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Rui G, Qin ZY, Chang YQ, Zheng YG, Zhang D, Yao LM, Guo L. Chemical Comparison and Identification of Xanthine Oxidase Inhibitors of Dioscoreae Hypoglaucae Rhizoma and Dioscoreae Spongiosae Rhizoma by Chemometric Analysis and Spectrum-Effect Relationship. Molecules 2023; 28:8116. [PMID: 38138603 PMCID: PMC10745721 DOI: 10.3390/molecules28248116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023] Open
Abstract
Dioscoreae hypoglaucae Rhizoma (DH) and Dioscoreae spongiosae Rhizoma (DS) are two similar Chinese herbal medicines derived from the Dioscorea family. DH and DS have been used as medicines in China and other Asian countries for a long time, but study on their phytochemicals and bioactive composition is limited. This present study aimed to compare the chemical compositions of DH and DS, and explore the anti-xanthine oxidase components based on chemometric analysis and spectrum-effect relationship. Firstly, an HPLC method was used to establish the chemical fingerprints of DH and DS samples, and nine common peaks were selected. Then, hierarchical clustering analysis, principal component analysis and orthogonal partial least squares discriminant analysis were employed to compare and discriminate DH and DS samples based on the fingerprints data, and four steroidal saponins compounds (protodioscin, protogracillin, dioscin, gracillin) could be chemical markers responsible for the differences between DH and DS. Meanwhile, the anti-xanthine oxidase activities of these two herbal medicines were evaluated by xanthine oxidase inhibitory assay in vitro. Pearson correlation analysis and partial least squares regression analysis were subsequently used to investigate the spectrum-effect relationship between chemical fingerprints and xanthine oxidase inhibitory activities. The results showed that four steroidal saponins, including protodioscin, protogracillin, methyl protodioscin and pseudoprogracillin could be potential anti-xanthine oxidase compounds in DH and DS. Furthermore, the xanthine oxidase inhibitory activities of the four selected inhibitors were validated by anti-xanthine oxidase inhibitory assessment and molecular docking experiments. The present work provided evidence for understanding of the chemical differences and the discovery of the anti-xanthine oxidase constituent of DH and DS, which could be useful for quality evaluation and bioactive components screening of these two herbal medicines.
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Affiliation(s)
- Guo Rui
- Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China; (G.R.); (Z.-Y.Q.); (Y.-Q.C.); (Y.-G.Z.); (D.Z.)
- International Joint Research Center on Resource Utilization and Quality Evaluation of Traditional Chinese Medicine of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
| | - Zhang-Yi Qin
- Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China; (G.R.); (Z.-Y.Q.); (Y.-Q.C.); (Y.-G.Z.); (D.Z.)
- International Joint Research Center on Resource Utilization and Quality Evaluation of Traditional Chinese Medicine of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
| | - Ya-Qing Chang
- Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China; (G.R.); (Z.-Y.Q.); (Y.-Q.C.); (Y.-G.Z.); (D.Z.)
- International Joint Research Center on Resource Utilization and Quality Evaluation of Traditional Chinese Medicine of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
| | - Yu-Guang Zheng
- Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China; (G.R.); (Z.-Y.Q.); (Y.-Q.C.); (Y.-G.Z.); (D.Z.)
- Department of Pharmaceutical Engineering, Hebei Chemical & Pharmaceutical College, Shijiazhuang 050026, China
| | - Dan Zhang
- Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China; (G.R.); (Z.-Y.Q.); (Y.-Q.C.); (Y.-G.Z.); (D.Z.)
- International Joint Research Center on Resource Utilization and Quality Evaluation of Traditional Chinese Medicine of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
| | - Li-Min Yao
- Bethune International Peace Hospital, Shijiazhuang 050082, China
| | - Long Guo
- Traditional Chinese Medicine Processing Technology Innovation Center of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China; (G.R.); (Z.-Y.Q.); (Y.-Q.C.); (Y.-G.Z.); (D.Z.)
- International Joint Research Center on Resource Utilization and Quality Evaluation of Traditional Chinese Medicine of Hebei Province, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
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Gao L, Zhong L, Wei Y, Li L, Wu A, Nie L, Yue J, Wang D, Zhang H, Dong Q, Zang H. A new perspective in understanding the processing mechanisms of traditional Chinese medicine by near-infrared spectroscopy with Aquaphotomics. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2023.135401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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Chen R, Liu F, Zhang C, Wang W, Yang R, Zhao Y, Peng J, Kong W, Huang J. Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy. FRONTIERS IN PLANT SCIENCE 2023; 14:1128300. [PMID: 37025139 PMCID: PMC10072231 DOI: 10.3389/fpls.2023.1128300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.
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Affiliation(s)
- Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Wei Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yiying Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Jiyu Peng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, China
| | - Jing Huang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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Liu C, Zuo Z, Xu F, Wang Y. Study of the suitable climate factors and geographical origins traceability of Panax notoginseng based on correlation analysis and spectral images combined with machine learning. FRONTIERS IN PLANT SCIENCE 2023; 13:1009727. [PMID: 36825249 PMCID: PMC9941628 DOI: 10.3389/fpls.2022.1009727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/28/2022] [Indexed: 06/18/2023]
Abstract
INTRODUCTION The cultivation and sale of medicinal plants are some of the main ways to meet the increased market demand for plant-based drugs. Panax notoginseng is a widely used Chinese medicinal material. The growth and accumulation of bioactive constituents mainly depend on a satisfactory growing environment. Additionally, the occurrence of market fraud means that care should be taken when purchasing. METHODS In this study, we report the correlation between saponins and climate factors based on high performance liquid chromatography (HPLC), and evaluate the influence of climate factors on the quality of P. notoginseng. In addition, the synchronous two-dimensional correlation spectroscopy (2D-COS) images of near infrared (NIR) data combined with the deep learning model were applied to traceability of geographic origins of P. notoginseng at two different levels (district and town levels). RESULTS The results indicated that the contents of saponins in P. notoginseng are negatively related to the annual mean temperature and the temperature annual range. A lower annual mean temperature and temperature annual range are favorable for the content accumulation of saponins. Additionally, high annual precipitation and high humidity are conducive to the content accumulation of Notoginsenoside R1 (NG-R1), Ginsenosides Rg1 (G-Rg1), and Ginsenosides Rb1 (G-Rb1), while Ginsenosides Rd (G-Rd), this is not the case. Regarding geographic origins, classifications at two different levels could be successfully distinguished through synchronous 2D-COS images combined with the residual convolutional neural network (ResNet) model. The model accuracy of the training set, test set, and external validation is achieved at 100%, and the cross-entropy loss function curves are lower. This demonstrated the potential feasibility of the proposed method for P. notoginseng geographic origin traceability, even if the distance between sampling points is small. DISCUSSION The findings of this study could improve the quality of P. notoginseng, provide a reference for cultivating P. notoginseng in the future and alleviate the occurrence of market fraud.
