument, and the total acidity and firmness of these samples were determined by titration and a texture analyzer, respectively. The Kennard-Stone (KS) algorithm was used to divide the total samples into a training set (270 samples) and a test set (90 samples) in a 3:1 ratio. The collected raw spectral data were preprocessed using various methods such as standard normal variate (SNV), Savitzky-Golay (SG), multivariate scatter correction (MSC), and normalization to determine the best spectral preprocessing method. Then, the feature variables were extracted from the spectral information using six dimensionality reduction algorithms: competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), genetic algorithm (GA), uninformative variable elimination (UVE), CARS-SPA, and UVE-SPA. Using partial least squares regression (PLSR), optimal prediction models for total acidity and firmness were developed separately. Finally, the total acidity and hardness for each pixel of the hyperspectral image were calculated according to the proposed optimal prediction models, and a gray scale image was obtained and pseudo-color transformed to visualize the distribution of total acidity and firmness of red globe grapes. The results showed that the optimal prediction model for total acidity was MSC-CARS-SPA-PLSR, with correlation coefficient for the prediction set (Rp), root mean square errors of prediction (RMSEP) and residual predictive deviation (RPD) of 0.985 1, 1.348 2 and 5.664 3, respectively. The optimal prediction model for firmness was SG-CARS-PLSR, with Rp, RMSEP and RPD of 0.929 1, 7.935 4 and 2.510 8, respectively. In summary, hyperspectral imaging provides a new method for the detection and visualization of total acidity and firmness of growing red globe grapes. |
| first_indexed | 2024-04-10T05:43:09Z |
| format | Article |
| id | doaj.art-6955f85d74074fc2a7ea7630a52695d9 |
| institution | Directory Open Access Journal |
| issn | 1002-6630 |
| language | English |
| last_indexed | 2024-04-10T05:43:09Z |
| publishDate | 2023-01-01 |
| publisher | China Food Publishing Company |
| record_format | Article |
| series | Shipin Kexue |
| spelling | doaj.art-6955f85d74074fc2a7ea7630a52695d92023-03-06T07:13:03ZengChina Food Publishing CompanyShipin Kexue1002-66302023-01-0144232733610.7506/spkx1002-6630-20220306-078Hyperspectral Imaging for Prediction and Distribution Visualization of Total Acidity and Hardness of Red Globe GrapesGAO Sheng, XU Jianhua0(1. School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China; 2. College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China)In this paper, hyperspectral imaging technology was used for nondestructive detection and distribution visualization of total acidity and firmness of red globe grapes. The hyperspectral information of 360 samples of growing red globe grapes in the wavelength range from 450 to 1 000 nm was collected using a hyperspectral instrument, and the total acidity and firmness of these samples were determined by titration and a texture analyzer, respectively. The Kennard-Stone (KS) algorithm was used to divide the total samples into a training set (270 samples) and a test set (90 samples) in a 3:1 ratio. The collected raw spectral data were preprocessed using various methods such as standard normal variate (SNV), Savitzky-Golay (SG), multivariate scatter correction (MSC), and normalization to determine the best spectral preprocessing method. Then, the feature variables were extracted from the spectral information using six dimensionality reduction algorithms: competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), genetic algorithm (GA), uninformative variable elimination (UVE), CARS-SPA, and UVE-SPA. Using partial least squares regression (PLSR), optimal prediction models for total acidity and firmness were developed separately. Finally, the total acidity and hardness for each pixel of the hyperspectral image were calculated according to the proposed optimal prediction models, and a gray scale image was obtained and pseudo-color transformed to visualize the distribution of total acidity and firmness of red globe grapes. The results showed that the optimal prediction model for total acidity was MSC-CARS-SPA-PLSR, with correlation coefficient for the prediction set (Rp), root mean square errors of prediction (RMSEP) and residual predictive deviation (RPD) of 0.985 1, 1.348 2 and 5.664 3, respectively. The optimal prediction model for firmness was SG-CARS-PLSR, with Rp, RMSEP and RPD of 0.929 1, 7.935 4 and 2.510 8, respectively. In summary, hyperspectral imaging provides a new method for the detection and visualization of total acidity and firmness of growing red globe grapes.https://www.spkx.net.cn/fileup/1002-6630/PDF/2023-44-2-041.pdfred globe grapes; total acidity; firmness; hyperspectral imaging; nondestructive detection; visualization |
| spellingShingle | GAO Sheng, XU Jianhua Hyperspectral Imaging for Prediction and Distribution Visualization of Total Acidity and Hardness of Red Globe Grapes Shipin Kexue red globe grapes; total acidity; firmness; hyperspectral imaging; nondestructive detection; visualization |
| title | Hyperspectral Imaging for Prediction and Distribution Visualization of Total Acidity and Hardness of Red Globe Grapes |
| title_full | Hyperspectral Imaging for Prediction and Distribution Visualization of Total Acidity and Hardness of Red Globe Grapes |
| title_fullStr | Hyperspectral Imaging for Prediction and Distribution Visualization of Total Acidity and Hardness of Red Globe Grapes |
| title_full_unstemmed | Hyperspectral Imaging for Prediction and Distribution Visualization of Total Acidity and Hardness of Red Globe Grapes |
| title_short | Hyperspectral Imaging for Prediction and Distribution Visualization of Total Acidity and Hardness of Red Globe Grapes |
| title_sort | hyperspectral imaging for prediction and distribution visualization of total acidity and hardness of red globe grapes |
| topic | red globe grapes; total acidity; firmness; hyperspectral imaging; nondestructive detection; visualization |
| url | https://www.spkx.net.cn/fileup/1002-6630/PDF/2023-44-2-041.pdf |
| work_keys_str_mv | AT gaoshengxujianhua hyperspectralimagingforpredictionanddistributionvisualizationoftotalacidityandhardnessofredglobegrapes |