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Benedetto A, Robotti E, Belay MH, Ghignone A, Fabbris A, Goggi E, Cerruti S, Manfredi M, Barberis E, Peletto S, Arillo A, Giaccio N, Masini MA, Brandi J, Cecconi D, Marengo E, Brizio P. Multi-Omics Approaches for Freshness Estimation and Detection of Illicit Conservation Treatments in Sea Bass ( Dicentrarchus Labrax): Data Fusion Applications. Int J Mol Sci 2024; 25:1509. [PMID: 38338789 PMCID: PMC10855268 DOI: 10.3390/ijms25031509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024] Open
Abstract
Fish freshness consists of complex endogenous and exogenous processes; therefore, the use of a few parameters to unravel illicit practices could be insufficient. Moreover, the development of strategies for the identification of such practices based on additives known to prevent and/or delay fish spoilage is still limited. The paper deals with the identification of the effect played by a Cafodos solution on the conservation state of sea bass at both short-term (3 h) and long-term (24 h). Controls and treated samples were characterized by a multi-omic approach involving proteomics, lipidomics, metabolomics, and metagenomics. Different parts of the fish samples were studied (muscle, skin, eye, and gills) and sampled through a non-invasive procedure based on EVA strips functionalized by ionic exchange resins. Data fusion methods were then applied to build models able to discriminate between controls and treated samples and identify the possible markers of the applied treatment. The approach was effective in the identification of the effect played by Cafodos that proved to be different in the short- and long-term and complex, involving proteins, lipids, and small molecules to a different extent.
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Affiliation(s)
- Alessandro Benedetto
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Elisa Robotti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Masho Hilawie Belay
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
- Department of Chemistry, Mekelle University, Mekelle P.O. Box 231, Ethiopia
| | - Arianna Ghignone
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Alessia Fabbris
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Eleonora Goggi
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Simone Cerruti
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Marcello Manfredi
- Department of Translational Medicine, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy;
| | - Elettra Barberis
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Simone Peletto
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Alessandra Arillo
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Nunzia Giaccio
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
| | - Maria Angela Masini
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Jessica Brandi
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy; (J.B.); (D.C.)
| | - Daniela Cecconi
- Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy; (J.B.); (D.C.)
| | - Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; (M.H.B.); (A.G.); (A.F.); (E.G.); (S.C.); (E.B.); (M.A.M.); (E.M.)
| | - Paola Brizio
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, Via Bologna 148, 10154 Torino, Italy; (A.B.); (S.P.); (A.A.); (N.G.); (P.B.)
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Huang R, Ma S, Dai S, Zheng J. Application of Data Fusion in Traditional Chinese Medicine: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 24:106. [PMID: 38202967 PMCID: PMC10781265 DOI: 10.3390/s24010106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 12/22/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024]
Abstract
Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.
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Affiliation(s)
- Rui Huang
- National Institutes for Food and Drug Control, Beijing 102629, China; (R.H.); (S.M.)
- School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Shuangcheng Ma
- National Institutes for Food and Drug Control, Beijing 102629, China; (R.H.); (S.M.)
| | - Shengyun Dai
- National Institutes for Food and Drug Control, Beijing 102629, China; (R.H.); (S.M.)
| | - Jian Zheng
- National Institutes for Food and Drug Control, Beijing 102629, China; (R.H.); (S.M.)
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Naumann L, Haun A, Höchsmann A, Mohr M, Novák M, Flottmann D, Neusüß C. Augmented region of interest for untargeted metabolomics mass spectrometry (AriumMS) of multi-platform-based CE-MS and LC-MS data. Anal Bioanal Chem 2023; 415:3137-3154. [PMID: 37225900 PMCID: PMC10287804 DOI: 10.1007/s00216-023-04715-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/16/2023] [Accepted: 04/20/2023] [Indexed: 05/26/2023]
Abstract
In mass spectrometry (MS)-based metabolomics, there is a great need to combine different analytical separation techniques to cover metabolites of different polarities and apply appropriate multi-platform data processing. Here, we introduce AriumMS (augmented region of interest for untargeted metabolomics mass spectrometry) as a reliable toolbox for multi-platform metabolomics. AriumMS offers augmented data analysis of several separation techniques utilizing a region-of-interest algorithm. To demonstrate the capabilities of AriumMS, five datasets were combined. This includes three newly developed capillary electrophoresis (CE)-Orbitrap MS methods using the recently introduced nanoCEasy CE-MS interface and two hydrophilic interaction liquid chromatography (HILIC)-Orbitrap MS methods. AriumMS provides a novel mid-level data fusion approach for multi-platform data analysis to simplify and speed up multi-platform data processing and evaluation. The key feature of AriumMS lies in the optimized data processing strategy, including parallel processing of datasets and flexible parameterization for processing of individual separation methods with different peak characteristics. As a case study, Saccharomyces cerevisiae (yeast) was treated with a growth inhibitor, and AriumMS successfully differentiated the metabolome based on the augmented multi-platform CE-MS and HILIC-MS investigation. As a result, AriumMS is proposed as a powerful tool to improve the accuracy and selectivity of metabolome analysis through the integration of several HILIC-MS/CE-MS techniques.
