A perfect tandem: chemometric methods and microfluidic colorimetric twin sensors on paper. Beyond the traditional analytical approach

•Twin-sensors on paper have been produced.•Development of a screening method for the detection of analytes in water.•Partial least squares – discriminant analysis and support vector machine are employed as classification methods.•Quality performance metrics were collected and applied to evaluate of...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Microchemical journal 2020-09, Vol.157, p.104930, Article 104930
Hauptverfasser: Jiménez-Carvelo, Ana M., Salloum-Llergo, Kalim D., Cuadros-Rodríguez, Luis, Capitán-Vallvey, Luis Fermín, Fernández-Ramos, M.D.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page 104930
container_title Microchemical journal
container_volume 157
creator Jiménez-Carvelo, Ana M.
Salloum-Llergo, Kalim D.
Cuadros-Rodríguez, Luis
Capitán-Vallvey, Luis Fermín
Fernández-Ramos, M.D.
description •Twin-sensors on paper have been produced.•Development of a screening method for the detection of analytes in water.•Partial least squares – discriminant analysis and support vector machine are employed as classification methods.•Quality performance metrics were collected and applied to evaluate of the performance of the classifications. Chemometrics has proven to be a powerful tool for processing multivariate analytical data aimed at locating and extracting useful information relating to a particular analyte or material system in a complex sample from non-specific analytical signals that have been previously acquired and recorded by one or more analytical instruments or devices. In this paper, the basis for the application of both classification and quantitation multivariate methods is described, using a colorimetric twin sensor produced on a microfluidic paper-based analytical device (μPAD) as instructive example. The selected twin sensor provides a dual analytical signal for four anions (acetate, cyanide, fluoride and phosphate) in aqueous solution (concentration interval 0–0.1 M) from two reagent dyes (alizarin and 4-nitrophenol), which are immobilised in parallel on the same device containing four microfluidic channels designed in the form of an X. In this way, a data vector is obtained from each test whose elements are the colour coordinates obtained from the four responses, which is then used to build the chemometric models to be applied. Two multivariate classification methods (partial least squares discriminant analysis and support vector machine classification) are explored and the latter makes it possible to detect the presence or absence of each anion in an aqueous solution mixture. Single (each dye dataset separately) and fused (merging the two dye datasets) models were built and a support vector machine was shown to be the best classification method, obtaining sensitivity and precision values of 100% in almost all cases.
doi_str_mv 10.1016/j.microc.2020.104930
format Article
fullrecord <record><control><sourceid>elsevier_webof</sourceid><recordid>TN_cdi_webofscience_primary_000565196100011CitationCount</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0026265X20307116</els_id><sourcerecordid>S0026265X20307116</sourcerecordid><originalsourceid>FETCH-LOGICAL-c269t-3e5a779fe3ac783627064321541c037f56697d3c745801d200f73bb9d257dd923</originalsourceid><addsrcrecordid>eNqNkc1qGzEQgEVoIW6aN8hB97CuflaSlUMgNWkTCOSSQm5ClmaxzHq1SEqCHyDvXW3W9FhymkEz30jzCaELSpaUUPljt9wHl6JbMsKmo1ZzcoIWlGjRaNrqL2hBCJMNk-L5FH3LeUcIUYLRBXq_wSOkDlzBxQ4e9lfYbWEf91BScLiGbfQZ1xL-uKPrX4KvBRf7mMKxq7yFAWcYckwZxwGPts5c4p9wiJUrW8AlWR9KiIPt6yzbH0pwUzqOKVq3_Y6-drbPcH6MZ-jPr9un9V3z8Pj7fn3z0DgmdWk4CKuU7oBbp1ZcMkVkyxkVLXWEq05IqZXnTrViRahnhHSKbzbaM6G814yfoXaeWzfJOUFnxrqETQdDiZlUmp2ZVZpJpZlVVuxyxt5gE7vsAgwO_qHVpZCCaklrRmntXn2-ex2Knbys48tQKno9o1AlvAZI5oj7kOoXGR_D_1_6F85koX4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A perfect tandem: chemometric methods and microfluidic colorimetric twin sensors on paper. Beyond the traditional analytical approach</title><source>ScienceDirect Journals (5 years ago - present)</source><source>Web of Science - Science Citation Index Expanded - 2020&lt;img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /&gt;</source><creator>Jiménez-Carvelo, Ana M. ; Salloum-Llergo, Kalim D. ; Cuadros-Rodríguez, Luis ; Capitán-Vallvey, Luis Fermín ; Fernández-Ramos, M.D.