文献类型： 外文期刊

作者： Tan, Chu Shan ¹ ; Leow, Shin Yee ¹ ; Ying, Chen ² ; Tan, Choo Jun ³ ; Yoon, Tiem Leong ⁴ ; Chen Jingying ⁵ ; Yam, Mun ¹ ;

作者机构： 1.Univ Sains Malaysia, Sch Pharmaceut Sci, George Town 11800, Malaysia

2.Beijing Univ Chinese Med, Sch Tradit Chinese Med, Beijing 102488, Peoples R China

3.Wawasan Open Univ, Sch Sci & Technol, 54 Jalan Sultan Ahmad Shah, George Town 10050, Malaysia

4.Univ Sains Malaysia, Sch Phys, George Town 11800, Malaysia

5.Fujian Acad Agr Sci, Inst Agr Bioresource, Res Ctr Med Plant, Fuzhou 350003, Fujian, Peoples R China

6.Fujian Univ Tradit Chinese Med, Coll Pharm, 1 Qiuyang Rd, Fuzhou 350122, Fujian, Peoples R China

关键词： Mutong; Tri-step Fourier Transform Instruments (FT-IR); PLS-DA; Machine learning; PCA analysis; Herbal adulteration

期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：3.594； 五年影响因子：3.273 ）

ISSN： 0026-265X

年卷期： 2021 年 163 卷

页码：

收录情况： SCI

摘要： Chuan-Mutong (Clemetis spp.) is a precious medicinal herb in traditional Chinese medicine that possesses various therapeutic effects especially well known for its diuretic effect and widely used in Malaysia. However, there were several reported Chinese herb nephropathy cases due to the adulteration of Aristolochia spp. found in combinational herbal regimen. Guan-Mutong (Aristolochia manshuriensis), which looks similar in appearance and has similar therapeutic effects as Chuan-Mutong, has the possibility to substitute the Chuan-Mutong. Therefore, there is a necessity to differentiate the types of Mutong using analytical authentication methods. In this paper, a rapid and accurate method is proposed to discriminate Chuan-Mutong from Guan-Mutong by using tri-step fourier transform infrared spectroscopy (FT-IR) identification approaches. The method involves the deployment of FTIR, second derivative infrared spectra (SD-IR), and two-dimensional correlation infrared spectra (2D-IR). In our approach, FT-IR spectra of Chuan-Mutong and Guan-Mutong were subjected to discrimination using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and machine learning classifiers (ML). Chuan-Mutong and Guan-Mutong can be clearly classified or discriminated against each other by ML, PLS-DA and PCA. The sensitivity, accuracy and specificity of ML were >90%, while the sensitivity, accuracy and specificity of PLS-DA were 100%. It is hence demonstrated that the infrared spectroscopic identification approach using PCA, PLS-DA and ML can be effectively used to differentiate Chuan-Mutong and Guan-Mutong. PLS-DA and ML provide a simple, fast, and high accuracy prediction to differentiate Chuan-Mutong and Guan-Mutong.