A novel integrated automatic strategy for amino acid composition analysis of seeds from 67 species
[Display omitted] •A novel integrated automatic identification strategy was established via Python.•117 amino acid derivatives were tentatively characterized from 67 species of seeds.•Amino acid profiles of gymnosperm and angiosperm seeds have been extensively analyzed.•The seeds from Fabaceae and R...
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| Veröffentlicht in: | Food chemistry 2023-11, Vol.426, p.136670-136670, Article 136670 |
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| container_title | Food chemistry |
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| creator | Ding, Yelin Bi, Qirui Huang, Dongdong Liao, Jingmei Yang, Lin Luo, Xiaoxiao Yang, Peilei Li, Yun Yao, Changliang Wei, Wenlong Zhang, Jianqing Li, Jiayuan Huang, Yong Guo, De-an |
| description | [Display omitted]
•A novel integrated automatic identification strategy was established via Python.•117 amino acid derivatives were tentatively characterized from 67 species of seeds.•Amino acid profiles of gymnosperm and angiosperm seeds have been extensively analyzed.•The seeds from Fabaceae and Rosaceae familes differed in amino acid profiles.
The composition and quantity of amino acids (AAs) in seeds are complicated due to the various origins and modifications of different species. In this study, a novel automatic neutral loss filtering (ANLF) strategy based on accurate mass searching by Python was developed to analyze the free and hydrolyzed AA-phenyl isothiocyanate (PITC) derivatives from seeds of Gymnosperm and Angiosperm phyla. Compared with traditional strategies, ANLF showed much higher accuracy in screening AA derivatives by filtering nitrogen-containing non-AA compounds and efficiency in processing large datasets. Meanwhile, the content phenotype of 20 proteinogenic AAs from seeds of these two families was characterized by a 35-min HPLC method combined with an automated peak-matching strategy. AA profiles of 232 batches of seeds from 67 species, consisting of 19 proteinogenic AAs, 21 modified AAs, and 77 unknown AAs, would be a good reference for their application in food and medicine. |
| doi_str_mv | 10.1016/j.foodchem.2023.136670 |
| format | Article |
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•A novel integrated automatic identification strategy was established via Python.•117 amino acid derivatives were tentatively characterized from 67 species of seeds.•Amino acid profiles of gymnosperm and angiosperm seeds have been extensively analyzed.•The seeds from Fabaceae and Rosaceae familes differed in amino acid profiles.
The composition and quantity of amino acids (AAs) in seeds are complicated due to the various origins and modifications of different species. In this study, a novel automatic neutral loss filtering (ANLF) strategy based on accurate mass searching by Python was developed to analyze the free and hydrolyzed AA-phenyl isothiocyanate (PITC) derivatives from seeds of Gymnosperm and Angiosperm phyla. Compared with traditional strategies, ANLF showed much higher accuracy in screening AA derivatives by filtering nitrogen-containing non-AA compounds and efficiency in processing large datasets. Meanwhile, the content phenotype of 20 proteinogenic AAs from seeds of these two families was characterized by a 35-min HPLC method combined with an automated peak-matching strategy. AA profiles of 232 batches of seeds from 67 species, consisting of 19 proteinogenic AAs, 21 modified AAs, and 77 unknown AAs, would be a good reference for their application in food and medicine.</description><identifier>ISSN: 0308-8146</identifier><identifier>EISSN: 1873-7072</identifier><identifier>DOI: 10.1016/j.foodchem.2023.136670</identifier><identifier>PMID: 37354578</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>amino acid composition ; Amino acids ; Amino Acids - analysis ; Angiospermae ; Automatic neutral loss filtering ; automation ; Chromatography, High Pressure Liquid ; Cycadopsida - chemistry ; data collection ; food chemistry ; Gymnospermae ; isothiocyanates ; Magnoliopsida - chemistry ; Mass Spectrometry ; medicine ; phenotype ; Phenyl isothiocyanate ; Phylogeny ; Python ; Seeds ; Seeds - chemistry</subject><ispartof>Food chemistry, 2023-11, Vol.426, p.136670-136670, Article 136670</ispartof><rights>2023 Elsevier Ltd</rights><rights>Copyright © 2023 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c348t-8d2db4194a89e04e880294d3f0494195ecd2026ca3e27e7637d7e7186ad827f33</cites><orcidid>0000-0003-0223-9448</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.foodchem.2023.136670$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37354578$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ding, Yelin</creatorcontrib><creatorcontrib>Bi, Qirui</creatorcontrib><creatorcontrib>Huang, Dongdong</creatorcontrib><creatorcontrib>Liao, Jingmei</creatorcontrib><creatorcontrib>Yang, Lin</creatorcontrib><creatorcontrib>Luo, Xiaoxiao</creatorcontrib><creatorcontrib>Yang, Peilei</creatorcontrib><creatorcontrib>Li, Yun</creatorcontrib><creatorcontrib>Yao, Changliang</creatorcontrib><creatorcontrib>Wei, Wenlong</creatorcontrib><creatorcontrib>Zhang, Jianqing</creatorcontrib><creatorcontrib>Li, Jiayuan</creatorcontrib><creatorcontrib>Huang, Yong</creatorcontrib><creatorcontrib>Guo, De-an</creatorcontrib><title>A novel integrated automatic strategy for amino acid composition analysis of seeds from 67 species</title><title>Food chemistry</title><addtitle>Food Chem</addtitle><description>[Display omitted]
•A novel integrated automatic identification strategy was established via Python.•117 amino acid derivatives were tentatively characterized from 67 species of seeds.•Amino acid profiles of gymnosperm and angiosperm seeds have been extensively analyzed.•The seeds from Fabaceae and Rosaceae familes differed in amino acid profiles.