</creator><creatorcontrib>Jiménez-Carvelo, Ana M. ; Salloum-Llergo, Kalim D. ; Cuadros-Rodríguez, Luis ; Capitán-Vallvey, Luis Fermín ; Fernández-Ramos, M.D.</creatorcontrib><description>•Twin-sensors on paper have been produced.•Development of a screening method for the detection of analytes in water.•Partial least squares – discriminant analysis and support vector machine are employed as classification methods.•Quality performance metrics were collected and applied to evaluate of the performance of the classifications. Chemometrics has proven to be a powerful tool for processing multivariate analytical data aimed at locating and extracting useful information relating to a particular analyte or material system in a complex sample from non-specific analytical signals that have been previously acquired and recorded by one or more analytical instruments or devices. In this paper, the basis for the application of both classification and quantitation multivariate methods is described, using a colorimetric twin sensor produced on a microfluidic paper-based analytical device (μPAD) as instructive example. The selected twin sensor provides a dual analytical signal for four anions (acetate, cyanide, fluoride and phosphate) in aqueous solution (concentration interval 0–0.1 M) from two reagent dyes (alizarin and 4-nitrophenol), which are immobilised in parallel on the same device containing four microfluidic channels designed in the form of an X. In this way, a data vector is obtained from each test whose elements are the colour coordinates obtained from the four responses, which is then used to build the chemometric models to be applied. Two multivariate classification methods (partial least squares discriminant analysis and support vector machine classification) are explored and the latter makes it possible to detect the presence or absence of each anion in an aqueous solution mixture. Single (each dye dataset separately) and fused (merging the two dye datasets) models were built and a support vector machine was shown to be the best classification method, obtaining sensitivity and precision values of 100% in almost all cases.</description><identifier>ISSN: 0026-265X</identifier><identifier>EISSN: 1095-9149</identifier><identifier>DOI: 10.1016/j.microc.2020.104930</identifier><language>eng</language><publisher>AMSTERDAM: Elsevier B.V</publisher><subject>Anion test ; Chemistry ; Chemistry, Analytical ; Chemometrics ; Data fusion ; Microfluidic paper-based analytical devices (µPADs) ; Multivariate classification and quantitation methods ; Physical Sciences ; Science &amp; Technology ; Support vector machine</subject><ispartof>Microchemical journal, 2020-09, Vol.157, p.104930, Article 104930</ispartof><rights>2020 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>4</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000565196100011</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c269t-3e5a779fe3ac783627064321541c037f56697d3c745801d200f73bb9d257dd923</citedby><cites>FETCH-LOGICAL-c269t-3e5a779fe3ac783627064321541c037f56697d3c745801d200f73bb9d257dd923</cites><orcidid>0000-0002-9061-1686</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.microc.2020.104930$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,28253,46000</link.rule.ids></links><search><creatorcontrib>Jiménez-Carvelo, Ana M.</creatorcontrib><creatorcontrib>Salloum-Llergo, Kalim D.</creatorcontrib><creatorcontrib>Cuadros-Rodríguez, Luis</creatorcontrib><creatorcontrib>Capitán-Vallvey, Luis Fermín</creatorcontrib><creatorcontrib>Fernández-Ramos, M.D.</creatorcontrib><title>A perfect tandem: chemometric methods and microfluidic colorimetric twin sensors on paper. Beyond the traditional analytical approach</title><title>Microchemical journal</title><addtitle>MICROCHEM J</addtitle><description>•Twin-sensors on paper have been produced.•Development of a screening method for the detection of analytes in water.•Partial least squares – discriminant analysis and support vector machine are employed as classification methods.