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Affiliation(s)
- Chunlu Liu
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, Yunnan, China
- Collge of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
| | - Zhitian Zuo
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, Yunnan, China
| | - Furong Xu
- Collge of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, Yunnan, China
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A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials. Molecules 2022; 27:molecules27227766. [PMID: 36431869 PMCID: PMC9696260 DOI: 10.3390/molecules27227766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Abstract
Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.
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Cui ZY, Liu CL, Li DD, Wang YZ, Xu FR. Anticoagulant activity analysis and origin identification of Panax notoginseng using HPLC and ATR-FTIR spectroscopy. PHYTOCHEMICAL ANALYSIS : PCA 2022; 33:971-981. [PMID: 35715878 DOI: 10.1002/pca.3152] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/29/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
INTRODUCTION Panax notoginseng is one of the traditional precious and bulk-traded medicinal materials in China. Its anticoagulant activity is related to its saponin composition. However, the correlation between saponins and anticoagulant activities in P. notoginseng from different origins and identification of the origins have been rarely reported. OBJECTIVES We aimed to analyze the correlation of components and activities of P. notoginseng from different origins and develop a rapid P. notoginseng origin identification method. MATERIALS AND METHODS Pharmacological experiments, HPLC, and ATR-FTIR spectroscopy (variable selection) combined with chemometrics methods of P. notoginseng main roots from four different origins (359 individuals) in Yunnan Province were conducted. RESULTS The pharmacological experiments and HPLC showed that the saponin content of P. notoginseng main roots was not significantly different. It was the highest in main roots from Wenshan Prefecture (9.86%). The coagulation time was prolonged to observe the strongest effect (4.99 s), and the anticoagulant activity was positively correlated with the contents of the three saponins. The content of ginsenoside Rg1 had the greatest influence on the anticoagulant effect. The results of spectroscopy combined with chemometrics show that the variable selection method could extract a small number of variables containing valid information and improve the performance of the model. The variable importance in projection has the best ability to identify the origins of P. notoginseng; the accuracy of the training set and the test set was 0.975 and 0.984, respectively. CONCLUSION This method is a powerful analytical tool for the activity analysis and identification of Chinese medicinal materials from different origins.
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Affiliation(s)
- Zhi-Ying Cui
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
| | - Chun-Lu Liu
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Yunnan, Kunming, China
| | - Dan-Dan Li
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
| | - Yuan-Zhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Yunnan, Kunming, China
| | - Fu-Rong Xu
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, China
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Ji C, Zhang Q, Shi R, Li J, Wang X, Wu Z, Ma Y, Guo J, He X, Zheng W. Determination of the Authenticity and Origin of Panax Notoginseng: A Review. J AOAC Int 2022; 105:1708-1718. [DOI: 10.1093/jaoacint/qsac081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 06/13/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022]
Abstract
Abstract
Panax notoginseng, a traditional medicinal and edible plant, is widely used in medicine, health care, cosmetics, and other industries. Affected by the discrepancy between market supply and demand and price, the adulteration of P. notoginseng products with other plant-derived ingredients occurs at times. With the continuous development of technologies such as spectroscopy, chromatography, and DNA barcoding, the detection techniques for rapid and sensitive determination of the authenticity identification and origin of P. notoginseng have become more diversified to meet the needs of different regulatory goals and could effectively control practices that mislead consumers and promote false labeling. This review analyzes and summarizes the existing technologies for determining the authenticity and origin of P. notoginseng from these three aspects: morphological, chemical, and molecular biology methods from the literature since 2001; on this basis, the current problems and future research directions are discussed to provide a reference for the establishment of rapid and accurate methods to verify authenticity and origin to promote the further development and improvement of quality control technology systems for P. notoginseng.