The composition and quantity of amino acids (AAs) in seeds are complicated due to the various origins and modifications of different species. In this study, a novel automatic neutral loss filtering (ANLF) strategy based on accurate mass searching by Python was developed to analyze the free and hydrolyzed AA-phenyl isothiocyanate (PITC) derivatives from seeds of Gymnosperm and Angiosperm phyla. Compared with traditional strategies, ANLF showed much higher accuracy in screening AA derivatives by filtering nitrogen-containing non-AA compounds and efficiency in processing large datasets. Meanwhile, the content phenotype of 20 proteinogenic AAs from seeds of these two families was characterized by a 35-min HPLC method combined with an automated peak-matching strategy. AA profiles of 232 batches of seeds from 67 species, consisting of 19 proteinogenic AAs, 21 modified AAs, and 77 unknown AAs, would be a good reference for their application in food and medicine.</description><subject>amino acid composition</subject><subject>Amino acids</subject><subject>Amino Acids - analysis</subject><subject>Angiospermae</subject><subject>Automatic neutral loss filtering</subject><subject>automation</subject><subject>Chromatography, High Pressure Liquid</subject><subject>Cycadopsida - chemistry</subject><subject>data collection</subject><subject>food chemistry</subject><subject>Gymnospermae</subject><subject>isothiocyanates</subject><subject>Magnoliopsida - chemistry</subject><subject>Mass Spectrometry</subject><subject>medicine</subject><subject>phenotype</subject><subject>Phenyl isothiocyanate</subject><subject>Phylogeny</subject><subject>Python</subject><subject>Seeds</subject><subject>Seeds - chemistry</subject><issn>0308-8146</issn><issn>1873-7072</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc1PGzEQxa2qqATafwH52MsGf8X23opQ-ZCQeilny7FnwdHuOngcpPz3OApwbU8jjX5vnuY9Qi44W3LG9eVmOeQcwzNMS8GEXHKptWFfyIJbIzvDjPhKFkwy21mu9Ck5Q9wwxgTj9hs5lUau1MrYBVlf0Tm_wkjTXOGp-AqR-l3Nk68pUKyHzdOeDrlQP6U5Ux9SpCFP24yppjxTP_txjwlpHigCRKRDyRPVhuIWQgL8Tk4GPyL8eJ_n5PHm99_ru-7hz-399dVDF6SytbNRxLXivfK2B6bAWiZ6FeXAVN_WKwixfaqDlyAMGC1NbINb7aMVZpDynPw83t2W_LIDrG5KGGAc_Qx5h05Y1dteC6P-A23Wkq160VB9REPJiAUGty1p8mXvOHOHKtzGfVThDlW4YxVNePHusVtPED9lH9k34NcRgBbKa4LisMU1B4ipQKgu5vQvjzcgDJ0b</recordid><startdate>20231115</startdate><enddate>20231115</enddate><creator>Ding, Yelin</creator><creator>Bi, Qirui</creator><creator>Huang, Dongdong</creator><creator>Liao, Jingmei</creator><creator>Yang, Lin</creator><creator>Luo, Xiaoxiao</creator><creator>Yang, Peilei</creator><creator>Li, Yun</creator><creator>Yao, Changliang</creator><creator>Wei, Wenlong</creator><creator>Zhang, Jianqing</creator><creator>Li, Jiayuan</creator><creator>Huang, Yong</creator><creator>Guo, De-an</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0003-0223-9448</orcidid></search><sort><creationdate>20231115</creationdate><title>A novel integrated automatic strategy for amino acid composition analysis of seeds from 67 species</title><author>Ding, Yelin ; Bi, Qirui ; Huang, Dongdong ; Liao, Jingmei ; Yang, Lin ; Luo, Xiaoxiao ; Yang, Peilei ; Li, Yun ; Yao, Changliang ; Wei, Wenlong ; Zhang, Jianqing ; Li, Jiayuan ; Huang, Yong ; Guo, De-an</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c348t-8d2db4194a89e04e880294d3f0494195ecd2026ca3e27e7637d7e7186ad827f33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>amino acid composition</topic><topic>Amino acids</topic><topic>Amino Acids - analysis</topic><topic>Angiospermae</topic><topic>Automatic neutral loss filtering</topic><topic>automation</topic><topic>Chromatography, High Pressure Liquid</topic><topic>Cycadopsida - chemistry</topic><topic>data collection</topic><topic>food chemistry</topic><topic>Gymnospermae</topic><topic>isothiocyanates</topic><topic>Magnoliopsida - chemistry</topic><topic>Mass Spectrometry</topic><topic>medicine</topic><topic>phenotype</topic><topic>Phenyl isothiocyanate</topic><topic>Phylogeny</topic><topic>Python</topic><topic>Seeds</topic><topic>Seeds - chemistry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ding, Yelin</creatorcontrib><creatorcontrib>Bi, Qirui</creatorcontrib><creatorcontrib>Huang, Dongdong</creatorcontrib><creatorcontrib>Liao, Jingmei</creatorcontrib><creatorcontrib>Yang, Lin</creatorcontrib><creatorcontrib>Luo, Xiaoxiao</creatorcontrib><creatorcontrib>Yang, Peilei</creatorcontrib><creatorcontrib>Li, Yun</creatorcontrib><creatorcontrib>Yao, Changliang</creatorcontrib><creatorcontrib>Wei, Wenlong</creatorcontrib><creatorcontrib>Zhang, Jianqing</creatorcontrib><creatorcontrib>Li, Jiayuan</creatorcontrib><creatorcontrib>Huang, Yong</creatorcontrib><creatorcontrib>Guo, De-an</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Food chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ding, Yelin</au><au>Bi, Qirui</au><au>Huang, Dongdong</au><au>Liao, Jingmei</au><au>Yang, Lin</au><au>Luo, Xiaoxiao</au><au>Yang, Peilei</au><au>Li, Yun</au><au>Yao, Changliang</au><au>Wei, Wenlong</au><au>Zhang, Jianqing</au><au>Li, Jiayuan</au><au>Huang, Yong</au><au>Guo, De-an</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel integrated automatic strategy for amino acid composition analysis of seeds from 67 species</atitle><jtitle>Food chemistry</jtitle><addtitle>Food Chem</addtitle><date>2023-11-15</date><risdate>2023</risdate><volume>426</volume><spage>136670</spage><epage>136670</epage><pages>136670-136670</pages><artnum>136670</artnum><issn>0308-8146</issn><eissn>1873-7072</eissn><abstract>[Display omitted]
•A novel integrated automatic identification strategy was established via Python.•117 amino acid derivatives were tentatively characterized from 67 species of seeds.•Amino acid profiles of gymnosperm and angiosperm seeds have been extensively analyzed.•The seeds from Fabaceae and Rosaceae familes differed in amino acid profiles.
The composition and quantity of amino acids (AAs) in seeds are complicated due to the various origins and modifications of different species. In this study, a novel automatic neutral loss filtering (ANLF) strategy based on accurate mass searching by Python was developed to analyze the free and hydrolyzed AA-phenyl isothiocyanate (PITC) derivatives from seeds of Gymnosperm and Angiosperm phyla. Compared with traditional strategies, ANLF showed much higher accuracy in screening AA derivatives by filtering nitrogen-containing non-AA compounds and efficiency in processing large datasets. Meanwhile, the content phenotype of 20 proteinogenic AAs from seeds of these two families was characterized by a 35-min HPLC method combined with an automated peak-matching strategy. AA profiles of 232 batches of seeds from 67 species, consisting of 19 proteinogenic AAs, 21 modified AAs, and 77 unknown AAs, would be a good reference for their application in food and medicine.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>37354578</pmid><doi>10.1016/j.foodchem.2023.136670</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-0223-9448</orcidid></addata></record> |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | amino acid composition Amino acids Amino Acids - analysis Angiospermae Automatic neutral loss filtering automation Chromatography, High Pressure Liquid Cycadopsida - chemistry data collection food chemistry Gymnospermae isothiocyanates Magnoliopsida - chemistry Mass Spectrometry medicine phenotype Phenyl isothiocyanate Phylogeny Python Seeds Seeds - chemistry |
| title | A novel integrated automatic strategy for amino acid composition analysis of seeds from 67 species |
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