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Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increas...

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Autores principales: Arora, Mehak, Zambrzycki, Stephen C., Levy, Joshua M., Esper, Annette, Frediani, Jennifer K., Quave, Cassandra L., Fernández, Facundo M., Kamaleswaran, Rishikesan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953436/
https://www.ncbi.nlm.nih.gov/pubmed/35323675
http://dx.doi.org/10.3390/metabo12030232
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author Arora, Mehak
Zambrzycki, Stephen C.
Levy, Joshua M.
Esper, Annette
Frediani, Jennifer K.
Quave, Cassandra L.
Fernández, Facundo M.
Kamaleswaran, Rishikesan
author_facet Arora, Mehak
Zambrzycki, Stephen C.
Levy, Joshua M.
Esper, Annette
Frediani, Jennifer K.
Quave, Cassandra L.
Fernández, Facundo M.
Kamaleswaran, Rishikesan
author_sort Arora, Mehak
collection PubMed
description Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications.
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spelling pubmed-89534362022-03-26 Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS Arora, Mehak Zambrzycki, Stephen C. Levy, Joshua M. Esper, Annette Frediani, Jennifer K. Quave, Cassandra L. Fernández, Facundo M. Kamaleswaran, Rishikesan Metabolites Article Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications. MDPI 2022-03-08 /pmc/articles/PMC8953436/ /pubmed/35323675 http://dx.doi.org/10.3390/metabo12030232 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
Arora, Mehak
Zambrzycki, Stephen C.
Levy, Joshua M.
Esper, Annette
Frediani, Jennifer K.
Quave, Cassandra L.
Fernández, Facundo M.
Kamaleswaran, Rishikesan
Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS
title Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS
title_full Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS
title_fullStr Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS
title_full_unstemmed Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS
title_short Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS
title_sort machine learning approaches to identify discriminative signatures of volatile organic compounds (vocs) from bacteria and fungi using spme-dart-ms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953436/
https://www.ncbi.nlm.nih.gov/pubmed/35323675
http://dx.doi.org/10.3390/metabo12030232
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