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Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography-mass spectrum
Herbal quality is strongly influenced by harvest time. It is therefore one of crucial factors that should be well respected by herbal producers when optimizing cultivation techniques, so that to obtain herbal products of high quality. In this work, we paid attention on one of common used Chinese her...
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Published in: | Chinese medicine 2017-05, Vol.12 (1), p.12-12, Article 12 |
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description | Herbal quality is strongly influenced by harvest time. It is therefore one of crucial factors that should be well respected by herbal producers when optimizing cultivation techniques, so that to obtain herbal products of high quality. In this work, we paid attention on one of common used Chinese herbals,
Kuan. According to previous studies, its quality may be related with growth years because of the variation of several main bioactive components in different growth years. However, information about the whole chemical composition is still scarce, which may jointly determine the herbal quality.
samples were collected in 1-4 growth years after sowing. To obtain a global insight on chemical profile of herbs, we applied a metabolomics approach based on gas chromatography-mass spectrum. Analysis of variance, principal component analysis, partial least squares discriminant analysis and hierarchical cluster analysis were combined to explore the significant difference in different growth years.
166 metabolites were identified by using gas chromatography-mass spectrum method. 63 metabolites showed significant change in different growth years in terms of analysis of variance. Those metabolites then were grouped into 4 classes by hierarchical cluster analysis, characterizing the samples of different growth ages. Samples harvested in the earliest years (1-2) were obviously differ with the latest years (3-4) as reported by principal component analysis. Further, partial least squares discriminant analysis revealed the detail difference in each growth year. Gluconic acid, xylitol, glutaric acid, pipecolinic acid, ribonic acid, mannose, oxalic acid, digalacturonic acid, lactic acid, 2-deoxyerythritol, acetol, 3-hydroxybutyric acid, citramalic acid,
-carbamylglutamate, and cellobiose are the main 15 discrimination metabolites between different growth years.
Harvest time should be well considered when producing
In order to boost the consistency of herbal quality,
is recommended to harvest in 4th growth year. The method of GC-MS combined with multivariate analysis was a powerful tool to evaluate the herbal quality. |
doi_str_mv | 10.1186/s13020-017-0133-1 |
format | article |
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Kuan. According to previous studies, its quality may be related with growth years because of the variation of several main bioactive components in different growth years. However, information about the whole chemical composition is still scarce, which may jointly determine the herbal quality.
samples were collected in 1-4 growth years after sowing. To obtain a global insight on chemical profile of herbs, we applied a metabolomics approach based on gas chromatography-mass spectrum. Analysis of variance, principal component analysis, partial least squares discriminant analysis and hierarchical cluster analysis were combined to explore the significant difference in different growth years.
166 metabolites were identified by using gas chromatography-mass spectrum method. 63 metabolites showed significant change in different growth years in terms of analysis of variance. Those metabolites then were grouped into 4 classes by hierarchical cluster analysis, characterizing the samples of different growth ages. Samples harvested in the earliest years (1-2) were obviously differ with the latest years (3-4) as reported by principal component analysis. Further, partial least squares discriminant analysis revealed the detail difference in each growth year. Gluconic acid, xylitol, glutaric acid, pipecolinic acid, ribonic acid, mannose, oxalic acid, digalacturonic acid, lactic acid, 2-deoxyerythritol, acetol, 3-hydroxybutyric acid, citramalic acid,
-carbamylglutamate, and cellobiose are the main 15 discrimination metabolites between different growth years.