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Affiliation(s)
- Chao Ji
- State Key Laboratory for Conservation and Utilization of Yunnan Biological Resources, Yunnan Agricultural University , Kunming 650201, China
| | - Qin Zhang
- Laboratory for Quality Control and Traceability of Food, Tianjin Normal University , Tianjin 300387, China
| | - Rui Shi
- Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Landscape Architecture Engineering Research Center of National Forestry and Grassland Administration, Southwest Forestry University , Kunming 650224, China
| | - Juan Li
- Laboratory for Quality Control and Traceability of Food, Tianjin Normal University , Tianjin 300387, China
| | - Xingyu Wang
- Laboratory for Quality Control and Traceability of Food, Tianjin Normal University , Tianjin 300387, China
| | - Zhiqiang Wu
- Laboratory for Quality Control and Traceability of Food, Tianjin Normal University , Tianjin 300387, China
| | - Ying Ma
- Laboratory for Quality Control and Traceability of Food, Tianjin Normal University , Tianjin 300387, China
| | - Junli Guo
- Laboratory for Quality Control and Traceability of Food, Tianjin Normal University , Tianjin 300387, China
| | - Xiahong He
- State Key Laboratory for Conservation and Utilization of Yunnan Biological Resources, Yunnan Agricultural University , Kunming 650201, China
- Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Landscape Architecture Engineering Research Center of National Forestry and Grassland Administration, Southwest Forestry University , Kunming 650224, China
| | - Wenjie Zheng
- State Key Laboratory for Conservation and Utilization of Yunnan Biological Resources, Yunnan Agricultural University , Kunming 650201, China
- Laboratory for Quality Control and Traceability of Food, Tianjin Normal University , Tianjin 300387, China
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Zhao N, Li Z, Li Y, Liu G, Deng X, Ma Q, Hong C, Sun S. Rapid Qualitative and Quantitative Characterization of Arnebiae Radix by Near-Infrared Spectroscopy (NIRS) with Partial Least Squares—Discriminant Analysis (PLS-DA). ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2096627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Na Zhao
- College of Pharmacy/Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University College of Chemistry and Chemical Engineering, Shihezi, Xinjiang, China
| | - Zhaoyang Li
- College of Pharmacy/Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University College of Chemistry and Chemical Engineering, Shihezi, Xinjiang, China
| | - Youping Li
- College of Pharmacy/Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University College of Chemistry and Chemical Engineering, Shihezi, Xinjiang, China
| | - Gaixia Liu
- College of Pharmacy/Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University College of Chemistry and Chemical Engineering, Shihezi, Xinjiang, China
| | - Xiling Deng
- College of Pharmacy/Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University College of Chemistry and Chemical Engineering, Shihezi, Xinjiang, China
| | - Qian Ma
- College of Pharmacy/Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University College of Chemistry and Chemical Engineering, Shihezi, Xinjiang, China
| | - Chenglin Hong
- College of Pharmacy/Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University College of Chemistry and Chemical Engineering, Shihezi, Xinjiang, China
| | - Shiguo Sun
- College of Pharmacy/Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University College of Chemistry and Chemical Engineering, Shihezi, Xinjiang, China
- College of Chemistry and Pharmaceutical Engineering, Hebei University of Science and Technology, Shijiazhuang, China
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Hyperspectral Identification of Ginseng Growth Years and Spectral Importance Analysis Based on Random Forest. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The growth year of ginseng is very important as it affects its economic value and even defines if ginseng can be used as medicine or food. In the case of large-scale developments in the ginseng industry, a set of non-destructive, fast, and nonprofessional operations related to the growth year identification method is needed. The characteristics of ginseng reflectance spectral data were analyzed, and the growth year recognition model was constructed by a decision-tree-based random forest machine learning method. After independent verification, the accuracy of distinguishing ginseng food and medicine can reach 92.9%, with 6-year growth as the boundary, and 100%, with 5-year growth as the boundary. The research results show that the spectral change of ginseng is the most obvious in the fifth year, which provides a reference for the key research years based on chemical analyses and other methods. For the application of growth year recognition, the NIR band (1000–2500 nm) had little contribution to the recognition of ginseng growth years, and the band with the largest contribution was 400–650 nm. The recognition model based on machine learning provides a non-destructive, fast, and simple scheme with high accuracy for ginseng year recognition, and the spectral importance analysis conclusion of ginseng growth years provides a design reference for the development of special lightweight spectral equipment for year recognition.
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Long W, Hu Z, Wei L, Chen H, Liu T, Wang S, Guan Y, Yang X, Yang J, Fu H. Accurate identification of the geographical origins of lily using near-infrared spectroscopy combined with carbon dot-tetramethoxyporphyrin nanocomposite and chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 271:120932. [PMID: 35123189 DOI: 10.1016/j.saa.2022.120932] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
Near-infrared spectroscopy technique is a prevailing tool for quality control of foods and traditional Chinese medicines. However, it usually faced the problems of severe peak overlap, low classification accuracy and poor specificity. In this work, the potential of carbon dot-tetramethoxyporphyrin nanocomposite-based nano-effect near-infrared spectroscopy sensor combined with chemometric method was investigated for the accurate identification lily from different geographical origins. Partial least squares-discriminant analysis (PLS-DA) was used for differentiating geographical origins of lily based on the collected traditional and nano-effect near-infrared spectroscopy. Compared with traditional near-infrared spectroscopy, the nano-effect near-infrared spectroscopy obtains superior classification performance with 100% accuracy on the training and test set. The results showed that the proposed method based on near-infrared spectroscopy combined with nanocomposites and chemometrics could be considered as a promising tool for rapid discrimination of the authenticity of food and traditional Chinese medicine in the future.
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Affiliation(s)
- Wanjun Long
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Zikang Hu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Liuna Wei
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Hengye Chen
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Tingkai Liu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Siyu Wang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Yuting Guan
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Xiaolong Yang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China
| | - Jian Yang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, PR China.
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, PR China.
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Chen H, He Y. Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review. THE AMERICAN JOURNAL OF CHINESE MEDICINE 2022; 50:91-131. [PMID: 34931589 DOI: 10.1142/s0192415x22500045] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.
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Affiliation(s)
- Haiyang Chen
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
| | - Yu He
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
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Rapid quantification of adulterated Panax notoginseng powder by ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1016/j.cjac.2022.100055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Wang F, Jia B, Song X, Dai J, Li X, Gao H, Pan H, Yan H, Han B. Rapid Identification of Peucedanum Praeruptorum Dunn and its Adulterants by Hand-Held near-Infrared Spectroscopy. J AOAC Int 2021; 105:928-933. [PMID: 34954793 DOI: 10.1093/jaoacint/qsab160] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 11/25/2021] [Accepted: 12/04/2021] [Indexed: 11/14/2022]
Abstract
Peucedanum praeruptorum Dunn (PPD) is a traditional Chinese medical herb of high medical and economic value. However, PPD is often pretended by inexpensive plants. To establish an integrated methodology using hand-held near-infrared spectroscopy (NIRS) combined with chemical pattern recognition techniques to identify adulterated PPD products. The standard normal variate (SNV) was used to preprocess the original near-infrared spectra. Principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares regression analysis (PLSDA) were used to construct the recognition models. PCA analysis could not correctly distinguish PPD from non-PPD. However, based on absorbance in the spectral region of 1,405-2,442 nm and SVN pretreatment, the accuracy of the LDA model was above 90% at identifying genuine PPD. Compared with the LDA method, the PLSDA model is more stable and reliable, and its model prediction accuracy was 93.4%. The combination of near-infrared spectroscopy and chemometric methods based on a hand-held near-infrared spectrometer is an efficient, non-destructive, and reliable method for validating traditional Chinese medicine PPD. It can be used for rapid identification and quality evaluation of PPD in the field, medicinal material markets, and points of sale.
