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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Bibliographic Details
Published in:Metabolites Vol. 12; no. 3; p. 232
Main Authors: Arora, Mehak, Zambrzycki, Stephen C., Levy, Joshua M., Esper, Annette, Frediani, Jennifer K., Quave, Cassandra L., Fernández, Facundo M., Kamaleswaran, Rishikesan
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 08.03.2022
MDPI
Subjects:
Algorithms
ambient plasma ionization
Antibiotic resistance
Artificial intelligence
Bacteria
Biomarkers
Chromatography
Classification
Clustering
DART-MS
Datasets
Decision trees
Fungi
Gases
Gene expression
imbalanced learning
K-means clustering
Learning algorithms
Machine learning
machine learning classification algorithms
Mass spectrometry
Mass spectroscopy
Organic compounds
Pathogens
Physiology
Scientific imaging
Skin
Solid phase methods
solid phase micro-extraction
Support vector machines
VOCs
Volatile organic compounds
imbalanced learning
pathogen identification
ambient plasma ionization
DART-MS
machine learning classification algorithms
solid phase micro-extraction
VOC
point-of-care devices
K-means clustering
ISSN:2218-1989, 2218-1989
Online Access:Get full text
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Summary: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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These authors contributed equally to this work.
ISSN:2218-1989
2218-1989
DOI:10.3390/metabo12030232