•Quality performance metrics were collected and applied to evaluate of the performance of the classifications. Chemometrics has proven to be a powerful tool for processing multivariate analytical data aimed at locating and extracting useful information relating to a particular analyte or material system in a complex sample from non-specific analytical signals that have been previously acquired and recorded by one or more analytical instruments or devices. In this paper, the basis for the application of both classification and quantitation multivariate methods is described, using a colorimetric twin sensor produced on a microfluidic paper-based analytical device (μPAD) as instructive example. The selected twin sensor provides a dual analytical signal for four anions (acetate, cyanide, fluoride and phosphate) in aqueous solution (concentration interval 0–0.1 M) from two reagent dyes (alizarin and 4-nitrophenol), which are immobilised in parallel on the same device containing four microfluidic channels designed in the form of an X. In this way, a data vector is obtained from each test whose elements are the colour coordinates obtained from the four responses, which is then used to build the chemometric models to be applied. Two multivariate classification methods (partial least squares discriminant analysis and support vector machine classification) are explored and the latter makes it possible to detect the presence or absence of each anion in an aqueous solution mixture. Single (each dye dataset separately) and fused (merging the two dye datasets) models were built and a support vector machine was shown to be the best classification method, obtaining sensitivity and precision values of 100% in almost all cases.</description><subject>Anion test</subject><subject>Chemistry</subject><subject>Chemistry, Analytical</subject><subject>Chemometrics</subject><subject>Data fusion</subject><subject>Microfluidic paper-based analytical devices (µPADs)</subject><subject>Multivariate classification and quantitation methods</subject><subject>Physical Sciences</subject><subject>Science &amp; Technology</subject><subject>Support vector machine</subject><issn>0026-265X</issn><issn>1095-9149</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><recordid>eNqNkc1qGzEQgEVoIW6aN8hB97CuflaSlUMgNWkTCOSSQm5ClmaxzHq1SEqCHyDvXW3W9FhymkEz30jzCaELSpaUUPljt9wHl6JbMsKmo1ZzcoIWlGjRaNrqL2hBCJMNk-L5FH3LeUcIUYLRBXq_wSOkDlzBxQ4e9lfYbWEf91BScLiGbfQZ1xL-uKPrX4KvBRf7mMKxq7yFAWcYckwZxwGPts5c4p9wiJUrW8AlWR9KiIPt6yzbH0pwUzqOKVq3_Y6-drbPcH6MZ-jPr9un9V3z8Pj7fn3z0DgmdWk4CKuU7oBbp1ZcMkVkyxkVLXWEq05IqZXnTrViRahnhHSKbzbaM6G814yfoXaeWzfJOUFnxrqETQdDiZlUmp2ZVZpJpZlVVuxyxt5gE7vsAgwO_qHVpZCCaklrRmntXn2-ex2Knbys48tQKno9o1AlvAZI5oj7kOoXGR_D_1_6F85koX4</recordid><startdate>202009</startdate><enddate>202009</enddate><creator>Jiménez-Carvelo, Ana M.</creator><creator>Salloum-Llergo, Kalim D.</creator><creator>Cuadros-Rodríguez, Luis</creator><creator>Capitán-Vallvey, Luis Fermín</creator><creator>Fernández-Ramos, M.D.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-9061-1686</orcidid></search><sort><creationdate>202009</creationdate><title>A perfect tandem: chemometric methods and microfluidic colorimetric twin sensors on paper. Beyond the traditional analytical approach</title><author>Jiménez-Carvelo, Ana M. ; Salloum-Llergo, Kalim D. ; Cuadros-Rodríguez, Luis ; Capitán-Vallvey, Luis Fermín ; Fernández-Ramos, M.D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c269t-3e5a779fe3ac783627064321541c037f56697d3c745801d200f73bb9d257dd923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Anion test</topic><topic>Chemistry</topic><topic>Chemistry, Analytical</topic><topic>Chemometrics</topic><topic>Data fusion</topic><topic>Microfluidic paper-based analytical devices (µPADs)</topic><topic>Multivariate classification and quantitation methods</topic><topic>Physical Sciences</topic><topic>Science &amp; Technology</topic><topic>Support vector machine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiménez-Carvelo, Ana M.</creatorcontrib><creatorcontrib>Salloum-Llergo, Kalim D.</creatorcontrib><creatorcontrib>Cuadros-Rodríguez, Luis</creatorcontrib><creatorcontrib>Capitán-Vallvey, Luis Fermín</creatorcontrib><creatorcontrib>Fernández-Ramos, M.D.