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Affiliation(s)
- Lukas Naumann
- Department of Chemistry, Aalen University, Beethovenstraße 1, 73430, Aalen, Germany
| | - Adrian Haun
- Department of Chemistry, Aalen University, Beethovenstraße 1, 73430, Aalen, Germany
| | - Alisa Höchsmann
- Department of Chemistry, Aalen University, Beethovenstraße 1, 73430, Aalen, Germany
| | - Michael Mohr
- Department of Chemistry, Aalen University, Beethovenstraße 1, 73430, Aalen, Germany
| | - Martin Novák
- Department of Chemistry, Aalen University, Beethovenstraße 1, 73430, Aalen, Germany
| | - Dirk Flottmann
- Department of Chemistry, Aalen University, Beethovenstraße 1, 73430, Aalen, Germany
| | - Christian Neusüß
- Department of Chemistry, Aalen University, Beethovenstraße 1, 73430, Aalen, Germany.
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Hou F, Fan X, Gui X, Li H, Li H, Wang Y, Shi J, Zhang L, Yao J, Li X, Liu R. Development of a variety and quality evaluation method for Amomi fructus using GC, electronic tongue, and electronic nose. Front Chem 2023; 11:1188219. [PMID: 37398979 PMCID: PMC10310405 DOI: 10.3389/fchem.2023.1188219] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/07/2023] [Indexed: 07/04/2023] Open
Abstract
Amomi fructus is rich in volatile components and valuable as a medicine and edible spice. However, the quality of commercially available A. fructus varies, and issues with mixed sources and adulteration by similar products are common. In addition, due to incomplete identification methods, rapid detection of the purchased A. fructus quality is still an issue. In this study, we developed qualitative and quantitative evaluation models to assess the variety and quality of A. fructus using GC, electronic tongue, and electronic nose to provide a rapid and accurate variety and quality evaluation method of A. fructus. The models performed well; the qualitative authenticity model had an accuracy of 1.00 (n = 64), the accuracy of the qualitative origin model was 0.86 (n = 44), and the quantitative model was optimal on the sensory fusion data from the electronic tongue and electronic nose combined with borneol acetate content, with R 2 = 0.7944, RMSEF = 0.1050, and RMSEP = 0.1349. The electronic tongue and electronic nose combined with GC quickly and accurately evaluated the variety and quality of A. fructus, and the introduction of multi-source information fusion technology improved the model prediction accuracy. This study provides a useful tool for quality evaluation of medicine and food.
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Affiliation(s)
- Fuguo Hou
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Xuehua Fan
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Xinjing Gui
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Han Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Haiyang Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Yanli Wang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Junhan Shi
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Lu Zhang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Jing Yao
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Xuelin Li
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Ruixin Liu
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
- Engineering Research Center for Pharmaceutics of Chinese Materia Medica and New Drug Development, Ministry of Education, Beijing, China
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Xia Z, Che X, Ye L, Zhao N, Guo D, Peng Y, Lin Y, Liu X. A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis. Molecules 2023; 28:molecules28041778. [PMID: 36838765 PMCID: PMC9965801 DOI: 10.3390/molecules28041778] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.
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Affiliation(s)
- Ziyi Xia
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Xiaoqing Che
- Shandong Runzhong Pharmaceutical Co., Ltd., Yantai 256603, China
| | - Lei Ye
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Na Zhao
- Key Laboratory of Xinjiang Phytomedicine Resources and Utilization in Ministry of Education, School of Pharmacy, Shihezi University, Shihezi 832002, China
| | - Dongxiao Guo
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
| | - Yanfang Peng
- Pharmacy Faculty, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Yongqiang Lin
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
- Correspondence: (Y.L.); (X.L.)
| | - Xiaona Liu
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
- Correspondence: (Y.L.); (X.L.)
