Comparison of FTIR spectrum with chemometric and machine learning classifying analysis for differentiating guan-mutong a nephrotoxic and carcinogenic traditional chinese medicine with chuan-mutong

• Published in: Microchemical journal Vol. 163; p. 105835
• Main Authors: Tan, Chu Shan, Leow, Shin Yee, Ying, Chen, Tan, Choo Jun, Yoon, Tiem Leong, Jingying, Chen, Yam, Mun Fei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2021
• Subjects: Herbal adulteration; Machine learning; Mutong; PCA analysis; PLS-DA; Tri-step Fourier Transform Instruments (FT-IR); Mutong; PLS-DA; PCA analysis; Herbal adulteration; Tri-step Fourier Transform Instruments (FT-IR); Machine learning
• ISSN: 0026-265X; 1095-9149
• DOI: 10.1016/j.microc.2020.105835

•Tri-step FT-IR fingerprints of Chuan-Mutong and Guan-Mutong are presented.•Machine learning classifiers were developed to distinguish between Chuan-Mutong and Guan-Mutong.•Comparison of machine learning classifier with PCA and PLS-DA was performed.•The proposed FT-IR with PLS-DA and machine learning classifier method was characterized by being simple, fast and reliable. 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 FT-IR, 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.