</creatorcontrib><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>CrossRef</collection><jtitle>Microchemical journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiménez-Carvelo, Ana M.</au><au>Salloum-Llergo, Kalim D.</au><au>Cuadros-Rodríguez, Luis</au><au>Capitán-Vallvey, Luis Fermín</au><au>Fernández-Ramos, M.D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A perfect tandem: chemometric methods and microfluidic colorimetric twin sensors on paper. Beyond the traditional analytical approach</atitle><jtitle>Microchemical journal</jtitle><stitle>MICROCHEM J</stitle><date>2020-09</date><risdate>2020</risdate><volume>157</volume><spage>104930</spage><pages>104930-</pages><artnum>104930</artnum><issn>0026-265X</issn><eissn>1095-9149</eissn><abstract>•Twin-sensors on paper have been produced.•Development of a screening method for the detection of analytes in water.•Partial least squares – discriminant analysis and support vector machine are employed as classification methods.•Quality performance metrics were collected and applied to evaluate of the performance of the classifications. Chemometrics has proven to be a powerful tool for processing multivariate analytical data aimed at locating and extracting useful information relating to a particular analyte or material system in a complex sample from non-specific analytical signals that have been previously acquired and recorded by one or more analytical instruments or devices. In this paper, the basis for the application of both classification and quantitation multivariate methods is described, using a colorimetric twin sensor produced on a microfluidic paper-based analytical device (μPAD) as instructive example. The selected twin sensor provides a dual analytical signal for four anions (acetate, cyanide, fluoride and phosphate) in aqueous solution (concentration interval 0–0.1 M) from two reagent dyes (alizarin and 4-nitrophenol), which are immobilised in parallel on the same device containing four microfluidic channels designed in the form of an X. In this way, a data vector is obtained from each test whose elements are the colour coordinates obtained from the four responses, which is then used to build the chemometric models to be applied. Two multivariate classification methods (partial least squares discriminant analysis and support vector machine classification) are explored and the latter makes it possible to detect the presence or absence of each anion in an aqueous solution mixture. Single (each dye dataset separately) and fused (merging the two dye datasets) models were built and a support vector machine was shown to be the best classification method, obtaining sensitivity and precision values of 100% in almost all cases.</abstract><cop>AMSTERDAM</cop><pub>Elsevier B.V</pub><doi>10.1016/j.microc.2020.104930</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-9061-1686</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0026-265X
ispartof Microchemical journal, 2020-09, Vol.157, p.104930, Article 104930
issn 0026-265X
1095-9149
language eng
recordid cdi_webofscience_primary_000565196100011CitationCount
source ScienceDirect Journals (5 years ago - present); Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />
subjects Anion test
Chemistry
Chemistry, Analytical
Chemometrics
Data fusion
Microfluidic paper-based analytical devices (µPADs)
Multivariate classification and quantitation methods
Physical Sciences
Science & Technology
Support vector machine
title A perfect tandem: chemometric methods and microfluidic colorimetric twin sensors on paper. Beyond the traditional analytical approach
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T14%3A34%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_webof&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20perfect%20tandem:%20chemometric%20methods%20and%20microfluidic%20colorimetric%20twin%20sensors%20on%20paper.%20Beyond%20the%20traditional%20analytical%20approach&rft.jtitle=Microchemical%20journal&rft.au=Jim%C3%A9nez-Carvelo,%20Ana%20M.&rft.date=2020-09&rft.volume=157&rft.spage=104930&rft.pages=104930-&rft.artnum=104930&rft.issn=0026-265X&rft.eissn=1095-9149&rft_id=info:doi/10.1016/j.microc.2020.104930&rft_dat=%3Celsevier_webof%3ES0026265X20307116%3C/elsevier_webof%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_els_id=S0026265X20307116&rfr_iscdi=true