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Wang H, Ma JX, Zhou M, Si J, Cui BK. Current advances and potential trends of the polysaccharides derived from medicinal mushrooms sanghuang. Front Microbiol 2022; 13:965934. [PMID: 35992671 PMCID: PMC9382022 DOI: 10.3389/fmicb.2022.965934] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/04/2022] [Indexed: 12/16/2022] Open
Abstract
For thousands of years, sanghuang is distinctive as a general designation for a group of precious and rare Chinese medicinal mushrooms. Numerous investigations have revealed that polysaccharide is one of the important biological active ingredients of sanghuang with various excellent biological activities, including antioxidant, anti-aging, anti-tumor, immunomodulatory, anti-inflammatory, anti-diabetic, hepatoprotective, and anti-microbial functionalities. For the past two decades, preparation, structural characterization, and reliable bioactivities of the polysaccharides from fruiting bodies, cultured mycelia, and fermentation broth of sanghuang have been arousing extensive interest, and particularly, different strains, sources, and isolation protocols might result in obvious discrepancies in structural features and bioactivities. Therefore, this review summarizes the recent reports on preparation strategies, structural features, bioactivities, and structure-activity relationships of sanghuang polysaccharides, which will enrich the knowledge on the values of natural sanghuang polysaccharides and support their further development and utilization as therapeutic agents, vaccines, and functional foods in tonic and clinical treatment.
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Wu L, Gao Y, Ren WC, Su Y, Li J, Du YQ, Wang QH, Kuang HX. Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 264:120327. [PMID: 34474220 DOI: 10.1016/j.saa.2021.120327] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/16/2021] [Accepted: 08/24/2021] [Indexed: 06/13/2023]
Abstract
In this study, a classification model was established based on near-infrared spectroscopy and random forest method to accurately distinguish three samples of Schisandra chinensis from different habitats. At the same time, the feasibility of fast and effective prediction of polysaccharide contents in Schisandra chinensis by near-infrared spectroscopy combined with chemometrics was evaluated. In this paper, phenol sulfuric acid method was used to determine the content of total polysaccharides in samples, and partial least squares regression algorithm was used to link the spectral information with the reference value. Different spectral pretreatment methods were used to optimize the model to improve its predictability and stability. The results showed that random forest could distinguish these samples accurately, with an accuracy of 97.47%. In the established prediction model, the RMSEC of the optimal model calibration set is 0.0012, and the coefficient of determination R is 0.9976. The RMSEP of prediction set is 0.0024, the coefficient of determination R is 0.9922, and the RPD is 11.36. In general, the method has good stability and applicability, which provides a new analytical method for the identification of Schisandra chinensis origin and quality evaluation.
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Affiliation(s)
- Lun Wu
- Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, China
| | - Yue Gao
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Key Laboratory of Medicinal Materials, Chinese Academy of Sciences, Harbin 150040, China
| | - Wen-Chen Ren
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Key Laboratory of Medicinal Materials, Chinese Academy of Sciences, Harbin 150040, China
| | - Yang Su
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Key Laboratory of Medicinal Materials, Chinese Academy of Sciences, Harbin 150040, China; Faculty of Microbiology and Immunogenetics, University of California, Los Angeles, CA 90095, USA.
| | - Jing Li
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Key Laboratory of Medicinal Materials, Chinese Academy of Sciences, Harbin 150040, China
| | - Ya-Qi Du
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Key Laboratory of Medicinal Materials, Chinese Academy of Sciences, Harbin 150040, China
| | - Qiu-Hong Wang
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510000, China
| | - Hai-Xue Kuang
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Key Laboratory of Medicinal Materials, Chinese Academy of Sciences, Harbin 150040, China
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Identification of Baha'sib mung beans based on Fourier transform near infrared spectroscopy and partial least squares. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhu W, Hong H, Hong Z, Kang X, Du W, Ge W, Li C. Rapid Quality Identification of Decoction Pieces of Crude and Processed Corydalis Rhizoma by Near-Infrared Spectroscopy Coupled with Chemometrics. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2021; 2021:1936057. [PMID: 34336355 PMCID: PMC8324354 DOI: 10.1155/2021/1936057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/05/2021] [Accepted: 07/15/2021] [Indexed: 06/13/2023]
Abstract
In order to identify the quality of crude and processed Corydalis Rhizoma decoction pieces, the research established a simple, fast, reliable, and validated near-infrared qualitative and quantitative model combined with chemometrics. 51 batches of crude and 40 batches of processed Corydalis Rhizoma from the Zhejiang and Jiangsu provinces of China were collected and analyzed. Crude and processed Corydalis Rhizoma samples were crushed to obtain NIR spectra. The content of seven alkaloids in crude and processed Corydalis Rhizoma was determined by high-performance liquid chromatography (HPLC). Pretreatment methods were screened such as normalization methods, offset filtering methods, and smoothing. Combined with partial least squares-discriminant analysis (PLS-DA) and partial least squares (PLS), the qualitative and quantitative models of crude and processed Corydalis Rhizoma were established, and the correlation coefficient (R 2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP) were used as evaluation indexes. Tetrahydropalmatine was used as an example for screening pretreatment methods; the results showed that MSC combined with the second derivative and no smoothing and the model with the wavelength range of 10000-5000 cm-1 had the best predictive ability and applied to all seven alkaloid components. Among them, the correlation coefficients were all higher than 0.99, and RMSEC and RMSEP were all less than 1%. The qualitative and quantitative model of the seven alkaloids in Corydalis Rhizoma can effectively identify the crude and processed Corydalis Rhizoma and determine the content of the seven alkaloids. By studying the NIR qualitative and quantitative models of crude and processed Corydalis Rhizoma, we can achieve rapid discrimination and quantitative prediction of crude and processed Corydalis Rhizoma. These methods can greatly improve the efficiency of traditional Chinese medicine analysis and provide a strong scientific basis for the quality identification and control of traditional Chinese medicine.