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Comparison of Ophiopogon japonicus and Liriope spicata var. prolifera from Different Origins Based on Multi-Component Quantification and Anticancer Activity. Molecules 2023; 28:molecules28031045. [PMID: 36770712 PMCID: PMC9920971 DOI: 10.3390/molecules28031045] [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: 12/30/2022] [Revised: 01/15/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023] Open
Abstract
The tuberous root of Ophiopogon japonicus (Thunb.) Ker-Gawl. is a well-known Chinese medicine also called Maidong (MD) in Chinese. It could be divided into "Chuanmaidong" (CMD) and "Zhemaidong" (ZMD), according to the geographic origins. Meanwhile, the root of Liriope spicata (Thunb.) Lour. var. prolifera Y. T. Ma (SMD) is occasionally used as a substitute for MD in the market. In this study, a reliable pressurized liquid extraction and HPLC-DAD-ELSD method was developed for the simultaneous determination of nine chemical components, including four steroidal saponins (ophiopojaponin C, ophiopogonin D, liriopesides B and ophiopogonin D'), four homoisoflavonoids (methylophiopogonone A, methylophiopogonone B, methylophiopogonanone A and methylophiopogonanone B) and one sapogenin (ruscogenin) in CMD, ZMD and SMD. The method was validated in terms of linearity, sensitivity, precision, repeatability and accuracy, and then applied to the real samples from different origins. The results indicated that there were significant differences in the contents of the investigated compounds in CMD, ZMD and SMD. Ruscogenin was not detected in all the samples, and liriopesides B was only found in SMD samples. CMD contained higher ophiopogonin D and ophiopogonin D', while the other compounds were more abundant in ZMD. Moreover, the anticancer effects of the herbal extracts and selected components against A2780 human ovarian cancer cells were also compared. CMD and ZMD showed similar cytotoxic effects, which were stronger than those of SMD. The effects of MD may be due to the significant anticancer potential of ophiopognin D' and homoisoflavonoids. These results suggested that there were great differences in the chemical composition and pharmacological activity among CMD, ZMD and SMD; thus, their origins should be carefully considered in clinical application.
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Gui XJ, Li H, Ma R, Tian LY, Hou FG, Li HY, Fan XH, Wang YL, Yao J, Shi JH, Zhang L, Li XL, Liu RX. Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy. Front Chem 2023; 11:1179039. [PMID: 37188096 PMCID: PMC10175593 DOI: 10.3389/fchem.2023.1179039] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.
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Affiliation(s)
- Xin-Jing Gui
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Han Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Rui Ma
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Liang-Yu Tian
- Zhengzhou Traditional Chinese Hospital of Orthopedics, Zhengzhou, China
| | - Fu-Guo Hou
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Hai-Yang Li
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Xue-Hua Fan
- School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
| | - Yan-Li Wang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Jing Yao
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Jun-Han Shi
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Lu Zhang
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
| | - Xue-Lin Li
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
- *Correspondence: Rui-Xin Liu, ; Xue-Lin Li,
| | - Rui-Xin Liu
- Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Zhengzhou, China
- Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan and Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
- Henan Provincial Key Laboratory for Clinical Pharmacy of Traditional Chinese Medicine, Zhengzhou, China
- Engineering Research Center for Pharmaceutics of Chinese Materia Medica and New Drug Development, Ministry of Education, Beijing, China
- *Correspondence: Rui-Xin Liu, ; Xue-Lin Li,
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8
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Wang S, Lin Z, Zhang B, Du J, Li W, Wang Z. Data fusion of electronic noses and electronic tongues aids in botanical origin identification on imbalanced Codonopsis Radix samples. Sci Rep 2022; 12:19120. [PMID: 36352023 PMCID: PMC9646742 DOI: 10.1038/s41598-022-23857-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/07/2022] [Indexed: 11/10/2022] Open
Abstract
Codonopsis Radix (CR) is an edible food and traditional Chinese herb medicine in China. Various varieties of Codonopsis Radix have different tastes. To make the flavor of processed food stable, two kinds of electronic sensory devices, electronic nose and electronic tongue, were used to establish a discrimination model to identify the botanical origin of each sample. The optimal model built on the 88 batches of samples was selected from the models trained with all combination of two pretreatment methods and three classification methods. A comparison were performed on the models trained on the data collected by electronic nose and electronic tongue. The results showed that the model trained on the fused dataset outperformed the models trained separately on the electronic nose data and electronic tongue data. The two preprocessing approaches could improve the prediction performance of all classification methods. Classification and Regression Tree approach performed better than Partial Least Square Discriminant Analysis and Linear Discriminant Analysis in terms of accuracy. But Classification and Regression Tree tends to assign the samples of minority class to the majority class. Meanwhile, Partial Least Square Discriminant Analysis keeps a good balance between the identification requirements of all the two groups of samples. Taking all the results above, the model built using the Partial Least Square Discriminant Analysis method on the fused data after z-score was used to identify the botanical origin of Codonopsis Radix.