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Affiliation(s)
- Fang Wang
- College of Biological and Pharmaceutical Engineering, West Anhui University, Lu'an, China.,Anhui Province Traditional Chinese Medicine Resource Protection and Sustainable Utilization Engineering Laboratory, Lu'an, China
| | - Bin Jia
- College of Biological and Pharmaceutical Engineering, West Anhui University, Lu'an, China.,School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China
| | - Xiangwen Song
- College of Biological and Pharmaceutical Engineering, West Anhui University, Lu'an, China.,Anhui Province Traditional Chinese Medicine Resource Protection and Sustainable Utilization Engineering Laboratory, Lu'an, China
| | - Jun Dai
- College of Biological and Pharmaceutical Engineering, West Anhui University, Lu'an, China.,Anhui Province Traditional Chinese Medicine Resource Protection and Sustainable Utilization Engineering Laboratory, Lu'an, China
| | - Xiaoli Li
- College of Biological and Pharmaceutical Engineering, West Anhui University, Lu'an, China.,School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China
| | - Haidi Gao
- College of Biological and Pharmaceutical Engineering, West Anhui University, Lu'an, China
| | - Haoyu Pan
- College of Biological and Pharmaceutical Engineering, West Anhui University, Lu'an, China
| | - Hui Yan
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Bangxing Han
- College of Biological and Pharmaceutical Engineering, West Anhui University, Lu'an, China.,Anhui Province Traditional Chinese Medicine Resource Protection and Sustainable Utilization Engineering Laboratory, Lu'an, China
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14
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Lei L, Ke C, Xiao K, Qu L, Lin X, Zhan X, Tu J, Xu K, Liu Y. Identification of different bran-fried Atractylodis Rhizoma and prediction of atractylodin content based on multivariate data mining combined with intelligent color recognition and near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 262:120119. [PMID: 34243140 DOI: 10.1016/j.saa.2021.120119] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 06/01/2021] [Accepted: 06/24/2021] [Indexed: 06/13/2023]
Abstract
Unclear established standard of bran-fried Atractylodis Rhizoma (BFAR), a commonly used drug in Traditional Chinese Medicine (TCM), compromised its clinical efficacy. In this study, we explored the correlation between color and near-infrared spectroscopy (NIR) feature with content of atractylodin, then established a rapid recognition model for the optimal degree of processing for BFAR preparation. The results of the Pearson analysis indicated that the color values were significantly and positively correlated with atractylodin content. The back propagation artificial neural network algorithm and cluster analysis revealed the color of different BFAR could be accurately divided into three categories; subsequently, the color range for the optimal degrees of stir-frying was established as follows: R[red value (105.79-127.25)], G[green value(75.84-89.64)], B[blue value(33.33-42.73)], L[Lightness (81.26-95.09)].Using NIR, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and cluster analysis, three types of BFAR were accurately identified. The prediction model of atractylodin content was established using partial least squares regression analysis. The R2 of the validation set was 0.9717 and the root mean square error was 0.026. In the color judgment model, the processing degree of 8 batches of BFAR from the market is inferior. According to the NIR judgment model, the processing degree of all samples from the market is inferior. In conclusion, the best fire degree of BFAR can be identified quickly and accurately based on our established model. It is a potential method for quality evaluation of Chinese Materia Medica processing.
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Affiliation(s)
- Lin Lei
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430070, China
| | - Chang Ke
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430070, China
| | - Kunyu Xiao
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430070, China
| | - Linghang Qu
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430070, China
| | - Xiong Lin
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430070, China
| | - Xin Zhan
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430070, China
| | - Jiyuan Tu
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430070, China; Center for Hubei TCM Processing Technology Engineering, Wuhan 430070, China
| | - Kang Xu
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430070, China.
| | - Yanju Liu
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430070, China; Center for Hubei TCM Processing Technology Engineering, Wuhan 430070, China.
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15
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Sun Y, Yuan M, Liu X, Su M, Wang L, Zeng Y, Zang H, Nie L. A sample selection method specific to unknown test samples for calibration and validation sets based on spectra similarity. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119870. [PMID: 33957450 DOI: 10.1016/j.saa.2021.119870] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 02/10/2021] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
As is known to all, the construction of calibration and validation sets is of great importance for how to select representative samples into subsets so that the calibration model can be built, evaluated and predicted effectively for model development. In this study, a method was proposed for the calibration and validation sets constructed by selecting samples maximally similar to the test samples based on the spectra data. Both the Euclidean distance and Mahalanobis distance were attempted to estimate the spectra similarity. The method to select samples for calibration is more suitable and specific to unknown test samples in practical applications, thus improving the measurement accuracy. In addition, the optimization of calibration set size was carried out to avoid the influence of unnecessary samples. Two data sets of Salvia miltiorrhiza (S. miltiorrhiza) and corn by near infrared spectroscopy (NIR) were used to test the performance of the proposed method compared with two typical sample-selection algorithms, Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). The experimental results indicated that the proposed method could select a more targeted set of samples for the unknown test samples and had the superior predictive performance to the KS and SPXY methods.
