Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS

Autores
Cappellin, Luca; Aprea, Eugenio; Granitto, Pablo Miguel; Romano, Andrea; Gasperi, Flavia; Biasioli, Franco
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Multiclass sample classification and marker selection are cutting-edge problems in metabolomics. In the present study we address the classification of 14 raspberry cultivars having different levels of gray mold (Botrytis cinerea) susceptibility. We characterized raspberry cultivars by two headspace analysis methods, namely solid-phase microextraction/gas chromatography-mass spectrometry (SPME/GC-MS) and proton transfer reaction-mass spectrometry (PTR-MS). Given the high number of classes, advanced data mining methods are necessary. Random Forest (RF), Penalized Discriminant Analysis (PDA), Discriminant Partial Least Squares (dPLS) and Support Vector Machine (SVM) have been employed for cultivar classification and Random Forest-Recursive Feature Elimination (RF-RFE) has been used to perform feature selection. In particular the most important GC-MS and PTR-MS variables related to gray mold susceptibility of the selected raspberry cultivars have been investigated. Moving from GC-MS profiling to the more rapid and less invasive PTR-MS fingerprinting leads to a cultivar characterization which is still related to the corresponding Botrytis susceptibility level and therefore marker identification is still possible.
Fil: Cappellin, Luca. Fondazione Edmund Mach. Research and Innovation Centre; Italia
Fil: Aprea, Eugenio. Fondazione Edmund Mach. Research and Innovation Centre; Italia
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Romano, Andrea. Fondazione Edmund Mach. Research and Innovation Centre; Italia
Fil: Gasperi, Flavia. Fondazione Edmund Mach. Research and Innovation Centre; Italia
Fil: Biasioli, Franco. Fondazione Edmund Mach. Research and Innovation Centre; Italia
Materia
Proton Transfer Reaction Mass Spectrometry
Raspberries
Cultivars
Chemometrics
Data Mining
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:11336/3180
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MSCappellin, LucaAprea, EugenioGranitto, Pablo MiguelRomano, AndreaGasperi, FlaviaBiasioli, FrancoProton Transfer Reaction Mass SpectrometryRaspberriesCultivarsChemometricsData Mininghttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Multiclass sample classification and marker selection are cutting-edge problems in metabolomics. In the present study we address the classification of 14 raspberry cultivars having different levels of gray mold (Botrytis cinerea) susceptibility. We characterized raspberry cultivars by two headspace analysis methods, namely solid-phase microextraction/gas chromatography-mass spectrometry (SPME/GC-MS) and proton transfer reaction-mass spectrometry (PTR-MS). Given the high number of classes, advanced data mining methods are necessary. Random Forest (RF), Penalized Discriminant Analysis (PDA), Discriminant Partial Least Squares (dPLS) and Support Vector Machine (SVM) have been employed for cultivar classification and Random Forest-Recursive Feature Elimination (RF-RFE) has been used to perform feature selection. In particular the most important GC-MS and PTR-MS variables related to gray mold susceptibility of the selected raspberry cultivars have been investigated. Moving from GC-MS profiling to the more rapid and less invasive PTR-MS fingerprinting leads to a cultivar characterization which is still related to the corresponding Botrytis susceptibility level and therefore marker identification is still possible.Fil: Cappellin, Luca. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaFil: Aprea, Eugenio. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaFil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Romano, Andrea. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaFil: Gasperi, Flavia. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaFil: Biasioli, Franco. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaElsevier2013-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/3180Cappellin, Luca; Aprea, Eugenio; Granitto, Pablo Miguel; Romano, Andrea; Gasperi, Flavia; et al.; Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS; Elsevier; Food Research International; 54; 1; 11-2013; 1313-13200963-9969enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0963996913000975info:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodres.2013.02.004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:05:13Zoai:ri.conicet.gov.ar:11336/3180instacron: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 10:05:13.393CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS
title Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS
spellingShingle Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS
Cappellin, Luca
Proton Transfer Reaction Mass Spectrometry
Raspberries
Cultivars
Chemometrics
Data Mining
