Estimation of the percentage of transgenic Bt maize in maize flour mixtures using perfusion and monolithic reversed-phase high-performance liquid chromatography and chemometric tools
The estimation of the percentage of transgenic Bt maize in maize flour mixtures has been achieved in this work by high-performance liquid chromatography using perfusion and monolithic columns and chemometric analysis. Principal component analysis allowed a preliminary study of the data structure. Th...
Gespeichert in:
| Veröffentlicht in: | Food chemistry 2008-11, Vol.111 (2), p.483-489 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 489 |
|---|---|
| container_issue | 2 |
| container_start_page | 483 |
| container_title | Food chemistry |
| container_volume | 111 |
| creator | Rodríguez-Nogales, J.M. Cifuentes, A. García, M.C. Marina, M.L. |
| description | The estimation of the percentage of transgenic Bt maize in maize flour mixtures has been achieved in this work by high-performance liquid chromatography using perfusion and monolithic columns and chemometric analysis. Principal component analysis allowed a preliminary study of the data structure. Then, linear discriminant analysis was used to develop decision rules to classify samples in the established categories (percentages of transgenic Bt maize). Finally, linear regression (LR) and multivariate regression models (namely, principal component analysis regression (PCR), partial least squares regression (PLS-1), and multiple linear regression (MLR)) were assayed for the prediction of the percentages of transgenic Bt maize present in a maize flour mixture. Using the relative areas of the protein peaks, MLR provided the best models and was able to predict the percentage of transgenic Bt maize in flour mixtures with an error of ±5.3%, ±2.3%, and ±3.8% in the predictions of Aristis Bt, DKC6575, and PR33P67, respectively. |
| doi_str_mv | 10.1016/j.foodchem.2008.03.079 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_19552907</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0308814608004123</els_id><sourcerecordid>19552907</sourcerecordid><originalsourceid>FETCH-LOGICAL-c453t-1af8d3ab7b3fd810ba865f224b593c43406d65356828ae4e0618de0beddedaa63</originalsourceid><addsrcrecordid>eNqFks9u1DAQxiMEokvhFYovIC5ZxnHiODegKn-kShygZ8uxJ4lXSby1nYryYDwfzu4WbnDyyPp938z4c5ZdUNhSoPztbts5Z_SA07YAEFtgW6ibR9mGiprlNdTF42wDDEQuaMnPsmch7ACgACqeZmcFh7Iuq3KT_boK0U4qWjcT15E4INmj1zhH1ePhxqs59DhbTT5EMin7E4mdT0U3usWTyf6Ii8dAlmDnftV3qUqGajZkcrMbbRyS3uMd-oAm3w8qIBlsP-Qr7PykZo1ktLeLNUQP3qWJXO_Vfrg_mKx7ugmjTy7RuTE8z550agz44nSeZzcfr75ffs6vv376cvn-OtdlxWJOVScMU23dss4ICq0SvOqKomyrhumSlcANr1jFRSEUlgicCoPQojFolOLsPHt99N17d7tgiHKyQeM4qhndEiRtqqpooE7gm3-DXPCmEal9QvkR1d6F4LGTe58i8PeSglzDlTv5EK5cw5XAZAo3CS9OPZZ2QvNH9pBmAl6dABW0GrsUnbbhLwcVlNVh2JdHrlNOqt4n5uZb-hoMoIGmLNa93x0JTI97Z9HLoC2mkIz1qKM0zv5v2t_IydT0</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1686998865</pqid></control><display><type>article</type><title>Estimation of the percentage of transgenic Bt maize in maize flour mixtures using perfusion and monolithic reversed-phase high-performance liquid chromatography and chemometric tools</title><source>Elsevier ScienceDirect Journals</source><creator>Rodríguez-Nogales, J.M. ; Cifuentes, A. ; García, M.C. ; Marina, M.L.</creator><creatorcontrib>Rodríguez-Nogales, J.M. ; Cifuentes, A. ; García, M.C. ; Marina, M.L.</creatorcontrib><description>The estimation of the percentage of transgenic Bt maize in maize flour mixtures has been achieved in this work by high-performance liquid chromatography using perfusion and monolithic columns and chemometric analysis. Principal component analysis allowed a preliminary study of the data structure. Then, linear discriminant analysis was used to develop decision rules to classify samples in the established categories (percentages of transgenic Bt maize). Finally, linear regression (LR) and multivariate regression models (namely, principal component analysis regression (PCR), partial least squares regression (PLS-1), and multiple linear regression (MLR)) were assayed for the prediction of the percentages of transgenic Bt maize present in a maize flour mixture. Using the relative areas of the protein peaks, MLR provided the best models and was able to predict the percentage of transgenic Bt maize in flour mixtures with an error of ±5.3%, ±2.3%, and ±3.8% in the predictions of Aristis Bt, DKC6575, and PR33P67, respectively.