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Affiliation(s)
- Yue Sun
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Meng Yuan
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Xiaoyan Liu
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Mei Su
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Linlin Wang
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Yingzi Zeng
- Shandong Wohua Pharmaceutical Technology Co., Ltd, Weifang 261205, China
| | - Hengchang Zang
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; National Glycoengineering Research Center, Jinan 250012, China
| | - Lei Nie
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.
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16
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Bai J, Yue P, Dong Q, Wang F, He C, Li Y, Guo J. Identification of geographical origins of Panax notoginseng based on HPLC multi-wavelength fusion profiling combined with average linear quantitative fingerprint method. Sci Rep 2021; 11:5126. [PMID: 33664325 PMCID: PMC7933339 DOI: 10.1038/s41598-021-84589-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 02/17/2021] [Indexed: 11/14/2022] Open
Abstract
The aim of this study was to establish a method for geographical origins identification of Panax notoginseng (P. notoginseng) based on abundant chromatographic spectral information. Characteristic fingerprints of P. notoginseng extracts samples were generated by Multi-wavelength Fusion Profiling (MWFP) method based on the HPLC fingerprints established at three wavelengths of 203 nm, 270 nm and 325 nm. The samples grouping results calculated with the averagely linear quantified fingerprint method (ALQFM) and the unsupervised statistical methods based on fusion fingerprints matches with the geographical origins. The Multi-wavelength Fusion Profiling (MWFP) method has been successfully applied to identification of geographical origins of P. notoginseng and shows the advantages compared with single—channel fingerprints. In addition, eight physiologically active components, including four saponins, two flavones and two amino acids, were identified from the most relevant ingredients of P. notoginseng geographical origins by fusion fingerprint-efficacy relationship analysis. Besides the recognized active saponins, other categories of active ingredients such as flavonoids and amino acids should be paid attention to in the producing areas identification or the quality judgment of P. notoginseng.
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Affiliation(s)
- Jing Bai
- Development and Utilization of Chinese Medicine Resources Key Laboratory Breeding Base, Key Laboratory of Systematic Research, The Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Pan Yue
- Development and Utilization of Chinese Medicine Resources Key Laboratory Breeding Base, Key Laboratory of Systematic Research, The Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Qiang Dong
- Development and Utilization of Chinese Medicine Resources Key Laboratory Breeding Base, Key Laboratory of Systematic Research, The Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Fang Wang
- Development and Utilization of Chinese Medicine Resources Key Laboratory Breeding Base, Key Laboratory of Systematic Research, The Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Chengyan He
- Development and Utilization of Chinese Medicine Resources Key Laboratory Breeding Base, Key Laboratory of Systematic Research, The Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Yang Li
- Development and Utilization of Chinese Medicine Resources Key Laboratory Breeding Base, Key Laboratory of Systematic Research, The Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
| | - Jinlin Guo
- Development and Utilization of Chinese Medicine Resources Key Laboratory Breeding Base, Key Laboratory of Systematic Research, The Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
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17
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Sun Y, Yuan M, Liu X, Su M, Wang L, Zeng Y, Zang H, Nie L. Comparative analysis of rapid quality evaluation of Salvia miltiorrhiza (Danshen) with Fourier transform near-infrared spectrometer and portable near-infrared spectrometer. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105492] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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18
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Zhang H, Li L, Quan S, Tian W, Zhang K, Nie L, Zang H. Novel Similarity Methods Evaluation and Feasible Application for Pharmaceutical Raw Material Identification with Near-Infrared Spectroscopy. ACS OMEGA 2020; 5:29864-29871. [PMID: 33251421 PMCID: PMC7689668 DOI: 10.1021/acsomega.0c03831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 10/12/2020] [Indexed: 06/12/2023]
Abstract
Raw material identification (RMID) is necessary and important to fulfill the quality and safety requirements in the pharmaceutical industry. Near-infrared (NIR) spectroscopy is a rapid, nondestructive, and commonly used analytical technique that could offer great advantages for RMID. In this study, two brand new similarity methods S1 and S2, which could reflect the similarity from the perspective of the inner product of the two vectors and the closeness with the cosine of the vectorial angle or correlation coefficient, were proposed. The ability of u and v factors to distinguish the difference between small peaks was investigated with the spectra of NIR. The results showed that the distinguishing ability of u is greater than v, and the distinguishing ability of S2 is greater than S1. Adjusting exponents u and v in these methods, which are variable and configurable parameters greater than 0 and less than infinity, could identify small peaks in different situations. Meanwhile, S1 and S2 could rapidly identify raw materials, suggesting that the on-site and in situ pharmaceutical RMID for large-volume applications can be highly achievable. The methods provided in this study are accurate and easier to use than traditional chemometric methods, which are important for the pharmaceutical RMID or other analysis.
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19
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Zhou D, Yu Y, Hu R, Li Z. Discrimination of Tetrastigma hemsleyanum according to geographical origin by near-infrared spectroscopy combined with a deep learning approach. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 238:118380. [PMID: 32388414 DOI: 10.1016/j.saa.2020.118380] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 04/14/2020] [Accepted: 04/14/2020] [Indexed: 06/11/2023]
Abstract
Recently, deep learning has presented as a powerful approach to overcome the deficiencies of the conventional biochemical approaches. In this study, a method for discriminating medicinal plant Tetrastigma hemsleyanum from different origins was proposed using near-infrared spectroscopy (NIRS) and deep learning models. Support vector machine (SVM), self-adaptive evolutionary extreme learning machine (SAE-ELM), and convolutional neural network (CNN) were used to process the near-infrared spectral data (4000-5600 cm-1). The results indicated that the average recognition accuracy of SVM on the test set samples (n = 60) reached 90%. The average recognition accuracy of SAE-ELM was 98.3%, while CNN correctly discriminated 100% of T. hemsleyanum from different origins. Notably, CNN avoids tedious redundant data preprocessing and is also able to save the trained model for the next call to achieve rapid detection. As above, this study provides an effective deep learning-based method for discriminating the geographical origins of T. hemsleyanum as well as providing a convenient and satisfactory approach to ensure the famous-region of other medicinal plants.