Harvest time should be well considered when producing
In order to boost the consistency of herbal quality,
is recommended to harvest in 4th growth year. The method of GC-MS combined with multivariate analysis was a powerful tool to evaluate the herbal quality.</description><identifier>ISSN: 1749-8546</identifier><identifier>EISSN: 1749-8546</identifier><identifier>DOI: 10.1186/s13020-017-0133-1</identifier><identifier>PMID: 28469699</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>Arthritis ; Cellobiose ; Chemical composition ; Chinese medicine ; Chromatography ; Cultivation techniques ; Cyathula officinalis ; Discriminant analysis ; Fatty acids ; Gas chromatography ; GC–MS ; Gluconic acid ; Harvest ; Harvest time ; Herbal medicine ; Herbs ; Lactic acid ; Least squares method ; Mannose ; Mass spectrometry ; Metabolism ; Metabolites ; Metabolomics ; Multivariate analysis ; Oxalic acid ; PCA ; PLS-DA ; Principal components analysis ; Quality control ; Quality standards ; Scientific imaging ; Variance ; Variance analysis ; Xylitol</subject><ispartof>Chinese medicine, 2017-05, Vol.12 (1), p.12-12, Article 12</ispartof><rights>Copyright BioMed Central 2017</rights><rights>The Author(s) 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c493t-f04b3045bfd968efe30098dd1bba18aefe11023cfb444492ae90be38eea735c13</citedby><cites>FETCH-LOGICAL-c493t-f04b3045bfd968efe30098dd1bba18aefe11023cfb444492ae90be38eea735c13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414129/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1894714316?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28469699$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tong, Kai</creatorcontrib><creatorcontrib>Li, Zhao-Ling</creatorcontrib><creatorcontrib>Sun, Xu</creatorcontrib><creatorcontrib>Yan, Shen</creatorcontrib><creatorcontrib>Jiang, Mei-Jie</creatorcontrib><creatorcontrib>Deng, Meng-Sheng</creatorcontrib><creatorcontrib>Chen, Ji</creatorcontrib><creatorcontrib>Li, Jing-Wei</creatorcontrib><creatorcontrib>Tian, Meng-Liang</creatorcontrib><title>Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography-mass spectrum</title><title>Chinese medicine</title><addtitle>Chin Med</addtitle><description>Herbal quality is strongly influenced by harvest time. It is therefore one of crucial factors that should be well respected by herbal producers when optimizing cultivation techniques, so that to obtain herbal products of high quality. In this work, we paid attention on one of common used Chinese herbals,
Kuan. According to previous studies, its quality may be related with growth years because of the variation of several main bioactive components in different growth years. However, information about the whole chemical composition is still scarce, which may jointly determine the herbal quality.
samples were collected in 1-4 growth years after sowing. To obtain a global insight on chemical profile of herbs, we applied a metabolomics approach based on gas chromatography-mass spectrum. Analysis of variance, principal component analysis, partial least squares discriminant analysis and hierarchical cluster analysis were combined to explore the significant difference in different growth years.
166 metabolites were identified by using gas chromatography-mass spectrum method. 63 metabolites showed significant change in different growth years in terms of analysis of variance. Those metabolites then were grouped into 4 classes by hierarchical cluster analysis, characterizing the samples of different growth ages. Samples harvested in the earliest years (1-2) were obviously differ with the latest years (3-4) as reported by principal component analysis. Further, partial least squares discriminant analysis revealed the detail difference in each growth year. Gluconic acid, xylitol, glutaric acid, pipecolinic acid, ribonic acid, mannose, oxalic acid, digalacturonic acid, lactic acid, 2-deoxyerythritol, acetol, 3-hydroxybutyric acid, citramalic acid,
-carbamylglutamate, and cellobiose are the main 15 discrimination metabolites between different growth years.