</description><identifier>ISSN: 0308-8146</identifier><identifier>EISSN: 1873-7072</identifier><identifier>DOI: 10.1016/j.foodchem.2008.03.079</identifier><identifier>PMID: 26047454</identifier><identifier>CODEN: FOCHDJ</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Bacillus thuringiensis ; Biological and medical sciences ; blended foods ; Cereal and baking product industries ; Chemometric analysis ; chemometric tools ; corn ; corn flour ; cultivars ; estimation ; food analysis ; food composition ; Food industries ; Fundamental and applied biological sciences. Psychology ; genetically modified foods ; Maize proteins ; monolithic reversed-phase high-performance liquid chromatography ; Monolithic RP-HPLC ; perfusion ; Perfusion RP-HPLC ; reversed-phase high performance liquid chromatography ; Transgenic Bt maize ; transgenic plants ; Zea mays</subject><ispartof>Food chemistry, 2008-11, Vol.111 (2), p.483-489</ispartof><rights>2008 Elsevier Ltd</rights><rights>2008 INIST-CNRS</rights><rights>Copyright © 2008 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-1af8d3ab7b3fd810ba865f224b593c43406d65356828ae4e0618de0beddedaa63</citedby><cites>FETCH-LOGICAL-c453t-1af8d3ab7b3fd810ba865f224b593c43406d65356828ae4e0618de0beddedaa63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0308814608004123$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20504507$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26047454$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rodríguez-Nogales, J.M.</creatorcontrib><creatorcontrib>Cifuentes, A.</creatorcontrib><creatorcontrib>García, M.C.</creatorcontrib><creatorcontrib>Marina, M.L.</creatorcontrib><title>Estimation of the percentage of transgenic Bt maize in maize flour mixtures using perfusion and monolithic reversed-phase high-performance liquid chromatography and chemometric tools</title><title>Food chemistry</title><addtitle>Food Chem</addtitle><description>The estimation of the percentage of transgenic Bt maize in maize flour mixtures has been achieved in this work by high-performance liquid chromatography using perfusion and monolithic columns and chemometric analysis. Principal component analysis allowed a preliminary study of the data structure. Then, linear discriminant analysis was used to develop decision rules to classify samples in the established categories (percentages of transgenic Bt maize). Finally, linear regression (LR) and multivariate regression models (namely, principal component analysis regression (PCR), partial least squares regression (PLS-1), and multiple linear regression (MLR)) were assayed for the prediction of the percentages of transgenic Bt maize present in a maize flour mixture. Using the relative areas of the protein peaks, MLR provided the best models and was able to predict the percentage of transgenic Bt maize in flour mixtures with an error of ±5.3%, ±2.3%, and ±3.8% in the predictions of Aristis Bt, DKC6575, and PR33P67, respectively.</description><subject>Bacillus thuringiensis</subject><subject>Biological and medical sciences</subject><subject>blended foods</subject><subject>Cereal and baking product industries</subject><subject>Chemometric analysis</subject><subject>chemometric tools</subject><subject>corn</subject><subject>corn flour</subject><subject>cultivars</subject><subject>estimation</subject><subject>food analysis</subject><subject>food composition</subject><subject>Food industries</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>genetically modified foods</subject><subject>Maize proteins</subject><subject>monolithic reversed-phase high-performance liquid chromatography</subject><subject>Monolithic RP-HPLC</subject><subject>perfusion</subject><subject>Perfusion RP-HPLC</subject><subject>reversed-phase high performance liquid chromatography</subject><subject>Transgenic Bt maize</subject><subject>transgenic plants</subject><subject>Zea mays</subject><issn>0308-8146</issn><issn>1873-7072</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNqFks9u1DAQxiMEokvhFYovIC5ZxnHiODegKn-kShygZ8uxJ4lXSby1nYryYDwfzu4WbnDyyPp938z4c5ZdUNhSoPztbts5Z_SA07YAEFtgW6ibR9mGiprlNdTF42wDDEQuaMnPsmch7ACgACqeZmcFh7Iuq3KT_boK0U4qWjcT15E4INmj1zhH1ePhxqs59DhbTT5EMin7E4mdT0U3usWTyf6Ii8dAlmDnftV3qUqGajZkcrMbbRyS3uMd-oAm3w8qIBlsP-Qr7PykZo1ktLeLNUQP3qWJXO_Vfrg_mKx7ugmjTy7RuTE8z550agz44nSeZzcfr75ffs6vv376cvn-OtdlxWJOVScMU23dss4ICq0SvOqKomyrhumSlcANr1jFRSEUlgicCoPQojFolOLsPHt99N17d7tgiHKyQeM4qhndEiRtqqpooE7gm3-DXPCmEal9QvkR1d6F4LGTe58i8PeSglzDlTv5EK5cw5XAZAo3CS9OPZZ2QvNH9pBmAl6dABW0GrsUnbbhLwcVlNVh2JdHrlNOqt4n5uZb-hoMoIGmLNa93x0JTI97Z9HLoC2mkIz1qKM0zv5v2t_IydT0</recordid><startdate>20081115</startdate><enddate>20081115</enddate><creator>Rodríguez-Nogales, J.M.</creator><creator>Cifuentes, A.</creator><creator>García, M.C.</creator><creator>Marina, M.L.