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Affiliation(s)
- Shuying Wang
- Beijing Zhongyan Tongrentang Medicine R&D Co.Ltd, Beijing, 100079 People’s Republic of China
| | - Zhaozhou Lin
- Beijing Zhongyan Tongrentang Medicine R&D Co.Ltd, Beijing, 100079 People’s Republic of China
| | - Bei Zhang
- Beijing Zhongyan Tongrentang Medicine R&D Co.Ltd, Beijing, 100079 People’s Republic of China
| | - Jing Du
- Beijing Zhongyan Tongrentang Medicine R&D Co.Ltd, Beijing, 100079 People’s Republic of China
| | - Wen Li
- grid.32566.340000 0000 8571 0482School of Pharmacy, Lanzhou University, Lanzhou, Gansu 730000 People’s Republic of China
| | - Zhibin Wang
- Beijing Zhongyan Tongrentang Medicine R&D Co.Ltd, Beijing, 100079 People’s Republic of China
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9
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An YL, Wei WL, Guo DA. Application of Analytical Technologies in the Discrimination and Authentication of Herbs from Fritillaria: A Review. Crit Rev Anal Chem 2022; 54:1775-1796. [PMID: 36227577 DOI: 10.1080/10408347.2022.2132374] [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] [Indexed: 10/17/2022]
Abstract
Medicinal plants of Fritillaria are widely distributed in numerous countries around the world and possess excellent antitussive and expectorant effects. In particular, Fritillariae Bulbus (FB) as a precious traditional medicine has thousands of years of medical history in China. Herbs of Fritillaria have a high market value and demand while limited by harsh growing circumstances and scarce wild resources. As a consequence, fraudulent behaviors are regularly engaged by the unscrupulous merchants in an attempt to reap greater profits. It is of an urgent need to evaluate the quality of Fritillaria herbs and their products using various analytical instruments and techniques. This review has scrutinized approximately 160 articles from 1995 to 2022 published on the investigation of Fritillaria herbs and related herbal products. The botanical classification of genus Fritillaria, types of counterfeits, technologies applied for differentiating Fritillaria species were comprehensively summarized and discussed in the current review. Molecular and chromatographic identification were the dominant technologies in the authentication of Fritillaria herbs. Additionally, we brought some potential and promising technologies and analytical strategies into attention, which are worthy attempting in the future researches. This review could conduce to excellent reference value for further investigations of the authenticity assessment of Fritillaria species.
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Affiliation(s)
- Ya-Ling An
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wen-Long Wei
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - De-An Guo
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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10
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Pagnin L, Calvini R, Sterflinger K, Izzo FC. Data Fusion Approach to Simultaneously Evaluate the Degradation Process Caused by Ozone and Humidity on Modern Paint Materials. Polymers (Basel) 2022; 14:1787. [PMID: 35566956 PMCID: PMC9100644 DOI: 10.3390/polym14091787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 02/05/2023] Open
Abstract
The knowledge of the atmospheric degradation reactions affecting the stability of modern materials is still of current interest. In fact, environmental parameters, such as relative humidity (RH), temperature, and pollutant agents, often fluctuate due to natural or anthropogenic climatic changes. This study focuses on evaluating analytical and statistical strategies to investigate the degradation processes of acrylic and styrene-acrylic paints after exposure to ozone (O3) and RH. A first comparison of FTIR and Py-GC/MS results allowed to obtain qualitative information on the degradation products and the influence of the pigments on the paints' stability. The combination of these results represents a significant potential for the use of data fusion methods. Specifically, the datasets obtained by FTIR and Py-GC/MS were combined using a low-level data fusion approach and subsequently processed by principal component analysis (PCA). It allowed to evaluate the different chemical impact of the variables for the characterization of unaged and aged samples, understanding which paint is more prone to ozone degradation, and which aging variables most compromise their stability. The advantage of this method consists in simultaneously evaluating all the FTIR and Py-GC/MS variables and describing common degradation patterns. From these combined results, specific information was obtained for further suitable conservation practices for modern and contemporary painted films.
