Linking GC-MS and PTR-TOF-MS fingerprints of food samples

Autores
Cappellin, Luca; Aprea, Eugenio; Granitto, Pablo Miguel; Wehrens, Ron; Soukoulis, Christos; Viola, Roberto; Mark, Tilmann D.; Gasperi, Flavia; Biasioli, Franco
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Recently the first applications in food science and technology of the newly available volatile organic compound (VOC) detection technique proton transfer reaction‐mass spectrometry, coupled with a time of flight mass analyzer (PTR-TOF-MS), have been published. In comparison with standard techniques such as GC-MS, PTR-TOF-MS has the remarkable advantage of being extremely fast but has the drawback that compound identification is more challenging and often not possible without further information. In order to better exploit and understand the analytical information entangled in the PTR-TOF-MS fingerprint and to link it with SPME/GC-MS analyses we employed two multivariate calibration methods, PLS and the more recent LASSO. We show that, while in some cases it is sufficient to consider a single PTR-TOF-MS peak in order to predict the intensity of a SPME/GC-MS peak, in general a multivariate approach is needed. We compare the performances of PLS and LASSO in terms of prediction capabilities and interpretability of the model coefficients and conclude that LASSO is more suitable for this problem. As case study, we compared GC and PTR-MS data for different matrices, namely olive oil and grana cheese.
Fil: Cappellin, Luca. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia. Universidad de Innsbruck; Austria
Fil: Aprea, Eugenio. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Wehrens, Ron. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Soukoulis, Christos. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Viola, Roberto. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Mark, Tilmann D.. Universidad de Innsbruck; Austria
Fil: Gasperi, Flavia. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Biasioli, Franco. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Materia
PLS
LASSO
PROTON TRANSFER REACTION-MASS SPECTROMETRY
TIME-OF-FLIGHT
PREDICTION
MULTIVARIATE CORRELATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/104541
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/104541
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Linking GC-MS and PTR-TOF-MS fingerprints of food samplesCappellin, LucaAprea, EugenioGranitto, Pablo MiguelWehrens, RonSoukoulis, ChristosViola, RobertoMark, Tilmann D.Gasperi, FlaviaBiasioli, FrancoPLSLASSOPROTON TRANSFER REACTION-MASS SPECTROMETRYTIME-OF-FLIGHTPREDICTIONMULTIVARIATE CORRELATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Recently the first applications in food science and technology of the newly available volatile organic compound (VOC) detection technique proton transfer reaction‐mass spectrometry, coupled with a time of flight mass analyzer (PTR-TOF-MS), have been published. In comparison with standard techniques such as GC-MS, PTR-TOF-MS has the remarkable advantage of being extremely fast but has the drawback that compound identification is more challenging and often not possible without further information. In order to better exploit and understand the analytical information entangled in the PTR-TOF-MS fingerprint and to link it with SPME/GC-MS analyses we employed two multivariate calibration methods, PLS and the more recent LASSO. We show that, while in some cases it is sufficient to consider a single PTR-TOF-MS peak in order to predict the intensity of a SPME/GC-MS peak, in general a multivariate approach is needed. We compare the performances of PLS and LASSO in terms of prediction capabilities and interpretability of the model coefficients and conclude that LASSO is more suitable for this problem. As case study, we compared GC and PTR-MS data for different matrices, namely olive oil and grana cheese.Fil: Cappellin, Luca. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia. Universidad de Innsbruck; AustriaFil: Aprea, Eugenio. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; ItaliaFil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Wehrens, Ron. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; ItaliaFil: Soukoulis, Christos. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; ItaliaFil: Viola, Roberto. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; ItaliaFil: Mark, Tilmann D.. Universidad de Innsbruck; AustriaFil: Gasperi, Flavia. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; ItaliaFil: Biasioli, Franco. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; ItaliaElsevier Science2012-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/104541Cappellin, Luca; Aprea, Eugenio; Granitto, Pablo Miguel; Wehrens, Ron; Soukoulis, Christos; et al.; Linking GC-MS and PTR-TOF-MS fingerprints of food samples; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 118; 5-2012; 301-3070169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2012.05.008info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0169743912001219info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:58:10Zoai:ri.conicet.gov.ar:11336/104541instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:58:11.029CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Linking GC-MS and PTR-TOF-MS fingerprints of food samples
title Linking GC-MS and PTR-TOF-MS fingerprints of food samples
spellingShingle Linking GC-MS and PTR-TOF-MS fingerprints of food samples
Cappellin, Luca
PLS
LASSO
PROTON TRANSFER REACTION-MASS SPECTROMETRY
TIME-OF-FLIGHT
PREDICTION
MULTIVARIATE CORRELATION
title_short Linking GC-MS and PTR-TOF-MS fingerprints of food samples
title_full Linking GC-MS and PTR-TOF-MS fingerprints of food samples
title_fullStr Linking GC-MS and PTR-TOF-MS fingerprints of food samples
title_full_unstemmed Linking GC-MS and PTR-TOF-MS fingerprints of food samples
title_sort Linking GC-MS and PTR-TOF-MS fingerprints of food samples
dc.creator.none.fl_str_mv Cappellin, Luca
Aprea, Eugenio
Granitto, Pablo Miguel
Wehrens, Ron
Soukoulis, Christos
Viola, Roberto
Mark, Tilmann D.
