Xu Lu, Shi Qiong, Yan Si-Min, Fu Hai-Yan, Xie Shunping, Lu Daowang
College of Material and Chemical Engineering, Tongren University, Tongren 554300, Guizhou, China.
The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, College of Pharmacy, South-Central University for Nationalities, Wuhan 430074, China.
J Anal Methods Chem. 2019 Jul 11;2019:2796502. doi: 10.1155/2019/2796502. eCollection 2019.
The feasibility of combining elemental fingerprints and chemical pattern recognition methods for authentication of the geographical origins of a Chinese herb, BI. (GE), was studied in this paper. A total of 210 GE samples were collected from 7 different producing areas. The levels of 15 mineral elements in GE, including Zn, Cd, Co, Cr, Cu, Ca, Mg, Mn, Mo, Ni, Pb, Sr, Fe, Na, and K, were determined using inductively coupled plasma mass spectrometry (ICP-MS). Using the autoscaled data of elemental fingerprints and partial least-squares discriminant analysis (PLSDA), two chemometrics strategies for multiclass classifications, One-Versus-Rest (OVR) and One-Versus-One (OVO), were studied and compared in discrimination of GE geographical origins. As a result, OVR-PLSDA and OVO-PLSDA could achieve the classification accuracy of 0.672 and 0.925, respectively. The results indicate that mineral elemental fingerprints coupled with chemometrics can provide a useful alternative method for simultaneous discrimination of multiple GE geographical origins.
本文研究了结合元素指纹图谱和化学模式识别方法对中药薤白(GE)产地进行鉴别的可行性。共采集了来自7个不同产地的210份GE样本。采用电感耦合等离子体质谱法(ICP-MS)测定了GE中15种矿物元素的含量,包括锌(Zn)、镉(Cd)、钴(Co)、铬(Cr)、铜(Cu)、钙(Ca)、镁(Mg)、锰(Mn)、钼(Mo)、镍(Ni)、铅(Pb)、锶(Sr)、铁(Fe)、钠(Na)和钾(K)。利用元素指纹图谱的自动缩放数据和偏最小二乘判别分析(PLSDA),研究并比较了两种用于多类分类的化学计量学策略,即一对其余(OVR)和一对一(OVO),以鉴别GE的产地。结果表明,OVR-PLSDA和OVO-PLSDA的分类准确率分别为0.672和0.925。结果表明,矿物元素指纹图谱结合化学计量学可为同时鉴别多个GE产地提供一种有用的替代方法。