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Affiliation(s)
- Laura Pagnin
- Institute of Science and Technology in Art, Academy of Fine Arts Vienna, Schillerplatz 3, 1010 Vienna, Austria;
| | - Rosalba Calvini
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy;
| | - Katja Sterflinger
- Institute of Science and Technology in Art, Academy of Fine Arts Vienna, Schillerplatz 3, 1010 Vienna, Austria;
| | - Francesca Caterina Izzo
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Via Torino 155/b, 30174 Venice, Italy;
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11
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Ming-Liang G, Yi Z, Fang-Fang C, Hang-Hang W, Ling-Run L, Xin J, Ya-Nan Z, Tian-Shu W, Pei-Dong C, Wei-Feng Y, Bei-Hua B, Li Z. A gradient-based discriminant analysis method for process quality control of carbonized TCM via Fourier transform near infrared spectroscopy: A case study on carbonized Typhae Pollen. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 265:120363. [PMID: 34562862 DOI: 10.1016/j.saa.2021.120363] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 08/28/2021] [Accepted: 08/31/2021] [Indexed: 06/13/2023]
Abstract
Carbonized traditional Chinese medicine (TCM) is a kind of distinctive traditional drug which has been widely used in various bleeding syndromes for over two thousand years, and most of them are still in clinical use. Although they share similar processing method: stir-frying, there are no specific quality standards and few quality control researches carried out on carbonized TCM up until now. Carbonized Typhae Pollen (CTP) is a typical carbonized TCM with efficacy of eliminating blood stasis and stanching bleeding. In this study, a novel process quality control model coupled with near infrared spectroscopy was established, called Gradient-based Discriminant Analysis method (GDA). Compared with conventional modeling methods (Convolutional Neural Network, Linear Discriminant Analysis, Standard Normal Variate-LDA), GDA model applied in fiber optic probe acquisition mode exhibited highest test accuracy (0.961), satisfactory correct identification (internal validation, 100%; external validation, 97.1%) and excellent model stability. This method provided a perfect guideline for process quality control of Carbonized TCM as well as ensured their clinical efficacy.
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Affiliation(s)
- Gao Ming-Liang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Zhang Yi
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Cheng Fang-Fang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Wang Hang-Hang
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Liu Ling-Run
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Jin Xin
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Zhou Ya-Nan
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Wang Tian-Shu
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, PR China.
| | - Chen Pei-Dong
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Yao Wei-Feng
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
| | - Bao Bei-Hua
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China.
| | - Zhang Li
- Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, PR China
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12
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Azcarate SM, Ríos-Reina R, Amigo JM, Goicoechea HC. Data handling in data fusion: Methodologies and applications. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116355] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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13
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Metabolomics-based Comparative Analysis of the Effects of Host and Environment on Viscum coloratum Metabolites and Antioxidative Activities. J Pharm Anal 2021; 12:243-252. [PMID: 35582400 PMCID: PMC9091737 DOI: 10.1016/j.jpha.2021.04.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 04/10/2021] [Accepted: 04/11/2021] [Indexed: 12/24/2022] Open
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14
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Fraga-Corral M, Carpena M, Garcia-Oliveira P, Pereira AG, Prieto MA, Simal-Gandara J. Analytical Metabolomics and Applications in Health, Environmental and Food Science. Crit Rev Anal Chem 2020; 52:712-734. [DOI: 10.1080/10408347.2020.1823811] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- M. Fraga-Corral
- Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Bragança, Portugal
| | - M. Carpena
- Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
| | - P. Garcia-Oliveira
- Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Bragança, Portugal
| | - A. G. Pereira
- Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Bragança, Portugal
| | - M. A. Prieto
- Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
| | - J. Simal-Gandara
- Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense, Spain
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15