Harvest time should be well considered when producing
In order to boost the consistency of herbal quality,
is recommended to harvest in 4th growth year. The method of GC-MS combined with multivariate analysis was a powerful tool to evaluate the herbal quality.</description><subject>Arthritis</subject><subject>Cellobiose</subject><subject>Chemical composition</subject><subject>Chinese medicine</subject><subject>Chromatography</subject><subject>Cultivation techniques</subject><subject>Cyathula officinalis</subject><subject>Discriminant analysis</subject><subject>Fatty acids</subject><subject>Gas chromatography</subject><subject>GC–MS</subject><subject>Gluconic acid</subject><subject>Harvest</subject><subject>Harvest time</subject><subject>Herbal medicine</subject><subject>Herbs</subject><subject>Lactic acid</subject><subject>Least squares method</subject><subject>Mannose</subject><subject>Mass spectrometry</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolomics</subject><subject>Multivariate analysis</subject><subject>Oxalic acid</subject><subject>PCA</subject><subject>PLS-DA</subject><subject>Principal components analysis</subject><subject>Quality control</subject><subject>Quality standards</subject><subject>Scientific imaging</subject><subject>Variance</subject><subject>Variance analysis</subject><subject>Xylitol</subject><issn>1749-8546</issn><issn>1749-8546</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdktGO1CAUhonRuOvoA3hjSLzxpgqFtnBjYiarblzjjV6TAz2dYdKWCu0k8wY-tsx23exKQjj5-fiBk5-Q15y951zVHxIXrGQF402eQhT8CbnkjdSFqmT99EF9QV6kdGCsEpVQz8lFqWSta60vyZ_vOIMNfRi8SxSmKQZwexrxiNBnYRwX6OmwQt7RI0QPsw8j9SONIcyJho5uTzDvlx5y3XnnR-h9ot8WGKmFhC3N-A4SdfsYBpjDLsK0PxUDpETThG6Oy_CSPOvyjfjqbt2QX5-vfm6_Fjc_vlxvP90UTmoxFx2TVjBZ2a7VtcIOBWNatS23FriCLHDOSuE6K_PQJaBmFoVChEZUjosNuV592wAHM0U_QDyZAN7cCiHuDMTZux5NqVpkZdtWApgEC0qgbdpK1410oHMzN-Tj6jUtdsDW4ThH6B-ZPt4Z_d7swtFUkkte6mzw7s4ght8LptkMPjnsexgxLMlwpauyEXUtM_r2P_QQlpg7fUvJhkvB60zxlXIxpBSxu38MZ-acGbNmxuTMmHNmzLkjbx7-4v7Ev5CIv_qCwNw</recordid><startdate>20170503</startdate><enddate>20170503</enddate><creator>Tong, Kai</creator><creator>Li, Zhao-Ling</creator><creator>Sun, Xu</creator><creator>Yan, Shen</creator><creator>Jiang, Mei-Jie</creator><creator>Deng, Meng-Sheng</creator><creator>Chen, Ji</creator><creator>Li, Jing-Wei</creator><creator>Tian, Meng-Liang</creator><general>BioMed Central</general><general>BMC</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7T5</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20170503</creationdate><title>Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography-mass spectrum</title><author>Tong, Kai ; Li, Zhao-Ling ; Sun, Xu ; Yan, Shen ; Jiang, Mei-Jie ; Deng, Meng-Sheng ; Chen, Ji ; Li, Jing-Wei ; Tian, Meng-Liang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c493t-f04b3045bfd968efe30098dd1bba18aefe11023cfb444492ae90be38eea735c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Arthritis</topic><topic>Cellobiose</topic><topic>Chemical composition</topic><topic>Chinese medicine</topic><topic>Chromatography</topic><topic>Cultivation techniques</topic><topic>Cyathula officinalis</topic><topic>Discriminant analysis</topic><topic>Fatty acids</topic><topic>Gas chromatography</topic><topic>GC–MS</topic><topic>Gluconic acid</topic><topic>Harvest</topic><topic>Harvest time</topic><topic>Herbal medicine</topic><topic>Herbs</topic><topic>Lactic acid</topic><topic>Least squares method</topic><topic>Mannose</topic><topic>Mass spectrometry</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Metabolomics</topic><topic>Multivariate analysis</topic><topic>Oxalic acid</topic><topic>PCA</topic><topic>PLS-DA</topic><topic>Principal components analysis</topic><topic>Quality control</topic><topic>Quality standards</topic><topic>Scientific imaging</topic><topic>Variance</topic><topic>Variance