</creator><general>Elsevier Ltd</general><general>[Amsterdam]: Elsevier Science</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope></search><sort><creationdate>20081115</creationdate><title>Estimation of the percentage of transgenic Bt maize in maize flour mixtures using perfusion and monolithic reversed-phase high-performance liquid chromatography and chemometric tools</title><author>Rodríguez-Nogales, J.M. ; Cifuentes, A. ; García, M.C. ; Marina, M.L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c453t-1af8d3ab7b3fd810ba865f224b593c43406d65356828ae4e0618de0beddedaa63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Bacillus thuringiensis</topic><topic>Biological and medical sciences</topic><topic>blended foods</topic><topic>Cereal and baking product industries</topic><topic>Chemometric analysis</topic><topic>chemometric tools</topic><topic>corn</topic><topic>corn flour</topic><topic>cultivars</topic><topic>estimation</topic><topic>food analysis</topic><topic>food composition</topic><topic>Food industries</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>genetically modified foods</topic><topic>Maize proteins</topic><topic>monolithic reversed-phase high-performance liquid chromatography</topic><topic>Monolithic RP-HPLC</topic><topic>perfusion</topic><topic>Perfusion RP-HPLC</topic><topic>reversed-phase high performance liquid chromatography</topic><topic>Transgenic Bt maize</topic><topic>transgenic plants</topic><topic>Zea mays</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rodríguez-Nogales, J.M.</creatorcontrib><creatorcontrib>Cifuentes, A.</creatorcontrib><creatorcontrib>García, M.C.</creatorcontrib><creatorcontrib>Marina, M.L.</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><jtitle>Food chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rodríguez-Nogales, J.M.</au><au>Cifuentes, A.</au><au>García, M.C.</au><au>Marina, M.L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of the percentage of transgenic Bt maize in maize flour mixtures using perfusion and monolithic reversed-phase high-performance liquid chromatography and chemometric tools</atitle><jtitle>Food chemistry</jtitle><addtitle>Food Chem</addtitle><date>2008-11-15</date><risdate>2008</risdate><volume>111</volume><issue>2</issue><spage>483</spage><epage>489</epage><pages>483-489</pages><issn>0308-8146</issn><eissn>1873-7072</eissn><coden>FOCHDJ</coden><abstract>The estimation of the percentage of transgenic Bt maize in maize flour mixtures has been achieved in this work by high-performance liquid chromatography using perfusion and monolithic columns and chemometric analysis. Principal component analysis allowed a preliminary study of the data structure. Then, linear discriminant analysis was used to develop decision rules to classify samples in the established categories (percentages of transgenic Bt maize). Finally, linear regression (LR) and multivariate regression models (namely, principal component analysis regression (PCR), partial least squares regression (PLS-1), and multiple linear regression (MLR)) were assayed for the prediction of the percentages of transgenic Bt maize present in a maize flour mixture. Using the relative areas of the protein peaks, MLR provided the best models and was able to predict the percentage of transgenic Bt maize in flour mixtures with an error of ±5.3%, ±2.3%, and ±3.8% in the predictions of Aristis Bt, DKC6575, and PR33P67, respectively.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><pmid>26047454</pmid><doi>10.1016/j.foodchem.2008.03.079</doi><tpages>7</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0308-8146 |
| ispartof | Food chemistry, 2008-11, Vol.111 (2), p.483-489 |
| issn | 0308-8146 1873-7072 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_19552907 |
| source | Elsevier ScienceDirect Journals |
| subjects | Bacillus thuringiensis Biological and medical sciences blended foods Cereal and baking product industries Chemometric analysis chemometric tools corn corn flour cultivars estimation food analysis food composition Food industries Fundamental and applied biological sciences. Psychology genetically modified foods Maize proteins monolithic reversed-phase high-performance liquid chromatography Monolithic RP-HPLC perfusion Perfusion RP-HPLC reversed-phase high performance liquid chromatography Transgenic Bt maize transgenic plants Zea mays |
| title | Estimation of the percentage of transgenic Bt maize in maize flour mixtures using perfusion and monolithic reversed-phase high-performance liquid chromatography and chemometric tools |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-16T08%3A45%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Estimation%20of%20the%20percentage%20of%20transgenic%20Bt%20maize%20in%20maize%20flour%20mixtures%20using%20perfusion%20and%20monolithic%20reversed-phase%20high-performance%20liquid%20chromatography%20and%20chemometric%20tools&rft.jtitle=Food%20chemistry&rft.au=Rodr%C3%ADguez-Nogales,%20J.M.&rft.date=2008-11-15&rft.volume=111&rft.issue=2&rft.spage=483&rft.epage=489&rft.pages=483-489&rft.issn=0308-8146&rft.eissn=1873-7072&rft.coden=FOCHDJ&rft_id=info:doi/10.1016/j.foodchem.2008.03.079&rft_dat=%3Cproquest_cross%3E19552907%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1686998865&rft_id=info:pmid/26047454&rft_els_id=S0308814608004123&rfr_iscdi=true |