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Kang C, Lv C, Yang J, Kang L, Ma W, Zhang W, Wang S, Wang T, Sun J, Ge Y, Huang LQ, Guo L. A Practical Protocol for a Comprehensive Evaluation of Sulfur Fumigation of Trichosanthis Radix Based on Both Non-Targeted and Widely Targeted Metabolomics. FRONTIERS IN PLANT SCIENCE 2020; 11:578086. [PMID: 33042192 PMCID: PMC7527402 DOI: 10.3389/fpls.2020.578086] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 09/02/2020] [Indexed: 05/15/2023]
Abstract
Trichosanthis Radix (TR) is one of the most severely sulfur-fumigated herbs in the market, whose transformation mechanism of chemical compositions and sulfur-fumigation markers of TR have not been clarified. To excavate characteristic sulfur-fumigation markers of TR samples, this study brings up a practical protocol using both ultra-performance liquid chromatography/quadrupole time-of-flight-mass spectrum (UPLC-ESI-QTOF-MS/MS)-based non-targeted metabolomics and ultra-performance liquid chromatography/electrospray ionization/quadrupole multiple-stage linear ion-trap mass spectrum (UPLC-ESI-QTRAP-MS/MS)-based widely targeted metabolomics. The results of study demonstrated that five characteristic markers are sulfur-containing components, which were identified as p-Hydroxybenzyl hydrogen sulfite, cucurbitacin D sulfite I, cucurbitacin D sulfite II, cucurbitacin B sulfite I, and cucurbitacin B sulfite II, respectively. Additionally, cucurbitacin B and D were also filtered and identified as the characteristic sulfur-fumigation markers. Meanwhile, the different sulfur-fumigation extent of TR samples was tested by chemical transformations analysis and sulfur dioxide residues test. Further, 58.16% (139 of 239) of the differential metabolites content significantly reduced in sulfur-fumigated TR samples. Besides, 20 kinds of non-sulfur marker metabolites were tested to evaluate the quality of TR samples before and after sulfur fumigation, predominantly including phenolic acids, amino acids, lipids and nucleotides. Taking TR as an example, this work provides a comprehensive practical protocol for the quality supervision of sulfur-fumigation herbs.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Lu-Qi Huang
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, State Key Laboratory Breeding Base of Dao-di Herbs, Beijing, China
| | - Lanping Guo
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, State Key Laboratory Breeding Base of Dao-di Herbs, Beijing, China
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16
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Lin L, Xu M, Ma L, Zeng J, Zhang F, Qiao Y, Wu Z. A rapid analysis method of safflower (Carthamus tinctorius L.) using combination of computer vision and near-infrared. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 236:118360. [PMID: 32330825 DOI: 10.1016/j.saa.2020.118360] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/08/2020] [Accepted: 04/09/2020] [Indexed: 06/11/2023]
Abstract
The quality of safflower (Carthamus tinctorius L.) in the market is uneven due to the problems of dyeing and adulteration of safflower, and there is no perfect standard for the classification of quality grade of safflower at present. In this study, computer vision (CV) and near-infrared (NIR) were combined to realize the rapid and nondestructive analysis of safflower. First, the partial least squares discrimination analysis (PLS-DA) model was used to qualitatively identify the dyed safflower from 150 samples. Then the partial least squares (PLS) model was used for quantitative analysis of the hydroxy safflower yellow pigment A (HSYA) and water extract of undyed safflower. Herein, the discrimination rate of PLS-DA model reached 100%, and the residual predictive deviation (RPD) values of the PLS models for HSYA and water extract were 2.5046 and 5.6195, respectively. It indicated that the rapid analysis method combining CV and NIR was reliable, and its results can provide important reference for the formulation of safflower quality classification standards in the market.
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Affiliation(s)
- Ling Lin
- Beijing University of Chinese Medicine, Beijing 100102, China
| | - Manfei Xu
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Lijuan Ma
- Beijing University of Chinese Medicine, Beijing 100102, China; Pharmaceutical Engineering and New Drug Development of TCM of Ministry of Education, Beijing 100102, China
| | - Jingqi Zeng
- Fujian University of Traditional Chinese Medicine, College of Pharmacy, Fuzhou 350122, Fujian, China
| | - Fangyu Zhang
- Beijing University of Chinese Medicine, Beijing 100102, China
| | - Yanjiang Qiao
- Beijing University of Chinese Medicine, Beijing 100102, China; Pharmaceutical Engineering and New Drug Development of TCM of Ministry of Education, Beijing 100102, China
| | - Zhisheng Wu
- Beijing University of Chinese Medicine, Beijing 100102, China; Pharmaceutical Engineering and New Drug Development of TCM of Ministry of Education, Beijing 100102, China.