Gasperi, Flavia
Biasioli, Franco
author Cappellin, Luca
author_facet Cappellin, Luca
Aprea, Eugenio
Granitto, Pablo Miguel
Wehrens, Ron
Soukoulis, Christos
Viola, Roberto
Mark, Tilmann D.
Gasperi, Flavia
Biasioli, Franco
author_role author
author2 Aprea, Eugenio
Granitto, Pablo Miguel
Wehrens, Ron
Soukoulis, Christos
Viola, Roberto
Mark, Tilmann D.
Gasperi, Flavia
Biasioli, Franco
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv PLS
LASSO
PROTON TRANSFER REACTION-MASS SPECTROMETRY
TIME-OF-FLIGHT
PREDICTION
MULTIVARIATE CORRELATION
topic PLS
LASSO
PROTON TRANSFER REACTION-MASS SPECTROMETRY
TIME-OF-FLIGHT
PREDICTION
MULTIVARIATE CORRELATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Recently the first applications in food science and technology of the newly available volatile organic compound (VOC) detection technique proton transfer reaction‐mass spectrometry, coupled with a time of flight mass analyzer (PTR-TOF-MS), have been published. In comparison with standard techniques such as GC-MS, PTR-TOF-MS has the remarkable advantage of being extremely fast but has the drawback that compound identification is more challenging and often not possible without further information. In order to better exploit and understand the analytical information entangled in the PTR-TOF-MS fingerprint and to link it with SPME/GC-MS analyses we employed two multivariate calibration methods, PLS and the more recent LASSO. We show that, while in some cases it is sufficient to consider a single PTR-TOF-MS peak in order to predict the intensity of a SPME/GC-MS peak, in general a multivariate approach is needed. We compare the performances of PLS and LASSO in terms of prediction capabilities and interpretability of the model coefficients and conclude that LASSO is more suitable for this problem. As case study, we compared GC and PTR-MS data for different matrices, namely olive oil and grana cheese.
Fil: Cappellin, Luca. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia. Universidad de Innsbruck; Austria
Fil: Aprea, Eugenio. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Wehrens, Ron. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Soukoulis, Christos. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Viola, Roberto. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Mark, Tilmann D.. Universidad de Innsbruck; Austria
Fil: Gasperi, Flavia. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
Fil: Biasioli, Franco. Instituto Agrario San Michele all'Adige Fondazione Edmund Mach; Italia
description Recently the first applications in food science and technology of the newly available volatile organic compound (VOC) detection technique proton transfer reaction‐mass spectrometry, coupled with a time of flight mass analyzer (PTR-TOF-MS), have been published. In comparison with standard techniques such as GC-MS, PTR-TOF-MS has the remarkable advantage of being extremely fast but has the drawback that compound identification is more challenging and often not possible without further information. In order to better exploit and understand the analytical information entangled in the PTR-TOF-MS fingerprint and to link it with SPME/GC-MS analyses we employed two multivariate calibration methods, PLS and the more recent LASSO. We show that, while in some cases it is sufficient to consider a single PTR-TOF-MS peak in order to predict the intensity of a SPME/GC-MS peak, in general a multivariate approach is needed. We compare the performances of PLS and LASSO in terms of prediction capabilities and interpretability of the model coefficients and conclude that LASSO is more suitable for this problem. As case study, we compared GC and PTR-MS data for different matrices, namely olive oil and grana cheese.
publishDate 2012
dc.date.none.fl_str_mv 2012-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/104541
Cappellin, Luca; Aprea, Eugenio; Granitto, Pablo Miguel; Wehrens, Ron; Soukoulis, Christos; et al.; Linking GC-MS and PTR-TOF-MS fingerprints of food samples; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 118; 5-2012; 301-307
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/104541
identifier_str_mv Cappellin, Luca; Aprea, Eugenio; Granitto, Pablo Miguel; Wehrens, Ron; Soukoulis, Christos; et al.; Linking GC-MS and PTR-TOF-MS fingerprints of food samples; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 118; 5-2012; 301-307
0169-7439
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2012.05.008
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0169743912001219
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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