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Affiliation(s)
- Dongren Zhou
- Agriculture Ministry Key Laboratory of Healthy Freshwater Aquaculture, Key Laboratory of Fish Health and Nutrition of Zhejiang Province, Zhejiang Institute of Freshwater Fisheries, Huzhou 313001, PR China
| | - Yue Yu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212004, Jiangsu, PR China
| | - Renwei Hu
- College of Life Sciences, China Jiliang University, Hangzhou 310018, PR China
| | - Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212004, Jiangsu, PR China; College of Life Sciences, China Jiliang University, Hangzhou 310018, PR China.
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20
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Ou J, Wang R, Li X, Huang L, Yuan Q, Fang C, Wu D. Comparative Analysis of Free Amino Acids and Nucleosides in Different Varieties of Mume Fructus Based on Simultaneous Determination and Multivariate Statistical Analyses. Int J Anal Chem 2020; 2020:4767605. [PMID: 32802060 PMCID: PMC7416269 DOI: 10.1155/2020/4767605] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 06/15/2020] [Accepted: 06/24/2020] [Indexed: 12/04/2022] Open
Abstract
Mume Fructus (MF) contains a variety of organic acids, free amino acids, and nucleoside components, and studies have not yet analyzed the relationship between the components of free amino acids and nucleosides with the varieties of MF. A rapid and sensitive method was established for simultaneous determination of 21 free amino acids and 9 nucleosides in MF by ultrafast liquid chromatography-mass spectrometry. The analysis was carried out on a Waters XBridge Amide column (100 mm × 2.1 mm, 3.5 μm) with elution by the mobile phase of 0.2% aqueous formic acid (A) and 0.2% formic acid acetonitrile (B) at a flow rate of 0.2 mL/min with 1 μL per injection. The column temperature was maintained at 30°C. The target compounds were analyzed by the positive ion multiple reaction monitoring (MRM) mode. The comprehensive evaluation of the samples was carried out by principal component analysis (PCA) and technique for order preference by similarity to an ideal solution (TOPSIS) analysis. Results showed the method could simultaneously determine 30 components in MF. The content of total analytes in six mainstream varieties was different, exhibited the order Nangao > Daqingmei > Zhaoshuimei > Yanmei > Shishengme > Baimei, and aspartic acid and adenosine were the most abundant amino acid and nucleoside. PCA and OPLS-DA could easily distinguish the samples, and 11 components could be chemical markers of sample classification. TOPSIS implied that the quality of Nangao and Daqingmei was superior to the other varieties. The results could provide a reliable basis for quality evaluation and utilisation of medicinal and edible MF.
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Affiliation(s)
- Jinmei Ou
- Anhui University of Chinese Medicine, Heifei 230038, China
- China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Rui Wang
- Anhui University of Chinese Medicine, Heifei 230038, China
| | - Xiaoli Li
- Anhui University of Chinese Medicine, Heifei 230038, China
| | - Luqi Huang
- China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Qingjun Yuan
- China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Chengwu Fang
- Anhui University of Chinese Medicine, Heifei 230038, China
| | - Deling Wu
- Anhui University of Chinese Medicine, Heifei 230038, China
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21
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Bian X, Lu Z, van Kollenburg G. Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:3499-3507. [PMID: 32672249 DOI: 10.1039/d0ay00285b] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other similar herbs (either from the same genus or of similar appearance). A total of 191 samples, including 40 ASR, 39 Angelicae Pubescentis Radix (APR), 38 Chuanxiong Rhizoma (CR), 35 Atractylodis Macrocephalae Rhizoma (AMR) and 39 Angelicae Dahuricae Radix (ADR), were collected and divided into the training and prediction sets. Principal component analysis (PCA) was used for observing the sample cluster tendency of the calibration set. Different preprocessing methods were investigated and the optimal preprocessing combination was selected according to spectral signal characteristics and three-dimensional PCA (3D PCA) clustering results. The final discriminant model was built using extreme learning machine (ELM). The exploratory studies on the raw spectra and their 3D PCA scores indicate that the classification of the five herbs cannot be achieved by PCA of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to different degrees. Furthermore, their combination in the order of CWT + autoscaling + SG smoothing can enhance the spectral resolution and obtain the best clustering result. These results are also validated using ELM models of raw and different preprocessing methods. By using CWT + autoscaling + SG smoothing + ELM, 100% classification accuracy can be achieved in both the calibration set and the prediction set. Therefore, the developed method could be used as a rapid, economic and effective method for discriminating the five herbs used in this study.
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Affiliation(s)
- Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemistry and Chemical Engineering, Tiangong University, Tianjin, 300387, P. R. China. and Department of Analytical Chemistry, Institute for Molecules and Materials (IMM), Radboud University, 6500 GL Nijmegen, The Netherlands
| | - Zhankui Lu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemistry and Chemical Engineering, Tiangong University, Tianjin, 300387, P. R. China.