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17
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Stuart KA, Welsh K, Walker MC, Edrada-Ebel R. Metabolomic tools used in marine natural product drug discovery. Expert Opin Drug Discov 2020; 15:499-522. [PMID: 32026730 DOI: 10.1080/17460441.2020.1722636] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: The marine environment is a very promising resource for natural product research, with many of these reaching the market as new drugs, especially in the field of cancer therapy as well as the drug discovery pipeline for new antimicrobials. Exploitation for bioactive marine compounds with unique structures and novel bioactivity such as the isoquinoline alkaloid; trabectedin, the polyether macrolide; halichondrin B, and the peptide; dolastatin 10, requires the use of analytical techniques, which can generate unbiased, quantitative, and qualitative data to benefit the biodiscovery process. Metabolomics has shown to bridge this understanding and facilitate the development of new potential drugs from marine sources and particularly their microbial symbionts.Areas covered: In this review, articles on applied secondary metabolomics ranging from 1990-2018 as well as to the last quarter of 2019 were probed to investigate the impact of metabolomics on drug discovery for new antibiotics and cancer treatment.Expert opinion: The current literature review highlighted the effectiveness of metabolomics in the study of targeting biologically active secondary metabolites from marine sources for optimized discovery of potential new natural products to be made accessible to a R&D pipeline.
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Affiliation(s)
- Kevin Andrew Stuart
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Keira Welsh
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Molly Clare Walker
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - RuAngelie Edrada-Ebel
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
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18
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Shengyun D, Yuqi W, Fei W, Xiaodan M, Jiayu Z. A proposed protocol based on integrative metabonomics analysis for the rapid detection and mechanistic understanding of sulfur fumigation of Chinese herbal medicines. RSC Adv 2019; 9:31150-31161. [PMID: 35529375 PMCID: PMC9072333 DOI: 10.1039/c9ra05032a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 09/12/2019] [Indexed: 01/24/2023] Open
Abstract
In the current work, Lonicera japonica Flos (FLJ) was selected as a model Chinese herbal medicine (CHM) and a protocol was proposed for the rapid detection of sulfur-fumigated (SF) CHMs. A multiple metabonomics analysis was conducted using HPLC, NIR spectroscopy and a UHPLC-LTQ-Orbitrap mass spectrometer. First, the group discriminatory potential of each technique was respectively investigated based on PCA. Then, the effect of mid-level metabonomics data fusion on sample spatial distribution was evaluated based on data obtained using the above three technologies. Furthermore, based on the acquired HRMS data, 76 markers discriminating SF from non-sulfur-fumigated (NSF) CHMs were observed and 49 of them were eventually characterized. Moreover, NIR absorptions of 18 sulfur-containing markers were identified to be in close correlation with the discriminatory NIR wavebands. In conclusion, the proposed protocol based on integrative metabonomics analysis that we established for the rapid detection and mechanistic explanation of the sulfur fumigation of CHMs was able to achieve variable selection, enhance group separation and reveal the intrinsic mechanism of the sulfur fumigation of CHMs.
