Color measurement of tea leaves at different drying periods using hyperspectral imaging technique.
This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral regi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4278674?pdf=render |
| _version_ | 1818701040697999360 |
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| author | Chuanqi Xie Xiaoli Li Yongni Shao Yong He |
| author_facet | Chuanqi Xie Xiaoli Li Yongni Shao Yong He |
| author_sort | Chuanqi Xie |
| collection | DOAJ |
| description | This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods. |
| first_indexed | 2024-12-17T15:14:31Z |
| format | Article |
| id | doaj.art-06f370a874bc4085adfbcbfb8e3a23cd |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T15:14:31Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-06f370a874bc4085adfbcbfb8e3a23cd2022-12-21T21:43:33ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01912e11342210.1371/journal.pone.0113422Color measurement of tea leaves at different drying periods using hyperspectral imaging technique.Chuanqi XieXiaoli LiYongni ShaoYong HeThis study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.http://europepmc.org/articles/PMC4278674?pdf=render |
| spellingShingle | Chuanqi Xie Xiaoli Li Yongni Shao Yong He Color measurement of tea leaves at different drying periods using hyperspectral imaging technique. PLoS ONE |
| title | Color measurement of tea leaves at different drying periods using hyperspectral imaging technique. |
| title_full | Color measurement of tea leaves at different drying periods using hyperspectral imaging technique. |
| title_fullStr | Color measurement of tea leaves at different drying periods using hyperspectral imaging technique. |
| title_full_unstemmed | Color measurement of tea leaves at different drying periods using hyperspectral imaging technique. |
| title_short | Color measurement of tea leaves at different drying periods using hyperspectral imaging technique. |
| title_sort | color measurement of tea leaves at different drying periods using hyperspectral imaging technique |
| url | http://europepmc.org/articles/PMC4278674?pdf=render |
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