Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76161
Title: A strategy to identify and quantify closely related adulterant herbal materials by mass spectrometry-based partial least squares regression
Authors: Wang, L
Liu, LF
Wang, JY
Shi, ZQ
Chang, WQ
Chen, ML
Yin, YH
Jiang, Y
Li, HJ
Li, P
Yao, ZP 
Xin, GZ
Keywords: Herbal adulteration
Fritillariae cirrhosae bulbus
Partial least squares regression
Mass spectrometric techniques
Issue Date: 2017
Publisher: Elsevier
Source: Analytica chimica acta, 2017, v. 977, p. 28-35 How to cite?
Journal: Analytica chimica acta 
Abstract: In this study, a new strategy combining mass spectrometric (MS) techniques with partial least squares regression (PLSR) was proposed to identify and quantify closely related adulterant herbal materials. This strategy involved preparation of adulterated samples, data acquisition and establishment of PLSR model. The approach was accurate, sensitive, durable and universal, and validation of the model was done by detecting the presence of Fritillaria Ussuriensis Bulbus in the adulteration of the bulbs of Fritillaria unibracteata. Herein, three different MS techniques, namely wooden-tip electrospray ionization mass spectrometry (wooden-tip ESI/MS), ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and UPLC-triple quadrupole tandem mass spectrometry (UPLC-TQ/MS), were applied to obtain MS profiles for establishing PLSR models. All three models afforded good linearity and good accuracy of prediction, with correlation coefficient of prediction (r(p)(2)) of 0.9072, 0.9922 and 0.9904, respectively, and root mean square error of prediction (RMSEP) of 0.1004, 0.0290 and 0.0323, respectively. Thus, this strategy is very promising in tracking the supply chain of herb-based pharmaceutical industry, especially for identifying adulteration of medicinal materials from their closely related herbal species.
URI: http://hdl.handle.net/10397/76161
ISSN: 0003-2670
EISSN: 1873-4324
DOI: 10.1016/j.aca.2017.04.023
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