Identification of Ligustici Rhizoma et Radix and its adulterants based on their chemical constituents by UHPLC-Q/TOF-MS combined with data mining.

Abstract:

:Ligustici Rhizoma et Radix (LR), known as Gaoben (GB) in Chinese, has been used in traditional Chinese medicine for more than 2000 years. However, the varieties of LR are not well characterized at present, and traditional recognition methods have encountered increasing difficulties. This research attempts to distinguish different varieties of LR and its adulterants based on their chemical composition. A total of 61 representative samples were collected, and their varieties were confirmed by combining expert opinion with DNA molecular technology. All of the samples were characterized by an UHPLC-Q/TOF-MS method. The marker components that may contribute to variety discrimination were discerned by a data mining method, and they were then hypothesized, analyzed, and identified. A cluster analysis was performed by partial least squares (PLS) based on their MS signals. Furthermore, a feature extraction to find out the characteristic components, and a correspondence analysis to illustrate the corresponding relationship between the varieties and their components, were developed. As a result, 71 components were identified, of which 27 components were unambiguously identified by comparison with standards. The cluster analysis shows that varieties of LR and its adulterant samples exhibited a certain classification trend, with butylphthalide, senkyunolide I, senkyunolide A, ferulic acid, (Z)-ligustilide, bergapten, levistilide A, vanillic acid, isochlorogenic acid C and isochlorogenic acid A as characteristic chemical components. The varieties and their components showed a modest correlation. In conclusion, our study verified the possibility of discriminating the varieties of LR according to their chemical compositions. This research provides a new reference for the recognition of LR and its adulterants.

journal_name

J Pharm Biomed Anal

authors

Lei XQ,Li G,Cheng L,Wang XL,Meng FY

doi

10.1016/j.jpba.2018.02.053

subject

Has Abstract

pub_date

2018-05-30 00:00:00

pages

123-137

eissn

0731-7085

issn

1873-264X

pii

S0731-7085(17)31791-0

journal_volume

154

pub_type

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