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结合元素谱、稳定同位素比率和化学计量学对来自37个产地的中国草药甘草(Fisch.)进行精细分类的化学方法

Chemistry Combining Elemental Profile, Stable Isotopic Ratios, and Chemometrics for Fine Classification of a Chinese Herb Licorice ( Fisch.) from 37 Producing Area.

作者信息

Lu Zhongying, Hai Chengying, Yan Simin, Xu Lu, Lu Daowang, Sou Yixin, Chen Hengye, Yang Xiaolong, Fu Haiyan, Yang Jian

机构信息

Department of Food Engineering, Guizhou Vocational College of Foodstuff Engineering, Guiyang 551400, China.

The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, College of Pharmacy, South-Central Minzu University, Wuhan 430074, China.

出版信息

J Anal Methods Chem. 2022 Aug 18;2022:8906305. doi: 10.1155/2022/8906305. eCollection 2022.

DOI:10.1155/2022/8906305
PMID:36032189
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9410990/
Abstract

A method based on elemental fingerprint, stable isotopic analysis and combined with chemometrics was proposed to trace the geographical origins of Licorice ( Fisch) from 37 producing areas. For elemental fingerprint, the levels of 15 elements, including Ca, Cu, Mg, Pb, Zn, Sr, Mn, Se, Cd, Fe, Na, Al, Cr, Co, and K, were analyzed by inductively coupled plasma atomic emission spectrometry (ICP-AES). Three stable isotopes, including C, N, and O, were measured using an isotope-ratio mass spectrometer (IRMS). For fine classification, three multiclass strategies, including the traditional one-versus-rest (OVR) and one-versus-one (OVO) strategies and a new ensemble strategy (ES), were combined with two binary classifiers, partial least squares discriminant analysis (PLSDA) and least squares support vector machines (LS-SVM). As a result, ES-PLSDA and ES-LS-SVM achieved 0.929 and 0.921 classification accuracy of GUF samples from the 37 origins. The results show that element fingerprint and stable isotope combined with chemometrics is an effective method for GUF traceability and provides a new idea for the geographical traceability of Chinese herbal medicine.

摘要

提出了一种基于元素指纹、稳定同位素分析并结合化学计量学的方法,用于追溯来自37个产地的甘草(Fisch)的地理来源。对于元素指纹,采用电感耦合等离子体原子发射光谱法(ICP-AES)分析了包括钙(Ca)、铜(Cu)、镁(Mg)、铅(Pb)、锌(Zn)、锶(Sr)、锰(Mn)、硒(Se)、镉(Cd)、铁(Fe)、钠(Na)、铝(Al)、铬(Cr)、钴(Co)和钾(K)在内的15种元素的含量。使用同位素比率质谱仪(IRMS)测量了碳(C)、氮(N)和氧(O)三种稳定同位素。为了进行精细分类,将三种多分类策略,包括传统的一对其余(OVR)和一对一(OVO)策略以及一种新的集成策略(ES),与两种二分类器,偏最小二乘判别分析(PLSDA)和最小二乘支持向量机(LS-SVM)相结合。结果表明,ES-PLSDA和ES-LS-SVM对来自37个产地的GUF样本的分类准确率分别达到了0.929和0.921。结果表明,元素指纹和稳定同位素结合化学计量学是一种有效的GUF溯源方法,为中药材的地理溯源提供了新思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cafb/9410990/06f0825b70c8/JAMC2022-8906305.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cafb/9410990/88cc3116dd7b/JAMC2022-8906305.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cafb/9410990/51901a2bc363/JAMC2022-8906305.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cafb/9410990/c9daa5331553/JAMC2022-8906305.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cafb/9410990/06f0825b70c8/JAMC2022-8906305.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cafb/9410990/88cc3116dd7b/JAMC2022-8906305.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cafb/9410990/51901a2bc363/JAMC2022-8906305.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cafb/9410990/c9daa5331553/JAMC2022-8906305.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cafb/9410990/06f0825b70c8/JAMC2022-8906305.004.jpg

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