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Affiliation(s)
- Dai Shengyun
- School of Chinese Pharmacy, Beijing University of Chinese Medicine Beijing 102488 China
- National Institute of Food and Drug Control Beijing 100050 China
| | - Wang Yuqi
- School of Chinese Pharmacy, Beijing University of Chinese Medicine Beijing 102488 China
| | - Wang Fei
- School of Chinese Pharmacy, Beijing University of Chinese Medicine Beijing 102488 China
- Department of Pharmacy, People Hospital of Peking University Beijing 100044 China
| | - Mei Xiaodan
- School of Chinese Pharmacy, Beijing University of Chinese Medicine Beijing 102488 China
| | - Zhang Jiayu
- Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine Beijing 100029 China
- School of Pharmacy, Binzhou Medical University Yantai 264003 China
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19
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Wang Y, Mei X, Liu Z, Li J, Zhang X, Lang S, Dai L, Zhang J. Drug Metabolite Cluster-Based Data-Mining Method for Comprehensive Metabolism Study of 5-hydroxy-6,7,3',4'-tetramethoxyflavone in Rats. Molecules 2019; 24:molecules24183278. [PMID: 31505804 PMCID: PMC6767304 DOI: 10.3390/molecules24183278] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 08/28/2019] [Accepted: 09/03/2019] [Indexed: 11/16/2022] Open
Abstract
The screening of drug metabolites in biological matrixes and structural characterization based on product ion spectra is among the most important, but also the most challenging due to the significant interferences from endogenous species. Traditionally, metabolite detection is accomplished primarily on the basis of predicted molecular masses or fragmentation patterns of prototype drug metabolites using ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). Although classical techniques are well-suited for achieving the partial characterization of prototype drug metabolites, there is a pressing need for a strategy to enable comprehensive drug metabolism depiction. Therefore, we present drug metabolite clusters (DMCs), different from, but complementary to, traditional approaches for mining the information regarding drugs and their metabolites on the basis of raw, processed, or identified tandem mass spectrometry (MS/MS) data. In this paper, we describe a DMC-based data-mining method for the metabolite identification of 5-hydroxy-6,7,3′,4′-tetramethoxyflavone (HTF), a typical hydroxylated-polymethoxyflavonoid (OH-PMF), which addressed the challenge of creating a thorough metabolic profile. Consequently, eight primary metabolism clusters, sixteen secondary metabolism clusters, and five tertiary metabolism clusters were proposed and 106 metabolites (19 potential metabolites included) were detected and identified positively and tentatively. These metabolites were presumed to generate through oxidation (mono-oxidation, di-oxidation), methylation, demethylation, methoxylation, glucuronidation, sulfation, ring cleavage, and their composite reactions. In conclusion, our study expounded drug metabolites in rats and provided a reference for further research on therapeutic material basis and the mechanism of drugs.
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Affiliation(s)
- Yuqi Wang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100029, China.
| | - Xiaodan Mei
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100029, China.
| | - Zihan Liu
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100029, China.
| | - Jie Li
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100029, China.
| | - Xiaoxin Zhang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100029, China.
| | - Shuang Lang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100029, China.
| | - Long Dai
- School of Pharmacy, BIN ZHOU Medical University, Yantai 260040, China.
| | - Jiayu Zhang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100029, China.
- School of Pharmacy, BIN ZHOU Medical University, Yantai 260040, China.
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20
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Wu XM, Zhang QZ, Wang YZ. Traceability the provenience of cultivated Paris polyphylla Smith var. yunnanensis using ATR-FTIR spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 212:132-145. [PMID: 30639599 DOI: 10.1016/j.saa.2019.01.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 12/19/2018] [Accepted: 01/02/2019] [Indexed: 05/20/2023]
Abstract
The conventional procedures, based on attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR), have been developed for the origins traceability of cultivated Paris polyphylla Smith var. yunnanensis (PPY) samples with the help of partial least square discriminant analysis (PLS-DA) and random forest. In this study, a set of 219 batch cultivated PPY samples, containing the cultivation years of 5, 6 and 7, and covering the municipal districts of Chuxiong, Dali, Honghe, Lijiang and Yuxi in Yunnan Province, China, were used to build the discrimination models. Firstly, a visualized analysis was carried out by t-distributed stochastic neighbor embedding (t-SNE) to reduce each data point in a two-dimensional map and make a knowledge of the sample distribution tendency. Secondly, the single spectra data sets of Paridis rhizome and leaf tissues, and the combination of these two data sets with variable selection (mid-level data fusion strategy), were used to establish PLS-DA and random forest models, and parallelly compared the model performance. Results demonstrated that the discrimination ability of PLS-DA preceded the random forest model, and the classification performance was remarkably improved after mid-level data fusion. These results verified each other by 5-, 6- and 7-year old Paridis samples and indicated that the model performance established in the present study was reliable. Besides, five agronomic characters, including the plant height, dry weight of rhizome and leaf tissues, and the allocation of rhizome and leaf were determined and analyzed, results of which indicated that the dry weight and their allocation was significantly different among various origins and fluctuated with the cultivation years. This study was using a comprehensive and green analytical method to discriminate the cultivated Paridis according to their provenances, which was simultaneously benefited for the appropriate cultivation areas selection based on the dry weight of rhizome tissues.
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Affiliation(s)
- Xue-Mei Wu
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, China
| | - Qing-Zhi Zhang
- College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, China
| | - Yuan-Zhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
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