Rapid detection of hydrogen sulfide produced by pathogenic bacteria in focused growth media using SHS-MCC-GC-IMS

Thompson, Ryan; Perry, John D.; Stanforth, Stephen P.; Dean, John R.

doi:10.1016/j.microc.2018.04.026

Cited by 31 publications

(20 citation statements)

References 32 publications

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“…W2W and WIW are sensitive to aromatics compounds, sulfur organic compounds and sulfur compounds, respectively. Hydrogen sulfide is a compound characteristically produced by pathogenic bacteria in focused growth media and predominantly relating to bacteria of the family Enterobacteriaceae (Thompson, Perry, Stanforth, & Dean, ). Conversion of tryptophan into indole is also associated with E. coli and is seen in the rise of W2W during E. coli sampling as well.…”

Section: Resultsmentioning

confidence: 99%

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Bonah

Huang

Ren

et al. 2019

J Food Process Engineering

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Rapid detection of bacterial foodborne pathogens is crucial in reducing the incidence of diseases associated with food contaminated with pathogens and toxins. This article presents a classification model of support vector machine (SVM) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms for bacterial foodborne pathogen classification and differentiation. LDA and SVM showed classification accuracies of training (90.3% and 91%) and prediction (89.5% and 90.6%), respectively using the Matlab classification learner app. Optimization of the modeling parameters c and g for SVM were performed to increase efficiency and classification accuracy. Simulated results show classification accuracies of 100% (training set) and 98.95% (prediction set) for five selected bacterial pathogens acquired using electronic nose dataset and PSO‐SVM model. Electronic nose recognition system made up of 12 metal oxide semiconductor sensors produced different distinctive response signals for each bacterial and could differentiate Escherichia coli, Escherichia coli O157: H7, Listeria monocytogenes, Salmonella enteritidis, and Salmonella Typhimurium. PSO‐SVM algorithm can be efficiently used in bacterial discrimination at the species and strain level by electronic nose and has the good ability both in learning and in generalization. Practical Applications Existing microbial methods depend on traditional culture‐based methods which are time‐wise lengthy, require trained and qualified personnel, and are not suitable as point‐of‐use (POU) sensing devices. The application of electronic nose for the detection and classification of bacterial foodborne pathogens have shown great promise and proven to be effective with increasing sensitivity and selectivity as compared to traditional methods. The ability for E‐nose to discriminate between individual bacteria colonies at both species and strain level is of great public health importance since most bacteria have many strains. Virulence and pathogenicity are often associated with only a subset of these strains and it is essential for a method to be able to differentiate between pathogenic and nonpathogenic strains during a foodborne outbreak.

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Section: Resultsmentioning

confidence: 99%

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Bonah

Huang

Ren

et al. 2019

J Food Process Engineering

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“…On the other hand, ion mobility spectrometry (IMS) has been successfully applied for a large variety of purposes, such as food fraud detection [ 36 , 37 , 38 , 39 ], fire debris analysis [ 40 , 41 ], and also to uncover drugs and explosives [ 42 , 43 , 44 ]. As regards environmental issues, IMS has mainly been used to detect bacterial contamination [ 45 , 46 , 47 ], to characterize biodegraded PDPs [ 48 ] and, in combination with extraction techniques, to identify polycyclic aromatic hydrocarbons in water [ 49 , 50 ]. IMS exhibits a very low limit of detection (within the µL·L −1 range), and it does not require complex sample preparation methods [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater

Jaén-González

Aliaño-González

Ferreiro-González

et al. 2021

Sensors

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The objective of the present study is to develop an optimized method where headspace-ion mobility spectrometry is applied for the detection and discrimination between four petroleum-derived products (PDPs) in water. A Box–Behnken design with a response surface methodology was used, and five variables (incubation temperature, incubation time, agitation, sample volume, and injection volume) with influences on the ion mobility spectrometry (IMS) response were optimized. An IMS detector was used as a multiple sensor device, in which, each drift time acts as a specific sensor. In this way, the total intensity at each drift time is equivalent to multiple sensor signals. According to our results, 2.5 mL of sample incubated for 5 min at 31 °C, agitated at 750 rpm, and with an injection volume of 0.91 mL were the optimal conditions for successful detection and discrimination of the PDPs. The developed method has exhibited good intermediate precision and repeatability with a coefficient of variation lower than 5%, (RSD (Relative Standard Deviation): 2.35% and 3.09%, respectively). Subsequently, the method was applied in the context of the detection and discrimination of petroleum-derived products added to water samples at low concentration levels (2 µL·L−1). Finally, the new method was applied to determine the presence of petroleum-derived products in seawater samples.

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“…We have previously identied that using targeted enzyme substrates, a unique exogenous volatile organic compound can be liberated and detected using either static headspace-solid phase micro-extraction-gas chromatography-mass spectrometry, SHS-SPME-GC-MS [23][24][25][26] or static headspace multicapillary column-gas chromatographyion mobility spectrometry, SHS-MCC-GC-IMS. 27 The use of exogenous volatile organic compounds (VOCs) to distinguish 7 Gram-positive from 15 Gram-negative bacteria using the enzyme substrates 2-amino-N-phenylpropanamide (to liberate the VOC aniline) and 2-amino-N-(4-methylphenyl)propanamide (to liberate the VOC p-toluidine) has been demonstrated. 24 Similarly the approach has been applied for the analysis of enzyme activity in Listeria species using the enzyme substrates benzyl-a-D-mannopyranoside (to liberate the VOC benzyl alcohol) and D-alanyl 3-uoroanilide (to liberate the VOC 3-uoroaniline).…”

Section: Introductionmentioning

confidence: 99%

Detection of β-alanyl aminopeptidase as a biomarker for Pseudomonas aeruginosa in the sputum of patients with cystic fibrosis using exogenous volatile organic compound evolution

et al. 2020

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Rapid detection of hydrogen sulfide produced by pathogenic bacteria in focused growth media using SHS-MCC-GC-IMS

Cited by 31 publications

References 32 publications

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater

Detection of β-alanyl aminopeptidase as a biomarker for Pseudomonas aeruginosa in the sputum of patients with cystic fibrosis using exogenous volatile organic compound evolution

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