Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples
Over the past decade, the application of liquid chromatography-high resolution mass spectroscopy (LC-HRMS) has been growing extensively due to its ability to analyze a wide range of suspected and unknown compounds in environmental samples. However, various criteria, such as mass accuracy and isotopi...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/345513 |
| Acceso en línea: | http://hdl.handle.net/10261/345513 https://api.elsevier.com/content/abstract/scopus_id/84979599437 |
| Access Level: | acceso abierto |
| Palabra clave: | Mass Spectrometric Screening Emerging contaminants http://metadata.un.org/sdg/3 http://metadata.un.org/sdg/6 Ensure healthy lives and promote well-being for all at all ages Ensure availability and sustainable management of water and sanitation for all |
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Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental SamplesAalizadeh, RezaThomaidis, Nikolaos S.Bletsou, Anna A.Gago-Ferrero, PabloMass Spectrometric ScreeningEmerging contaminantshttp://metadata.un.org/sdg/3http://metadata.un.org/sdg/6Ensure healthy lives and promote well-being for all at all agesEnsure availability and sustainable management of water and sanitation for allOver the past decade, the application of liquid chromatography-high resolution mass spectroscopy (LC-HRMS) has been growing extensively due to its ability to analyze a wide range of suspected and unknown compounds in environmental samples. However, various criteria, such as mass accuracy and isotopic pattern of the precursor ion, MS/MS spectra evaluation, and retention time plausibility, should be met to reach a certain identification confidence. In this context, a comprehensive workflow based on computational tools was developed to understand the retention time behavior of a large number of compounds belonging to emerging contaminants. Two extensive data sets were built for two chromatographic systems, one for positive and one for negative electrospray ionization mode, containing information for the retention time of 528 and 298 compounds, respectively, to expand the applicability domain of the developed models. Then, the data sets were split into training and test set, employing k-nearest neighborhood clustering, to build and validate the models' internal and external prediction ability. The best subset of molecular descriptors was selected using genetic algorithms. Multiple linear regression, artificial neural networks, and support vector machines were used to correlate the selected descriptors with the experimental retention times. Several validation techniques were used, including Golbraikh-Tropsha acceptable model criteria, Euclidean based applicability domain, modified correlation coefficient (rm2), and concordance correlation coefficient values, to measure the accuracy and precision of the models. The best linear and nonlinear models for each data set were derived and used to predict the retention time of suspect compounds of a wide-scope survey, as the evaluation data set. For the efficient outlier detection and interpretation of the origin of the prediction error, a novel procedure and tool was developed and applied, enabling us to identify if the suspect compound was in the applicability domain or not.This research has been cofinanced by the European Union (European Social Fund) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)–ARISTEIA 624 (TREMEPOL project).Peer reviewedAmerican Chemical SocietyConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242016info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/345513https://api.elsevier.com/content/abstract/scopus_id/84979599437reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésJournal of chemical information and modelinghttps://doi.org/10.1021/acs.jcim.5b00752Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3455132026-05-22T06:33:51Z |
| dc.title.none.fl_str_mv |
Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples |
| title |
Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples |
| spellingShingle |
Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples Aalizadeh, Reza Mass Spectrometric Screening Emerging contaminants http://metadata.un.org/sdg/3 http://metadata.un.org/sdg/6 Ensure healthy lives and promote well-being for all at all ages Ensure availability and sustainable management of water and sanitation for all |
| title_short |
Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples |
| title_full |
Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples |
| title_fullStr |
Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples |
| title_full_unstemmed |
Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples |
| title_sort |
Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples |
| dc.creator.none.fl_str_mv |
Aalizadeh, Reza Thomaidis, Nikolaos S. Bletsou, Anna A. Gago-Ferrero, Pablo |
| author |
Aalizadeh, Reza |
| author_facet |
Aalizadeh, Reza Thomaidis, Nikolaos S. Bletsou, Anna A. Gago-Ferrero, Pablo |
| author_role |
author |
| author2 |
Thomaidis, Nikolaos S. Bletsou, Anna A. Gago-Ferrero, Pablo |
| author2_role |
author author author |
| dc.contributor.none.fl_str_mv |
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] |
| dc.subject.none.fl_str_mv |
Mass Spectrometric Screening Emerging contaminants http://metadata.un.org/sdg/3 http://metadata.un.org/sdg/6 Ensure healthy lives and promote well-being for all at all ages Ensure availability and sustainable management of water and sanitation for all |
| topic |
Mass Spectrometric Screening Emerging contaminants http://metadata.un.org/sdg/3 http://metadata.un.org/sdg/6 Ensure healthy lives and promote well-being for all at all ages Ensure availability and sustainable management of water and sanitation for all |
| description |
Over the past decade, the application of liquid chromatography-high resolution mass spectroscopy (LC-HRMS) has been growing extensively due to its ability to analyze a wide range of suspected and unknown compounds in environmental samples. However, various criteria, such as mass accuracy and isotopic pattern of the precursor ion, MS/MS spectra evaluation, and retention time plausibility, should be met to reach a certain identification confidence. In this context, a comprehensive workflow based on computational tools was developed to understand the retention time behavior of a large number of compounds belonging to emerging contaminants. Two extensive data sets were built for two chromatographic systems, one for positive and one for negative electrospray ionization mode, containing information for the retention time of 528 and 298 compounds, respectively, to expand the applicability domain of the developed models. Then, the data sets were split into training and test set, employing k-nearest neighborhood clustering, to build and validate the models' internal and external prediction ability. The best subset of molecular descriptors was selected using genetic algorithms. Multiple linear regression, artificial neural networks, and support vector machines were used to correlate the selected descriptors with the experimental retention times. Several validation techniques were used, including Golbraikh-Tropsha acceptable model criteria, Euclidean based applicability domain, modified correlation coefficient (rm2), and concordance correlation coefficient values, to measure the accuracy and precision of the models. The best linear and nonlinear models for each data set were derived and used to predict the retention time of suspect compounds of a wide-scope survey, as the evaluation data set. For the efficient outlier detection and interpretation of the origin of the prediction error, a novel procedure and tool was developed and applied, enabling us to identify if the suspect compound was in the applicability domain or not. |
| publishDate |
2016 |
| dc.date.none.fl_str_mv |
2016 2024 2024 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 Postprint info:eu-repo/semantics/acceptedVersion |
| format |
article |
| status_str |
acceptedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10261/345513 https://api.elsevier.com/content/abstract/scopus_id/84979599437 |
| url |
http://hdl.handle.net/10261/345513 https://api.elsevier.com/content/abstract/scopus_id/84979599437 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Journal of chemical information and modeling https://doi.org/10.1021/acs.jcim.5b00752 Sí |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
American Chemical Society |
| publisher.none.fl_str_mv |
American Chemical Society |
| dc.source.none.fl_str_mv |
reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC) |
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Consejo Superior de Investigaciones Científicas (CSIC) |
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DIGITAL.CSIC. Repositorio Institucional del CSIC |
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DIGITAL.CSIC. Repositorio Institucional del CSIC |
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1869411565329973248 |
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15,81155 |