Rapid identification of Salmonella serovars Enteritidis and Typhimurium using whole cell matrix assisted laser desorption ionization – Time of flight mass spectrometry (MALDI-TOF MS) coupled with multivariate analysis and artificial intelligence

Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method was investigated to identify these serovars using whole-cell MALDI-TOF MS coupled with multivariate analysis and artificial intelligence and...

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Published in	Journal of microbiological methods Vol. 213; p. 106827
Main Authors	Gao, Anli, Fischer-Jenssen, Jennifer, Slavic, Durda, Rutherford, Kimani, Lippert, Sarah, Wilson, Emily, Chen, Shu, Leon-Velarde, Carlos G., Martos, Perry
Format	Journal Article
Language	English
Published	Elsevier B.V 01.10.2023
Subjects	Artificial intelligence Classification data collection desorption Enteritidis food pathogens ionization MALDI-TOF MS mass spectrometry Multivariate analysis principal component analysis rapid methods Salmonella serotypes Serovar identification Typhimurium Salmonella MALDI-TOF MS Typhimurium Serovar identification Classification Enteritidis Multivariate analysis Artificial intelligence
Online Access	Get full text
ISSN	0167-7012 1872-8359 1872-8359
DOI	10.1016/j.mimet.2023.106827

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Abstract	Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method was investigated to identify these serovars using whole-cell MALDI-TOF MS coupled with multivariate analysis and artificial intelligence and 113 Salmonella strains, including 38 Enteritidis (SE), 38 Typhimurium (ST) and 37 strains from 32 other Salmonella serovars (SG). Datasets of ions (presence/absence) with high discriminative power were created using newly developed criteria and subject to multivariate analyses and eight artificial intelligence (AI) tools. Principal Component Analysis based on 55 or 88 selected ions separated SE, ST and SG without overlap on the first three principal components. Datasets were partitioned using five partitioning methods with 70% of samples for AI model training and 30% for validation. Of the eight AI models evaluated, high performance (HP) SVM and HP Neural were the top performers, identified three serovar groups 97% correctly on average (range 82%–100%) according to the validation results. Selection of serovar specific ions facilitated differentiation of serotypes using unsupervised model PCA and improved the accuracy of classification using AI significantly (p < 0.01). MALDI-TOF MS incorporated with advanced data processing and classification tools is a promising method to allow rapid identification of Salmonella serovars of concern in routine diagnostic laboratories. •Rapid identification of Salmonella Enteritidis and Typhimurium using MALDI-TOF MS•Selection of specific ions for subtyping using unsupervised models•Optimized protocols from MALDI-TOF raw data to identification of ion peaks•The best performers of artificial intelligence models for classification•Perspective of onsite identification of zoonotic pathogens in routine test
AbstractList	Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method was investigated to identify these serovars using whole-cell MALDI-TOF MS coupled with multivariate analysis and artificial intelligence and 113 Salmonella strains, including 38 Enteritidis (SE), 38 Typhimurium (ST) and 37 strains from 32 other Salmonella serovars (SG). Datasets of ions (presence/absence) with high discriminative power were created using newly developed criteria and subject to multivariate analyses and eight artificial intelligence (AI) tools. Principal Component Analysis based on 55 or 88 selected ions separated SE, ST and SG without overlap on the first three principal components. Datasets were partitioned using five partitioning methods with 70% of samples for AI model training and 30% for validation. Of the eight AI models evaluated, high performance (HP) SVM and HP Neural were the top performers, identified three serovar groups 97% correctly on average (range 82%–100%) according to the validation results. Selection of serovar specific ions facilitated differentiation of serotypes using unsupervised model PCA and improved the accuracy of classification using AI significantly (p < 0.01). MALDI-TOF MS incorporated with advanced data processing and classification tools is a promising method to allow rapid identification of Salmonella serovars of concern in routine diagnostic laboratories. •Rapid identification of Salmonella Enteritidis and Typhimurium using MALDI-TOF MS•Selection of specific ions for subtyping using unsupervised models•Optimized protocols from MALDI-TOF raw data to identification of ion peaks•The best performers of artificial intelligence models for classification•Perspective of onsite identification of zoonotic pathogens in routine test Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method was investigated to identify these serovars using whole-cell MALDI-TOF MS coupled with multivariate analysis and artificial intelligence and 113 Salmonella strains, including 38 Enteritidis (SE), 38 Typhimurium (ST) and 37 strains from 32 other Salmonella serovars (SG). Datasets of ions (presence/absence) with high discriminative power were created using newly developed criteria and subject to multivariate analyses and eight artificial intelligence (AI) tools. Principal Component Analysis based on 55 or 88 selected ions separated SE, ST and SG without overlap on the first three principal components. Datasets were partitioned using five partitioning methods with 70% of samples for AI model training and 30% for validation. Of the eight AI models evaluated, high performance (HP) SVM and HP Neural were the top performers, identified three serovar groups 97% correctly on average (range 82%-100%) according to the validation results. Selection of serovar specific ions facilitated differentiation of serotypes using unsupervised model PCA and improved the accuracy of classification using AI significantly (p < 0.01). MALDI-TOF MS incorporated with advanced data processing and classification tools is a promising method to allow rapid identification of Salmonella serovars of concern in routine diagnostic laboratories.Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method was investigated to identify these serovars using whole-cell MALDI-TOF MS coupled with multivariate analysis and artificial intelligence and 113 Salmonella strains, including 38 Enteritidis (SE), 38 Typhimurium (ST) and 37 strains from 32 other Salmonella serovars (SG). Datasets of ions (presence/absence) with high discriminative power were created using newly developed criteria and subject to multivariate analyses and eight artificial intelligence (AI) tools. Principal Component Analysis based on 55 or 88 selected ions separated SE, ST and SG without overlap on the first three principal components. Datasets were partitioned using five partitioning methods with 70% of samples for AI model training and 30% for validation. Of the eight AI models evaluated, high performance (HP) SVM and HP Neural were the top performers, identified three serovar groups 97% correctly on average (range 82%-100%) according to the validation results. Selection of serovar specific ions facilitated differentiation of serotypes using unsupervised model PCA and improved the accuracy of classification using AI significantly (p < 0.01). MALDI-TOF MS incorporated with advanced data processing and classification tools is a promising method to allow rapid identification of Salmonella serovars of concern in routine diagnostic laboratories. Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method was investigated to identify these serovars using whole-cell MALDI-TOF MS coupled with multivariate analysis and artificial intelligence and 113 Salmonella strains, including 38 Enteritidis (SE), 38 Typhimurium (ST) and 37 strains from 32 other Salmonella serovars (SG). Datasets of ions (presence/absence) with high discriminative power were created using newly developed criteria and subject to multivariate analyses and eight artificial intelligence (AI) tools. Principal Component Analysis based on 55 or 88 selected ions separated SE, ST and SG without overlap on the first three principal components. Datasets were partitioned using five partitioning methods with 70% of samples for AI model training and 30% for validation. Of the eight AI models evaluated, high performance (HP) SVM and HP Neural were the top performers, identified three serovar groups 97% correctly on average (range 82%–100%) according to the validation results. Selection of serovar specific ions facilitated differentiation of serotypes using unsupervised model PCA and improved the accuracy of classification using AI significantly (p < 0.01). MALDI-TOF MS incorporated with advanced data processing and classification tools is a promising method to allow rapid identification of Salmonella serovars of concern in routine diagnostic laboratories.
ArticleNumber	106827
Author	Gao, Anli Fischer-Jenssen, Jennifer Rutherford, Kimani Slavic, Durda Wilson, Emily Chen, Shu Lippert, Sarah Martos, Perry Leon-Velarde, Carlos G.
Author_xml	– sequence: 1 givenname: Anli surname: Gao fullname: Gao, Anli email: agao@uoguelph.ca organization: Agriculture and Food Laboratory, Laboratory Services Division, University of Guelph, Guelph, ON, Canada – sequence: 2 givenname: Jennifer surname: Fischer-Jenssen fullname: Fischer-Jenssen, Jennifer organization: Agriculture and Food Laboratory, Laboratory Services Division, University of Guelph, Guelph, ON, Canada – sequence: 3 givenname: Durda surname: Slavic fullname: Slavic, Durda organization: Animal Health Laboratory, Laboratory Services Division, University of Guelph, Guelph, ON, Canada – sequence: 4 givenname: Kimani surname: Rutherford fullname: Rutherford, Kimani organization: Animal Health Laboratory, Laboratory Services Division, University of Guelph, Guelph, ON, Canada – sequence: 5 givenname: Sarah surname: Lippert fullname: Lippert, Sarah organization: Animal Health Laboratory, Laboratory Services Division, University of Guelph, Guelph, ON, Canada – sequence: 6 givenname: Emily surname: Wilson fullname: Wilson, Emily organization: Agriculture and Food Laboratory, Laboratory Services Division, University of Guelph, Guelph, ON, Canada – sequence: 7 givenname: Shu surname: Chen fullname: Chen, Shu organization: Agriculture and Food Laboratory, Laboratory Services Division, University of Guelph, Guelph, ON, Canada – sequence: 8 givenname: Carlos G. surname: Leon-Velarde fullname: Leon-Velarde, Carlos G. organization: Agriculture and Food Laboratory, Laboratory Services Division, University of Guelph, Guelph, ON, Canada – sequence: 9 givenname: Perry surname: Martos fullname: Martos, Perry organization: Agriculture and Food Laboratory, Laboratory Services Division, University of Guelph, Guelph, ON, Canada
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Keywords	Salmonella MALDI-TOF MS Typhimurium Serovar identification Classification Enteritidis Multivariate analysis Artificial intelligence
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Snippet	Salmonella is a common food-borne pathogen with Enteritidis and Typhimurium being among the most important serovars causing numerous outbreaks. A rapid method...
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SubjectTerms	Artificial intelligence Classification data collection desorption Enteritidis food pathogens ionization MALDI-TOF MS mass spectrometry Multivariate analysis principal component analysis rapid methods Salmonella serotypes Serovar identification Typhimurium
Title	Rapid identification of Salmonella serovars Enteritidis and Typhimurium using whole cell matrix assisted laser desorption ionization – Time of flight mass spectrometry (MALDI-TOF MS) coupled with multivariate analysis and artificial intelligence
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