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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Main Authors: Manfei Xu, Chenzhao Du, Na Zhang, Xinyuan Shi, Zhisheng Wu, Yanjiang Qiao
Format: Article
Language:English
Published: Elsevier 2016-07-01
Series:Journal of Traditional Chinese Medical Sciences
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2095754816300308
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spelling 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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