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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Vydáno v:	Metabolites Ročník 12; číslo 3; s. 232
Hlavní autoři:	Arora, Mehak, Zambrzycki, Stephen C., Levy, Joshua M., Esper, Annette, Frediani, Jennifer K., Quave, Cassandra L., Fernández, Facundo M., Kamaleswaran, Rishikesan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 08.03.2022 MDPI
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors contributed equally to this work.
ISSN:	2218-1989 2218-1989
DOI:	10.3390/metabo12030232