| | - Geert van Kollenburg
- Department of Analytical Chemistry, Institute for Molecules and Materials (IMM), Radboud University, 6500 GL Nijmegen, The Netherlands
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22
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Zheng Y, Fan C, Liu M, Chen Y, Lu Z, Xu N, Huang H, Zeng H, Liu S, Cao H, Liu J, Yu L. Overall quality control of the chemical and bioactive consistency of ShengMai Formula. J Pharm Biomed Anal 2020; 189:113411. [PMID: 32603924 DOI: 10.1016/j.jpba.2020.113411] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/30/2020] [Accepted: 06/01/2020] [Indexed: 12/12/2022]
Abstract
ShengMai Formula (SMF), a famous traditional Chinese medicine (TCM) formula, has been extensively used for treating the diseases caused by Qi-Yin deficiency for almost 1000 years. However, few studies are elucidated about its batch-to-batch quality control system and the quality control markers remain largely unrevealed, which have hindered the development and utilization of SMF. In this study, we aimed to screen the optimal quality control markers to evaluate the overall quality consistency of SMF. High-performance liquid chromatography (HPLC) fingerprint coupled with similarity analysis (SA), principal components analysis (PCA) and hierarchical cluster analysis (HCA) was firstly established to hunt for the discriminant components that resulting in the chemical inconsistence among different batches of SMF. Subsequently, different batches of samples were selected to explore their immunomodulatory activities by neutral red method, Cell Counting Kit-8 (CCK-8) assay and enzyme-linked immunosorbent assay (ELISA). Finally, the fingerprint-efficacy relationships were further illuminated to discover the major bioactive compositions using grey relational analysis (GRA), partial least squares regression (PLSR) analysis and artificial neural network (ANN) analysis. As a result, schisandrol A, schisandrol B, methylophiopogonanone A, schisandrin B, ginsenoside Rf, ginsenoside Rb1, ginsenoside Rg2 and ginsenoside Rb2 were selected as the quality control markers and thus their simultaneous quantification was performed to both evaluate the batch-to-batch chemical and bioactive consistency among different batches of SMF. Our investigation not only stresses the necessity of consistency in efficacy besides chemical consistency, but also provides a comprehensive and powerful quality assessment approach, which is promising to monitor the overall quality consistency of SMF.
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Affiliation(s)
- Yuanru Zheng
- Traditional Chinese Pharmacological Laboratory, Third Level Research Laboratory of State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, PR China; Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, Guangzhou, 510515, PR China
| | - Chunlin Fan
- Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, PR China
| | - Menghua Liu
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, 510515, PR China
| | - Ye Chen
- Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, PR China
| | - Zibin Lu
- Traditional Chinese Pharmacological Laboratory, Third Level Research Laboratory of State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, PR China; Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, Guangzhou, 510515, PR China
| | - Nishan Xu
- Traditional Chinese Pharmacological Laboratory, Third Level Research Laboratory of State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, PR China; Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, Guangzhou, 510515, PR China
| | - Hefei Huang
- Traditional Chinese Pharmacological Laboratory, Third Level Research Laboratory of State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, PR China; Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, Guangzhou, 510515, PR China
| | - Huhu Zeng
- Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, PR China
| | - Shanhong Liu
- Traditional Chinese Pharmacological Laboratory, Third Level Research Laboratory of State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, PR China; Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, Guangzhou, 510515, PR China
| | - Huihui Cao
- Traditional Chinese Pharmacological Laboratory, Third Level Research Laboratory of State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, PR China; Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, Guangzhou, 510515, PR China
| | - Junshan Liu
- Traditional Chinese Pharmacological Laboratory, Third Level Research Laboratory of State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, PR China; Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, Guangzhou, 510515, PR China.
| | - Linzhong Yu
- Traditional Chinese Pharmacological Laboratory, Third Level Research Laboratory of State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, PR China; Guangdong Provincial Key Laboratory of Chinese Medicine Pharmaceutics, Guangzhou, 510515, PR China.
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Yue J, Zuo Z, Huang H, Wang Y. Application of Identification and Evaluation Techniques for Ethnobotanical Medicinal Plant of Genus Panax: A Review. Crit Rev Anal Chem 2020; 51:373-398. [PMID: 32166968 DOI: 10.1080/10408347.2020.1736506] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Genus Panax, as worldwide medicinal plants, has a medical history for thousands of years. Most of the entire genus are traditional ethnobotanical medicine in China, Myanmar, Thailand, Vietnam and Laos, which have given rise to international attention and use. This paper reviewed more than 210 articles and related books on the research of Panax medicinal plants and their Chinese patent medicines published in the last 30 years. The purpose was to review and summarize the species classification, geographical distribution, and ethnic minorities medicinal records of the genus Panax, and further to review the analytical tools and data analysis methods for the authentication and quality assessment of Panax medicinal materials and Chinese patent medicines. Five main technologies applied in the identification and evaluation of Panax have been introduced and summarized. Chromatography was the most widely used one. Further research and development of molecular identification technology had the potential to become a mainstream identification technology. In addition, some novel, controversial, and worthy methods including electronic noses, electronic eyes, and DNA barcoding were also introduced. At the same time, more than 80% of the researches were carried out by a combination of chemometric pattern-recognition technologies and multi-analysis technologies. All the technologies and methods applied can provide strong support and guarantee for the identification and evaluation of genus Panax, and also conduce to excellent reference value for the development and in-depth research of new technologies in Panax.
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Affiliation(s)
- Jiaqi Yue
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China.,College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Zhitian Zuo
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Hengyu Huang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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Yang Y, Ju Z, Yang Y, Zhang Y, Yang L, Wang Z. Phytochemical analysis of Panax species: a review. J Ginseng Res 2020; 45:1-21. [PMID: 33437152 PMCID: PMC7790905 DOI: 10.1016/j.jgr.2019.12.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/29/2019] [Accepted: 12/31/2019] [Indexed: 12/22/2022] Open
Abstract
Panax species have gained numerous attentions because of their various biological effects on cardiovascular, kidney, reproductive diseases known for a long time. Recently, advanced analytical methods including thin layer chromatography, high-performance thin layer chromatography, gas chromatography, high-performance liquid chromatography, ultra-high performance liquid chromatography with tandem ultraviolet, diode array detector, evaporative light scattering detector, and mass detector, two-dimensional high-performance liquid chromatography, high speed counter-current chromatography, high speed centrifugal partition chromatography, micellar electrokinetic chromatography, high-performance anion-exchange chromatography, ambient ionization mass spectrometry, molecularly imprinted polymer, enzyme immunoassay, 1H-NMR, and infrared spectroscopy have been used to identify and evaluate chemical constituents in Panax species. Moreover, Soxhlet extraction, heat reflux extraction, ultrasonic extraction, solid phase extraction, microwave-assisted extraction, pressurized liquid extraction, enzyme-assisted extraction, acceleration solvent extraction, matrix solid phase dispersion extraction, and pulsed electric field are discussed. In this review, a total of 219 articles published from 1980 to 2018 are investigated. Panax species including P. notoginseng, P. quinquefolius, sand P. ginseng in the raw and processed forms from different parts, geographical origins, and growing times are studied. Furthermore, the potential biomarkers are screened through the previous articles. It is expected that the review can provide a fundamental for further studies.