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Affiliation(s)
- Weihao Zhu
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China
| | - Hao Hong
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China
| | - Zhihui Hong
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China
| | - Xianjie Kang
- Research Center of TCM Processing Technology, Zhejiang Chinese Medical University, Hangzhou 311401, China
| | - Weifeng Du
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China
- Research Center of TCM Processing Technology, Zhejiang Chinese Medical University, Hangzhou 311401, China
| | - Weihong Ge
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 311402, China
- Research Center of TCM Processing Technology, Zhejiang Chinese Medical University, Hangzhou 311401, China
| | - Changyu Li
- Academy of Chinese Medical Sciences, Zhejiang Chinese Medical University, Hangzhou 311401, China
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SUN JJ, YANG WD, FENG MC, XIAO LJ, SUN H, KUBAR MS. Adaptive Variable Re-weighting and Shrinking Approach for Variable Selection in Multivariate Calibration for Near-infrared Spectroscopy. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2021. [DOI: 10.1016/s1872-2040(21)60102-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Huang Y, Wang T, Yin G, Wang J, Jiang K, Tu J. High-performance liquid chromatography-based fingerprint analysis with chemical pattern recognition for evaluation of Mahonia bealei (Fort.) Carr. J Sep Sci 2020; 43:3625-3635. [PMID: 32700401 DOI: 10.1002/jssc.201901219] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 07/02/2020] [Accepted: 07/19/2020] [Indexed: 12/24/2022]
Abstract
A simple and efficient high-performance liquid chromatography method combined with chemical pattern recognition was established for quality evaluation of Mahonia bealei (Fort.) Carr. A common pattern of 30 characteristic peaks was applied for similarity analysis, hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis in the 37 batches of M. bealei (Fort.) Carr. to discriminate wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes. The results showed that partial least squares discriminant analysis was the most effective method for discrimination. Eight characteristics peaks with higher variable importance in projection values were selected for pattern recognition model. A permutation test and 26 batches of testing set samples were performed to validate the model that was successfully established. All of the training and testing set samples were correctly classified into three clusters (wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes) based on the selected chemical markers. Moreover, 26 batches of unknown samples were used to predict the accuracy of the established model with a discrimination accuracy of 100%. The obtained results indicated that the method showed great potential application for accurate evaluation and prediction of the quality of M. bealei (Fort.) Carr.
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Affiliation(s)
- Yang Huang
- Shenzhen Institute for Drug Control, Shenzhen, P. R. China.,State Key Laboratory of Natural Medicines, Department of Pharmaceutics, China Pharmaceutical University, Nanjing, P. R. China.,Key Laboratory of Molecular Target & Clinical Pharmacology and the State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences & the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, P. R. China
| | - Tiejie Wang
- Shenzhen Institute for Drug Control, Shenzhen, P. R. China.,Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, P. R. China
| | - Guo Yin
- Shenzhen Institute for Drug Control, Shenzhen, P. R. China.,Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, P. R. China
| | - Jue Wang
- Shenzhen Institute for Drug Control, Shenzhen, P. R. China.,Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, P. R. China
| | - Kun Jiang
- Shenzhen Institute for Drug Control, Shenzhen, P. R. China.,Shenzhen Key Laboratory of Drug Quality Standard Research, Shenzhen, P. R. China
| | - Jiasheng Tu
- State Key Laboratory of Natural Medicines, Department of Pharmaceutics, China Pharmaceutical University, Nanjing, P. R. China
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