Assessing the quality of Smilacis Glabrae Rhizoma (Tufuling) by colormetrics and UPLC-Q-TOF-MS. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: He, Xicheng; Yi, Tao; Tang, Yina; Xu, Jun; Zhang, Jianye; Zhang, Yazhou; Dong, Lisha; Chen, Hubiao
• Is Part Of: Chinese medicine. Volume 11:Issue 1(2016)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-12
• Subjects: Smilacis Glabrae Rhizoma -- Colormetrics -- Fingerprint analysis; Medicine, Chinese -- Periodicals; Evidence-based medicine -- China -- Periodicals; Medicine, Experimental -- Periodicals; 610.95105
• DOI: 10.1186/s13020-016-0104-y
• Languages: English
• ISSNs: 1749-8546

Abstract

Abstract Background The quality of the materials used in Chinese medicine (CM) is generally assessed based on an analysis of their chemical components (e.g., chromatographic fingerprint analysis). However, there is a growing interest in the use of color metrics as an indicator of quality in CM. The aim of this study was to investigate the accuracy and feasibility of using color metrics and chemical fingerprint analysis to determine the quality ofSmilacis Glabrae Rhizoma (Tufuling ) (SGR). The SGR samples were divided into two categories based on their cross-sectional coloration, including red SGR (R-SGR) and white SGR (W-SGR). Methods Forty-three samples of SGR were collected and their colors were quantized based on an RGB color model using the Photoshop software. An ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/QTOF MS) system was used for chromatographic fingerprint analysis to evaluate the quality of the different SGR samples. Hierarchical cluster analysis and dimensional reduction were used to evaluate the data generated from the different samples. Pearson correlation coefficient was used to evaluate the relationship between the color metrics and the chemical compositions of R-SGR and W-SGR. Results The SGR samples were divided into two different groups based on their cross-sectional color, including color A (CLA) and B (CLB), as well as being into two separate classes based on their chemical composition, including chemical A (CHA) and B (CHB). Standard fingerprint chromatograms were for CHA and CHB. Statistical analysis revealed a significant correlation (Pearson's r = −0.769, P < 0.001 ) between the color metrics and the results of the chemical fingerprint analysis. Conclusions The SGR samples were divided into two major clusters, and the variations in the colors of these samples reflected differences in the quality of the SGR material. Furthermore, we observed a statistically significant correlation between the color metrics and the quality of the SGR material.