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

Authors :
Mehak Arora
Stephen C. Zambrzycki
Joshua M. Levy
Annette Esper
Jennifer K. Frediani
Cassandra L. Quave
Facundo M. Fernández
Rishikesan Kamaleswaran
Source :
Metabolites, Vol 12, Iss 3, p 232 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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.

Details

Language :
English
ISSN :
22181989
Volume :
12
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Metabolites
Publication Type :
Academic Journal
Accession number :
edsdoj.7239b35896ca44f3b4610dfc424c5e27
Document Type :
article
Full Text :
https://doi.org/10.3390/metabo12030232