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A Multi-Label Classifier for Predicting the Most Appropriate Instrumental Method for the Analysis of Contaminants of Emerging Concern

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) and gas chromatography-high resolution mass spectrometry (GC-HRMS) have revolutionized analytical chemistry among many other disciplines. These advanced instrumentations allow to theoretically capture the whole chemical universe that...

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Autores principales: Alygizakis, Nikiforos, Konstantakos, Vasileios, Bouziotopoulos, Grigoris, Kormentzas, Evangelos, Slobodnik, Jaroslav, Thomaidis, Nikolaos S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949148/
https://www.ncbi.nlm.nih.gov/pubmed/35323641
http://dx.doi.org/10.3390/metabo12030199
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author Alygizakis, Nikiforos
Konstantakos, Vasileios
Bouziotopoulos, Grigoris
Kormentzas, Evangelos
Slobodnik, Jaroslav
Thomaidis, Nikolaos S.
author_facet Alygizakis, Nikiforos
Konstantakos, Vasileios
Bouziotopoulos, Grigoris
Kormentzas, Evangelos
Slobodnik, Jaroslav
Thomaidis, Nikolaos S.
author_sort Alygizakis, Nikiforos
collection PubMed
description Liquid chromatography-high resolution mass spectrometry (LC-HRMS) and gas chromatography-high resolution mass spectrometry (GC-HRMS) have revolutionized analytical chemistry among many other disciplines. These advanced instrumentations allow to theoretically capture the whole chemical universe that is contained in samples, giving unimaginable opportunities to the scientific community. Laboratories equipped with these instruments produce a lot of data daily that can be digitally archived. Digital storage of data opens up the opportunity for retrospective suspect screening investigations for the occurrence of chemicals in the stored chromatograms. The first step of this approach involves the prediction of which data is more appropriate to be searched. In this study, we built an optimized multi-label classifier for predicting the most appropriate instrumental method (LC-HRMS or GC-HRMS or both) for the analysis of chemicals in digital specimens. The approach involved the generation of a baseline model based on the knowledge that an expert would use and the generation of an optimized machine learning model. A multi-step feature selection approach, a model selection strategy, and optimization of the classifier’s hyperparameters led to a model with accuracy that outperformed the baseline implementation. The models were used to predict the most appropriate instrumental technique for new substances. The scripts are available at GitHub and the dataset at Zenodo.
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spelling pubmed-89491482022-03-26 A Multi-Label Classifier for Predicting the Most Appropriate Instrumental Method for the Analysis of Contaminants of Emerging Concern Alygizakis, Nikiforos Konstantakos, Vasileios Bouziotopoulos, Grigoris Kormentzas, Evangelos Slobodnik, Jaroslav Thomaidis, Nikolaos S. Metabolites Article Liquid chromatography-high resolution mass spectrometry (LC-HRMS) and gas chromatography-high resolution mass spectrometry (GC-HRMS) have revolutionized analytical chemistry among many other disciplines. These advanced instrumentations allow to theoretically capture the whole chemical universe that is contained in samples, giving unimaginable opportunities to the scientific community. Laboratories equipped with these instruments produce a lot of data daily that can be digitally archived. Digital storage of data opens up the opportunity for retrospective suspect screening investigations for the occurrence of chemicals in the stored chromatograms. The first step of this approach involves the prediction of which data is more appropriate to be searched. In this study, we built an optimized multi-label classifier for predicting the most appropriate instrumental method (LC-HRMS or GC-HRMS or both) for the analysis of chemicals in digital specimens. The approach involved the generation of a baseline model based on the knowledge that an expert would use and the generation of an optimized machine learning model. A multi-step feature selection approach, a model selection strategy, and optimization of the classifier’s hyperparameters led to a model with accuracy that outperformed the baseline implementation. The models were used to predict the most appropriate instrumental technique for new substances. The scripts are available at GitHub and the dataset at Zenodo. MDPI 2022-02-23 /pmc/articles/PMC8949148/ /pubmed/35323641 http://dx.doi.org/10.3390/metabo12030199 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alygizakis, Nikiforos
Konstantakos, Vasileios
Bouziotopoulos, Grigoris
Kormentzas, Evangelos
Slobodnik, Jaroslav
Thomaidis, Nikolaos S.
A Multi-Label Classifier for Predicting the Most Appropriate Instrumental Method for the Analysis of Contaminants of Emerging Concern
title A Multi-Label Classifier for Predicting the Most Appropriate Instrumental Method for the Analysis of Contaminants of Emerging Concern
title_full A Multi-Label Classifier for Predicting the Most Appropriate Instrumental Method for the Analysis of Contaminants of Emerging Concern
title_fullStr A Multi-Label Classifier for Predicting the Most Appropriate Instrumental Method for the Analysis of Contaminants of Emerging Concern
title_full_unstemmed A Multi-Label Classifier for Predicting the Most Appropriate Instrumental Method for the Analysis of Contaminants of Emerging Concern
title_short A Multi-Label Classifier for Predicting the Most Appropriate Instrumental Method for the Analysis of Contaminants of Emerging Concern
title_sort multi-label classifier for predicting the most appropriate instrumental method for the analysis of contaminants of emerging concern
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949148/
https://www.ncbi.nlm.nih.gov/pubmed/35323641
http://dx.doi.org/10.3390/metabo12030199
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