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Affiliation(s)
- Yuangui Yang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China
| | - Zhengcai Ju
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China
| | - Yingbo Yang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China
| | - Yanhai Zhang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China
| | - Li Yang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China.,Shanghai R&D Center for Standardization of Chinese Medicines, China
| | - Zhengtao Wang
- The MOE Key Laboratory for Standardization of Chinese Medicines and the SATCM Key Laboratory for New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, China.,Shanghai R&D Center for Standardization of Chinese Medicines, China
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Roberto de Alvarenga Junior B, Lajarim Carneiro R. Chemometrics Approaches in Forced Degradation Studies of Pharmaceutical Drugs. Molecules 2019; 24:E3804. [PMID: 31652589 PMCID: PMC6833076 DOI: 10.3390/molecules24203804] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/07/2019] [Accepted: 10/08/2019] [Indexed: 02/03/2023] Open
Abstract
Chemometrics is the chemistry field responsible for planning and extracting the maximum of information of experiments from chemical data using mathematical tools (linear algebra, statistics, and so on). Active pharmaceutical ingredients (APIs) can form impurities when exposed to excipients or environmental variables such as light, high temperatures, acidic or basic conditions, humidity, and oxidative environment. By considering that these impurities can affect the safety and efficacy of the drug product, it is necessary to know how these impurities are yielded and to establish the pathway of their formation. In this context, forced degradation studies of pharmaceutical drugs have been used for the characterization of physicochemical stability of APIs. These studies are also essential in the validation of analytical methodologies, in order to prove the selectivity of methods for the API and its impurities and to create strategies to avoid the formation of degradation products. This review aims to demonstrate how forced degradation studies have been actually performed and the applications of chemometric tools in related studies. Some papers are going to be discussed to exemplify the chemometric applications in forced degradation studies.
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Shen T, Yu H, Wang YZ. Assessing Geographical Origin of Gentiana Rigescens Using Untargeted Chromatographic Fingerprint, Data Fusion and Chemometrics. Molecules 2019; 24:molecules24142562. [PMID: 31337159 PMCID: PMC6680800 DOI: 10.3390/molecules24142562] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 07/10/2019] [Accepted: 07/12/2019] [Indexed: 12/22/2022] Open
Abstract
Gentiana rigescens Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of G. rigescens and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies (“low-level” and “mid-level”). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities (the accuracy is higher than 99% and sensibility, specificity, Matthews correlation coefficient, and efficiency range from 0.95 to 1.00). Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of G. rigescens and developed a reliable and accurate identification method for G. rigescens that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials.
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Affiliation(s)
- Tao Shen
- Yunnan Herbal Laboratory, Institute of Herb Biotic Resources, School of Life and Sciences, Yunnan University, Kunming 650091, China
- The International Joint Research Center for Sustainable Utilization of Cordyceps Bioresouces in China and Southeast Asia, Yunnan University, Kunming 650091, China
- College of Chemistry, Biological and Environment, Yuxi Normal University, Yu'xi 653100, China
| | - Hong Yu
- Yunnan Herbal Laboratory, Institute of Herb Biotic Resources, School of Life and Sciences, Yunnan University, Kunming 650091, China.
- The International Joint Research Center for Sustainable Utilization of Cordyceps Bioresouces in China and Southeast Asia, Yunnan University, Kunming 650091, China.
| | - Yuan-Zhong Wang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China
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Identification of anti-inflammatory components in Sinomenii Caulis based on spectrum-effect relationship and chemometric methods. J Pharm Biomed Anal 2019; 167:38-48. [DOI: 10.1016/j.jpba.2019.01.047] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 01/26/2019] [Accepted: 01/28/2019] [Indexed: 11/18/2022]
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Chen H, Tan C, Lin Z, Li H. Quantifying several adulterants of notoginseng powder by near-infrared spectroscopy and multivariate calibration. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 211:280-286. [PMID: 30557845 DOI: 10.1016/j.saa.2018.12.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 11/30/2018] [Accepted: 12/02/2018] [Indexed: 06/09/2023]
Abstract
The authentication of traditional Chinese medicine (TCM) is critically important for public-health and economic terms. Notoginseng, a classical TCM of high economic and medical value, could be easily adulterated with Sophora flavescens powder (SFP), corn flour (CF) or other analogues of low-grade (ALG) because of their similar tastes, appearances and much lower cost. The main objective of this study was to evaluate the feasibility of applying of near-infrared (NIR) spectroscopy and multivariate calibration for identifying and quantifying several common adulterants in notoginseng powder. Two datasets were prepared for experiment. The competitive adaptive reweighted sampling (CARS) was used to select informative variables. Two different schemes were used for sample set partition. Model population analysis (MPA) was made. The results showed that, the constructed partial least squares (PLS) model using a reduced set of variables from CARS can provide superior performance to the full-spectrum PLS model. Also, the sample set partition is very of great importance. It seems that the combination of NIR spectroscopy, CARS and PLS is feasible to quantify common adulterants in notoginseng powder.
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Affiliation(s)
- Hui Chen
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; Hospital, Yibin University, Yibin, Sichuan 644000, China
| | - Chao Tan
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China.
| | - Zan Lin
- The First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China
| | - Hongjin Li
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China
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