A strategy to identify and quantify closely related adulterant herbal materials by mass spectrometry-based partial least squares regression

Li Wang, Li Fang Liu, Jian Ying Wang, Zi Qi Shi, Wen Qi Chang, Meng Lu Chen, Ying Hao Yin, Yan Jiang, Hui Jun Li, Ping Li, Zhongping Yao, Gui Zhong Xin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

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 (rp2) 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.
Original languageEnglish
Pages (from-to)28-35
Number of pages8
JournalAnalytica Chimica Acta
Volume977
DOIs
Publication statusPublished - 18 Jul 2017

Keywords

  • Fritillariae cirrhosae bulbus
  • Herbal adulteration
  • Mass spectrometric techniques
  • Partial least squares regression

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Environmental Chemistry
  • Spectroscopy

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