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Wang L, Li X, Wang Y, Ren X, Liu X, Dong Y, Ma J, Song R, Wei J, Yu AX, Fan Q, Shan D, Yao J, She G. Rapid discrimination and screening of volatile markers for varietal recognition of Curcumae Radix using ATR-FTIR and HS-GC-MS combined with chemometrics. JOURNAL OF ETHNOPHARMACOLOGY 2021; 280:114422. [PMID: 34274441 DOI: 10.1016/j.jep.2021.114422] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/10/2021] [Accepted: 07/13/2021] [Indexed: 06/13/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Curcumae Radix (Yujin) has a long medicinal use history in China, which is used to cure diseases like jaundice, cholelithiasis caused by dampness-heat of gallbladder and liver, and so on. It comes from the dried tuberous roots of C. kwangsiensis (Guiyujin), C. longa (Huangyujin), C. phaeocaulis (Lvyujin) and C. wenyujin (Wenyujin). Though there are differences in chemical compositions and pharmacological activities among the four species of Yujin, they have not been differentiated well in clinical application due to their similar morphological characterizations. AIM OF THE STUDY In this study, the four species of Yujin were rapidly and accurately discriminated. The potential volatile markers for varietal recognition were identified. MATERIALS AND METHODS Attenuated total reflection fourier transformed infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to rapidly discriminate the four species of Yujin. Headspace-gas chromatography-mass spectrometry (HS-GC-MS) technology coupled with chemometrics was employed to characterize volatile profiling, differentiate species and select potential markers for varietal recognition of Yujin. RESULTS By applying PCA (principal components analysis) and HCA (hierarchical cluster analysis), HS-GC-MS realized complete differentiation of the four species of Yujin, while ATR-FTIR only recognized Guiyuijin. Back propagation neural network (BP-NN), KNN (K-nearest neighbor) and LDA (linear discriminant analysis) models based on spectral data achieved 100% discriminant accuracies. Support vector machines (SVM), KNN and PLS-DA (partial least square discriminant analysis) models based on volatile compounds also realized 100% discriminant accuracies. Additionally, the potential volatile markers for varietal recognition of Yujin were screened using PLS-DA, including 2 for Guiyujin, 6 for Lvyujin, 9 for Wenyujin and 13 for Huangyujin. CONCLUSIONS The present study developed reliable methods for the varietal discrimination and volatile compounds characterization of Yujin, which will provide references for its quality control and clinical efficacy.
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Affiliation(s)
- Le Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China; School of Pharmacy, Minzu University of China, 27 Zhongguancun South Avenue, Beijing, China.
| | - Xiang Li
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Yu Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Xueyang Ren
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Xiaoyun Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Ying Dong
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Jiamu Ma
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Ruolan Song
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Jing Wei
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - AXiang Yu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Qiqi Fan
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Dongjie Shan
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Jianling Yao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
| | - Gaimei She
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China; Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, China.
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Abstract
The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.
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Affiliation(s)
- Shigeaki Morita
- Department of Engineering Science, Osaka Electro-Communication University
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LIU S, ZHANG X, GUO R, WEI Y, NODA I, OZAKI Y, XU Y, WU J. Design of a Novel Apparatus to Enrich Analytes via a Diffuse-Evaporation Process for HPLC-FTIR Analysis. ANAL SCI 2018; 34:1351-1356. [DOI: 10.2116/analsci.18p227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Shengnan LIU
- College of Chemistry and Material Science, Hebei Normal University
- College of Chemistry and Molecular Engineering, Peking University
| | - Xiaohua ZHANG
- College of Chemistry and Molecular Engineering, Peking University
| | - Ran GUO
- College of Chemistry and Molecular Engineering, Peking University
| | - Yongju WEI
- College of Chemistry and Material Science, Hebei Normal University
| | - Isao NODA
- College of Chemistry and Molecular Engineering, Peking University
- Department of Materials Science and Engineering, University of Delaware
| | - Yukihiro OZAKI
- College of Chemistry and Molecular Engineering, Peking University
- Department of Chemistry, School of Science and Technology, Kwansei Gakuin University
| | - Yizhuang XU
- College of Chemistry and Molecular Engineering, Peking University
| | - Jinguang WU
- College of Chemistry and Molecular Engineering, Peking University
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