Color spaces of safflower (Carthamus tinctorius L.) for quality assessment
Objective: In this study, safflower (Carthamus tinctorius L.) was taken as a representative example to examine the application of color characteristics to evaluate quality. Methods: A computer vision system was established for the objective and nondestructive assessment of color using image processi...
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doaj-fb3fec200a2244238690fe57767120382021-04-02T03:15:29ZengElsevierJournal of Traditional Chinese Medical Sciences2095-75482016-07-013316817510.1016/j.jtcms.2016.11.004Color spaces of safflower (Carthamus tinctorius L.) for quality assessmentManfei Xu0Chenzhao Du1Na Zhang2Xinyuan Shi3Zhisheng Wu4Yanjiang Qiao5Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, ChinaBeijing University of Chinese Medicine, Beijing 100102, ChinaBeijing University of Chinese Medicine, Beijing 100102, ChinaBeijing University of Chinese Medicine, Beijing 100102, ChinaBeijing University of Chinese Medicine, Beijing 100102, ChinaBeijing University of Chinese Medicine, Beijing 100102, ChinaObjective: In this study, safflower (Carthamus tinctorius L.) was taken as a representative example to examine the application of color characteristics to evaluate quality. Methods: A computer vision system was established for the objective and nondestructive assessment of color using image processing algorithms. Color parameters were investigated based on the RGB, L*a*b and HSV color spaces. The content of hydroxysafflor yellow A (HSYA), a major bioactive constituent of safflower, was determined by high-performance liquid chromatography. The relationship between HSYA content and color values was investigated by Pearson correlation analysis. A multiple linear regression model was established to predict the HSYA content from color values. Results: The red color and lightness of safflower were found to be significantly related to HSYA content. The prediction equation obtained by multiple regression was reliable with an R2 value of 0.805 (P < .01). Conclusion: The results suggest that the computer vision technique could be used as a promising and non-destructive technology for color measurement and quality evaluation of CHM.http://www.sciencedirect.com/science/article/pii/S2095754816300308Computer visionCarthamus tinctorius L.ColorQuality evaluation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Manfei Xu Chenzhao Du Na Zhang Xinyuan Shi Zhisheng Wu Yanjiang Qiao |
spellingShingle |
Manfei Xu Chenzhao Du Na Zhang Xinyuan Shi Zhisheng Wu Yanjiang Qiao Color spaces of safflower (Carthamus tinctorius L.) for quality assessment Journal of Traditional Chinese Medical Sciences Computer vision Carthamus tinctorius L. Color Quality evaluation |
author_facet |
Manfei Xu Chenzhao Du Na Zhang Xinyuan Shi Zhisheng Wu Yanjiang Qiao |
author_sort |
Manfei Xu |
title |
Color spaces of safflower (Carthamus tinctorius L.) for quality assessment |
title_short |
Color spaces of safflower (Carthamus tinctorius L.) for quality assessment |
title_full |
Color spaces of safflower (Carthamus tinctorius L.) for quality assessment |
title_fullStr |
Color spaces of safflower (Carthamus tinctorius L.) for quality assessment |
title_full_unstemmed |
Color spaces of safflower (Carthamus tinctorius L.) for quality assessment |
title_sort |
color spaces of safflower (carthamus tinctorius l.) for quality assessment |
publisher |
Elsevier |
series |
Journal of Traditional Chinese Medical Sciences |
issn |
2095-7548 |
publishDate |
2016-07-01 |
description |
Objective: In this study, safflower (Carthamus tinctorius L.) was taken as a representative example to examine the application of color characteristics to evaluate quality.
Methods: A computer vision system was established for the objective and nondestructive assessment of color using image processing algorithms. Color parameters were investigated based on the RGB, L*a*b and HSV color spaces. The content of hydroxysafflor yellow A (HSYA), a major bioactive constituent of safflower, was determined by high-performance liquid chromatography. The relationship between HSYA content and color values was investigated by Pearson correlation analysis. A multiple linear regression model was established to predict the HSYA content from color values.
Results: The red color and lightness of safflower were found to be significantly related to HSYA content. The prediction equation obtained by multiple regression was reliable with an R2 value of 0.805 (P < .01).
Conclusion: The results suggest that the computer vision technique could be used as a promising and non-destructive technology for color measurement and quality evaluation of CHM. |
topic |
Computer vision Carthamus tinctorius L. Color Quality evaluation |
url |
http://www.sciencedirect.com/science/article/pii/S2095754816300308 |
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