analysis</topic><topic>Xylitol</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tong, Kai</creatorcontrib><creatorcontrib>Li, Zhao-Ling</creatorcontrib><creatorcontrib>Sun, Xu</creatorcontrib><creatorcontrib>Yan, Shen</creatorcontrib><creatorcontrib>Jiang, Mei-Jie</creatorcontrib><creatorcontrib>Deng, Meng-Sheng</creatorcontrib><creatorcontrib>Chen, Ji</creatorcontrib><creatorcontrib>Li, Jing-Wei</creatorcontrib><creatorcontrib>Tian, Meng-Liang</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Immunology Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest Publicly Available Content database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Chinese medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tong, Kai</au><au>Li, Zhao-Ling</au><au>Sun, Xu</au><au>Yan, Shen</au><au>Jiang, Mei-Jie</au><au>Deng, Meng-Sheng</au><au>Chen, Ji</au><au>Li, Jing-Wei</au><au>Tian, Meng-Liang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography-mass spectrum</atitle><jtitle>Chinese medicine</jtitle><addtitle>Chin Med</addtitle><date>2017-05-03</date><risdate>2017</risdate><volume>12</volume><issue>1</issue><spage>12</spage><epage>12</epage><pages>12-12</pages><artnum>12</artnum><issn>1749-8546</issn><eissn>1749-8546</eissn><abstract>Herbal quality is strongly influenced by harvest time. It is therefore one of crucial factors that should be well respected by herbal producers when optimizing cultivation techniques, so that to obtain herbal products of high quality. In this work, we paid attention on one of common used Chinese herbals,
Kuan. According to previous studies, its quality may be related with growth years because of the variation of several main bioactive components in different growth years. However, information about the whole chemical composition is still scarce, which may jointly determine the herbal quality.
samples were collected in 1-4 growth years after sowing. To obtain a global insight on chemical profile of herbs, we applied a metabolomics approach based on gas chromatography-mass spectrum. Analysis of variance, principal component analysis, partial least squares discriminant analysis and hierarchical cluster analysis were combined to explore the significant difference in different growth years.
166 metabolites were identified by using gas chromatography-mass spectrum method. 63 metabolites showed significant change in different growth years in terms of analysis of variance. Those metabolites then were grouped into 4 classes by hierarchical cluster analysis, characterizing the samples of different growth ages. Samples harvested in the earliest years (1-2) were obviously differ with the latest years (3-4) as reported by principal component analysis. Further, partial least squares discriminant analysis revealed the detail difference in each growth year. Gluconic acid, xylitol, glutaric acid, pipecolinic acid, ribonic acid, mannose, oxalic acid, digalacturonic acid, lactic acid, 2-deoxyerythritol, acetol, 3-hydroxybutyric acid, citramalic acid,
-carbamylglutamate, and cellobiose are the main 15 discrimination metabolites between different growth years.
Harvest time should be well considered when producing
In order to boost the consistency of herbal quality,
is recommended to harvest in 4th growth year. The method of GC-MS combined with multivariate analysis was a powerful tool to evaluate the herbal quality.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>28469699</pmid><doi>10.1186/s13020-017-0133-1</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Arthritis Cellobiose Chemical composition Chinese medicine Chromatography Cultivation techniques Cyathula officinalis Discriminant analysis Fatty acids Gas chromatography GC–MS Gluconic acid Harvest Harvest time Herbal medicine Herbs Lactic acid Least squares method Mannose Mass spectrometry Metabolism Metabolites Metabolomics Multivariate analysis Oxalic acid PCA PLS-DA Principal components analysis Quality control Quality standards Scientific imaging Variance Variance analysis Xylitol |
title | Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography-mass spectrum |
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