title_short Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS
title_full Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS
title_fullStr Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS
title_full_unstemmed Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS
title_sort Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS
dc.creator.none.fl_str_mv Cappellin, Luca
Aprea, Eugenio
Granitto, Pablo Miguel
Romano, Andrea
Gasperi, Flavia
Biasioli, Franco
author Cappellin, Luca
author_facet Cappellin, Luca
Aprea, Eugenio
Granitto, Pablo Miguel
Romano, Andrea
Gasperi, Flavia
Biasioli, Franco
author_role author
author2 Aprea, Eugenio
Granitto, Pablo Miguel
Romano, Andrea
Gasperi, Flavia
Biasioli, Franco
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Proton Transfer Reaction Mass Spectrometry
Raspberries
Cultivars
Chemometrics
Data Mining
topic Proton Transfer Reaction Mass Spectrometry
Raspberries
Cultivars
Chemometrics
Data Mining
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Multiclass sample classification and marker selection are cutting-edge problems in metabolomics. In the present study we address the classification of 14 raspberry cultivars having different levels of gray mold (Botrytis cinerea) susceptibility. We characterized raspberry cultivars by two headspace analysis methods, namely solid-phase microextraction/gas chromatography-mass spectrometry (SPME/GC-MS) and proton transfer reaction-mass spectrometry (PTR-MS). Given the high number of classes, advanced data mining methods are necessary. Random Forest (RF), Penalized Discriminant Analysis (PDA), Discriminant Partial Least Squares (dPLS) and Support Vector Machine (SVM) have been employed for cultivar classification and Random Forest-Recursive Feature Elimination (RF-RFE) has been used to perform feature selection. In particular the most important GC-MS and PTR-MS variables related to gray mold susceptibility of the selected raspberry cultivars have been investigated. Moving from GC-MS profiling to the more rapid and less invasive PTR-MS fingerprinting leads to a cultivar characterization which is still related to the corresponding Botrytis susceptibility level and therefore marker identification is still possible.
Fil: Cappellin, Luca. Fondazione Edmund Mach. Research and Innovation Centre; Italia
Fil: Aprea, Eugenio. Fondazione Edmund Mach. Research and Innovation Centre; Italia
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Romano, Andrea. Fondazione Edmund Mach. Research and Innovation Centre; Italia
Fil: Gasperi, Flavia. Fondazione Edmund Mach. Research and Innovation Centre; Italia
Fil: Biasioli, Franco. Fondazione Edmund Mach. Research and Innovation Centre; Italia
description Multiclass sample classification and marker selection are cutting-edge problems in metabolomics. In the present study we address the classification of 14 raspberry cultivars having different levels of gray mold (Botrytis cinerea) susceptibility. We characterized raspberry cultivars by two headspace analysis methods, namely solid-phase microextraction/gas chromatography-mass spectrometry (SPME/GC-MS) and proton transfer reaction-mass spectrometry (PTR-MS). Given the high number of classes, advanced data mining methods are necessary. Random Forest (RF), Penalized Discriminant Analysis (PDA), Discriminant Partial Least Squares (dPLS) and Support Vector Machine (SVM) have been employed for cultivar classification and Random Forest-Recursive Feature Elimination (RF-RFE) has been used to perform feature selection. In particular the most important GC-MS and PTR-MS variables related to gray mold susceptibility of the selected raspberry cultivars have been investigated. Moving from GC-MS profiling to the more rapid and less invasive PTR-MS fingerprinting leads to a cultivar characterization which is still related to the corresponding Botrytis susceptibility level and therefore marker identification is still possible.
publishDate 2013
dc.date.none.fl_str_mv 2013-11
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/11336/3180
Cappellin, Luca; Aprea, Eugenio; Granitto, Pablo Miguel; Romano, Andrea; Gasperi, Flavia; et al.; Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS; Elsevier; Food Research International; 54; 1; 11-2013; 1313-1320
0963-9969
url http://hdl.handle.net/11336/3180
identifier_str_mv Cappellin, Luca; Aprea, Eugenio; Granitto, Pablo Miguel; Romano, Andrea; Gasperi, Flavia; et al.; Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC-MS and PTR-MS; Elsevier; Food Research International; 54; 1; 11-2013; 1313-1320
0963-9969
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0963996913000975
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodres.2013.02.004
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
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
_version_ 1842269899692441600
score 13.13397

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